# Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

Article (PDF Available)inJournal of Medicinal Chemistry 55(20):8926-42 · October 2012with34 Reads
DOI: 10.1021/jm301210j · Source: PubMed
Abstract
Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for "synthesis." Beginning with a small number of molecules, based only on structures and activities, a model was constructed. Compound selection was done computationally, each time making five selections based on confident predictions of high activity and five selections based on a quantitative measure of three-dimensional structural novelty. Compound selection was followed by model refinement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active novelty selection.

# Figures

Iterative Renement of a Binding Pocket Model: Active
Rocco Varela,
W. Patrick Walters,
Brian B. Goldman,
and Ajay N. Jain*
,
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143-0912, United States
Vertex Pharmaceuticals Inc., Cambridge, Massachusetts 02139, United States
ABSTRACT: Computational approaches for binding anity
prediction are most frequently demonstrated through cross-
validation within a series of molecules or through performance
shown on a blinded test set. Here, we show how such a system
performs in an iterative, temporal lead optimization exercise. A
series of gyrase inhibitors with known synthetic order formed
the set of molecules that could be selected for synthesis.
Beginning with a small number of molecules, based only on
structures and activities, a model was constructed. Compound
selection was done computationally, each time making ve
selections based on condent predictions of high activity and ve
selections based on a quantitative measure of three-dimensional
structural novelty. Compound selection was followed by model
renement using the new data. Iterative computational candidate selection produced rapid improvements in selected compound
activity, and incorporation of explicitly novel compounds uncovered much more diverse active inhibitors than strategies lacking active
novelty selection.
INTRODUCTION
The eld of computational structureactivity modeling in
medicinal chem istry has a long h istory, going back at least
40 years.
1
Methods-oriented papers have generally analyzed statis-
tical performance in terms of numerical prediction accuracy, and
application-oriented papers have described predictions made based
upon QSAR models built from a particular training set. The
present study considers these aspects of predictive activity
modeling but adds new dimensions. Rather than focus purely
on how well a method can predict activity based on a xed,
guide a trajectory of chemical exploration in a protocol that
incorporates iterative model renement. Further, in addition to
considering prediction accuracy and the eciency of discovering
active com pounds, we consider how selection strategies and
modeling methods aect the structural diversity of the chemical
space that is uncovered over time. We show that there is a direct
benet for active selection of molecules that will break amodel
by venturing into chemical and physical space that is poorly
understood. W e also show that modeling methods that are
accurate within a narrow range of structural variation can appear to
be highly predictive but guide m olecular selection toward a
structurally narrow end point. Conservative selection strategies
and conservative modeling methods can lead to active compounds,
but these may represent just a fraction of the space of active
compounds that exist.
The primary method used to explore these issues is a
relatively new one for binding anity prediction, called Surex
QMOD (Quantitative MODeling), which constructs a physical
binding pocket into which ligands are exibly t and scored to
predict both a bioactive pose and binding anity.
24
Our initial
work focused on demonstrating the feasibility of the approach,
with a particular emphasis on addressing cross-chemotype
predictions, as well as the relationship between the under-
pinnings o f the method to the physical pro cess of protein
ligand binding. Those studies considered receptors (5HT1a
and muscarinic), e nzymes (CDK2), and membrane-bound ion
channels (hERG). The present work addresses two new areas.
First, we examined the performance of QMOD in an iterative
renement scenario, where a large set of molecules from a lead-
optimization exercise
5
was used as a pool from which selections
were made using model predictions. Multiple rounds of
model building, molecule selection, and model renement
produced a trajectory of molecular choices. Second , we
considered the eect of active selection of structurally novel
molecules that probed parts of three-dimensional space that
were unexplored by the training ligands for each rounds model.
Figure 1 shows a diagram of the iterative model renement
procedure. Selection of molecules for synthesis for the rst
round took place from a batch of molecules made after the
initial training pool had been synthesized. Subsequent rounds
allowed for choice from later temporal batches, along with
previously considered but unselected molecules. The approach
was designed to limit the amount of look-ahead for the
procedure. The space for molecular selections within each
Published: October 9, 2012
Article
pubs.acs.org/jmc
© 2012 American Chemical Society 8926 dx.doi.org/10.1021/jm301210j | J. Med. Chem. 2012, 55, 89268942
round formed a structural window that reected the changing
chemical diversity that was explored over the course of the project.
The iterative procedure was carried out until all molecules were
tested. The primary procedural variations involved use of dierent
modeling and selection methods, and the analyses focused on the
characteristics of the selected molecular populations, and the
relationship of the models to the experimentally determined
structure of the protein binding pocket.
All of the molecules used in this study were taken from a lead
optimization program conducted at Vertex Pharmaceuticals. This
program involved the optimization of benzimidazole based
inhibitors of the bacterial gyrase heterotetramer.
5
Theenzymeis
a type II topoisomerase that alters chromosome structure through
modication of double stranded DNA. Antibacterials such as the
uoroquinolones target the non-ATP catalytic sites of gyrase. In
contrast, the benzimidazole inhibitors were discovered in a high-
throughput ATPase assay of the GyrB subunit. These were then
optimized for activity against the ATP-binding site of GyrB, with
an eye toward activity against the ATP site of the ParE subunit
(topoisomerase IV) as well. Both of these subunits are responsible
for supplying energy for catalysis. In the present study, only activity
data from GyrB assays were used for modeling and compound
selection. Figure 2 shows typical examples of structures and GyrB
activities from the initial training set. The position 2 substituents of
all inhibit ors used in this study were either alkyl-urea (e.g.,
compound 1) or alkyl-carbamate (e.g., 4). Structural exploration
was predominated by variation in the position 5 substituent of the
benzimidazole, with some substitutions also being made at other
positions on the central scaold (especially position 7). The series
used in this study consisted of 426 compounds.
For the present study, the most interesting aspect of the
QMOD approach is that it constructs a physical model of a
protein binding site based purely on structureactivity data,
and it produces predictions of both binding anity and bound
ligand pose. Because the optimal molecular poses depend
directly on the physical pocket model, multiple-instance
machine-learning is used for model induction.
2,3,612
Figure 3
gives a brief overview of the process, which begins with selection
of a small number of molecules to form a seed alignment
hypothesis (the boxed inhibitors from Figure 2) and ends with a
physical representation of a binding pocket, to which we refer as a
pocketm ol. Newmoleculesaredockedintothepocketmoland
scored, yielding predictions of activity and binding mode. By
considering the dierences between the predicted bound poses of
molecules with known activity (training molecules) and novel
candidates, it is possible to quantify the degree to which a new
molecule probes part of a modeled binding cavity differently
than has been probed before. This computational denition of
molecular novelty oers a visualizable means to actively consider
synthetic choices that specically probe beyond the established
and explored 3D space of a particular model. As a comparator, we
also made use of a descriptor-based QSAR approach that
constructs a purely statistical model of activity prediction based
on topological molecular features.
There were four primary results of the study. First, the iterative
QMOD procedure rapidly converged on models that reliably
identied highly active molecules. By the nal two model
renement rounds, 7080% of molecules selected based on
predicted activity fell into the highest category of experimental
activity (pK
i
> 7.9, which represented all molecules having activity
within 3-fold of the most active inhibitors). Second, explicit
computational selection of novel molecules lead to a much more
structurally diverse pool of active inhibitors than resulted from a
control procedure that made selections purely based on activity
Figure 1. Inhibitors rst synthesized were used for initial training. All subsequent molecules were divided into sequential batches of 50 candidates
each. At the completion of each build/rene iteration, the next sequential batch and all previously considered but unchosen molecules formed a
window for molecular selections. Based upon model predictions, ten molecules were selected and added to the training set for each round of model
renement. Two selection schemes were employed. The standard method selected molecules based on high-con dence predictions of high activity
or based on 3D structural novelty. The control procedure made selections purely based on activity predictions.
Journal of Medicinal Chemistry Article
dx.doi.org/10.1021/jm301210j | J. Med. Chem. 2012, 55, 892689428927
considerations. Both procedures produced similar performance in
terms of the distributions of experimental activity for selected
molecules. Third, the induced binding site model showed strong
concordance with the experimentally determined gyrase binding
site. This was true both in terms of predicted ligand poses as well
as similarities in contact patterns between ligand/pocketmol and
ligand/protein. Fourth, direct comparison with descriptor-based
QSAR methods showed that while such models yielded similar
distributions of activity among selected molecules, the structural
diversity of selected active molecules was much lower than for
QMOD. In particular, while QMOD identied examples of active
molecules across the entire arc of the projectschemical
exploration, the descriptor-based approach failed to select a
particularly attractive set of inhibitors made toward the end of the
project.
The basic Surex QMOD methodology has been validated in
prior studies.
24
The signicance here relates to systematic
application in the context of a virtual lead optimization exercise.
There is a dramatic benet in making use of an active-learning
paradigm in which exploration of unknown space is explicitly
made through the selection of structurally novel molecules. In
addition, apart from the obvious benets of providing a physical
model along with accurate predictions of binding modes, the
physically realistic modeling approach of QMOD showed a
surprising benet: great structural diversity among the set
of discovered active inhib itors. In particular, the pro cedure
identied ligands that showed strong activity against GyrB
but al so against ParE (topoiso merase IV). Activity of ligands
against ParE was an indirect consequence of spatial probing
through active selection of co mpounds. These ligands had
large 7-position substitu ents that r epresented a clearly new
structural direction when compared with the bulk of inhibi-
In the case of the congeneric chemical series studied here, it was
not surprising that descriptor-based QSAR methods performed
competitively in a purely numeric sense with respect to
identication of active GyrB inhibitors. However, the narrow
domain of applicability of such models manifested itself by
predicting high activity based only upon very close structural
similarity to pre-existing active inhibitors. The resulting trajectory
of selected molecules failed to identify the pool of active ParE
inhibitors that the QMOD approach found, even when a
procedure to increase novelty was employed in conjunction with
the descriptor-based method. Models that are fundamentally
correlative machines may appear to work well, but they may
sharply limit the space of compound exploration over the course
of time. Structural conservatism would appear to be a hidden cost
of reliance upon modeling methods that directly depend upon the
Figure 2. Examples of gyrase ligands in the initial training set, which contained the rst 39 made from a total of 426 gyrase inhibitors (both pK
i
and
synthetic sequence number are given). Training molecule activities ranged from a pK
i
of 8.2 to 4.7. The 3 most active compounds of the training set
(boxed) were used to generate the initial alignment hypotheses.
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existence of near-neighbors to make accurate predictions on new
molecules.
We believe that this approach of studying trajectories
through chemical space, subject to dierent prediction and
selection methods, oers a very dierent means by which to
assess the real-world behavior of modeling systems. The results
clearly encourage the use of physically sensible approaches that
move beyond purely correlative modeling and also support the
active incorporation of chemical possibilities that are clearly
beyond the knowledge of a model at a given time.
RESULTS AND DISCUSSION
Figure 4 shows the initial QMOD pocketmol derived from 39
training molecules (atom-color thin sticks with surface). The
pose of compound 2, which was part of the initial training set, is
shown along with the optimal pose of compound 9 (the 47th
molecule in the synthetic series). Molecule 9 was predicted
with high condence (0.92/1.0) to have high activity (predicted
pK
i
of 8.2), yielding an error of 0.3 log units when compared
with experimental activity. The condence measure is dened
as the maximal 3D molecular similarity between a test molecule
and any of the training molecules (each in its optimal pose
according to t within the pocketmol). Here, the most similar
training compound to 9 was 2, with the high similarity obvious
in the 2D representations, and with the optimal poses of both
molecules being concordant, even including volume overlap of
the diering left-hand side substituents.
As described above (and shown in Figure 1), this initial
model formed the root of two branches for molecular choice:
one making use of a novelty computation and the other
focusing only on activity. Figure 5 depicts an example of the
novelty computation relating to a substitution at position 1 of
the benzimidazole scaold. Molecular novelty is a quantitative
measure of the degree to which a new molecule explores the
space of the binding pocket with new chemical functionality. It
is dened using statistics based on the interactions of training
molecules with the pocketmol and the interactions with
unoccupied space near the pocketmol (termed the antipock-
etmol). The statistics characterize the scores for each probe
against the optimal poses for each training molecule and
additional poses that sample ligand congurations that are close
to optimal (see the Experimental Section for details). The
antipocketmol is constructed such that it borders on the
explored pose pool but excludes the space immediately around
the pocketmol. Novelty is quantied by comparing the inter-
actions made with the pocketmol/antipocketmol to those
novelty score among all 50 molecules in the rst batch of
compounds from which selections were made. Compound 10
was predicted incorrectly to have low activity, and it was
Figure 3. Derivation and testing of a QMOD pocketmol proceeds in six automated steps: (A) an alignment seed hypothesis is constructed from 2 to
3 ligands; (B) 100200 alignments for each training ligand are produced; (C) a large set of probes (many thousands) is created where interactions
may exist; (D) a small near-optimal set is selected based on t to experimental binding data and model parsimony; (E) probe positions and ligand
poses are rened iteratively; (F) new molecules are tested by exible alignment into the pocket to optimize score. The nal pocketmol is used in a
xed conguration, but conformational exibility within the corresponding protein pocket is represented by probes being places in multiple
positions.
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correctly agged as a low-condence prediction. Its novelty
score was 51.6, corresponding to a normalized Z-score of 5.7
standard deviation units greater than the mean of the remaining
pool from those molecules upon which the initial model was
tested. The extreme relative magnitude highlights the novelty of
the pattern of interaction scores associated with the substitution
at position 1 of the central scaold.
Eects of Selection Strategy o n Experimental
Activities of Chosen Molecules. The ideal experiment in
which to assess dierent design strategies for lead optimization
would require independent synthetic teams of equivalent
capabilities, each totally isolated from the other. Given an initial
starting point, the teams would make a xed number of com-
pounds over a set time period, with common protocols involving
compound testing and provision of assay feedback to the design
teams. While we do not have the resources to perform the ideal
experime nt, we have tried to pe rform a balanced comparison.
Here the 39 initial training molecules and their GyrB activities
form a common initial starting point, and it is interesting to
consider the eects of dierent computational approaches in terms
of the properties of the molecules that are selected from among
the remaining 387 that were part of the series. In the standard
procedure, half of the molecules selected were chosen to maximize
predicted activity and half were chosen as being structurally novel
in order to inform the model in areas that had not been explored .
In the control procedure, all of the molecules were chosen to
maximize activity. Figure 6 shows the distributions of experimental
activities of molecules chosen using the QMOD standard
procedure compared with the QMOD control procedure (recall
Figure 1). The two distributions within the standard procedure
were very dierent (p 0.01 by KolmogorovSmirnov (KS)),
with the novelty-driven selections exhibiting a wider dispersion of
experimental activity and a much larger proportion of poorly active
molecules (roughly 30% with pK
i
<6.5comparedwith<5%from
the activity-driven selections). Despite being informed quite
dierently in terms of structureactivity data, the distribution of
activities for molecules selected for activity under the standard
protocol were not die rent than those selected in the control
procedure (see Figure 6b). The structural characteristics of the
resulting pools were very dierent, and this will be discussed in the
next section.
The comparison between the two QMOD procedure variations
ts our Gedanken ideal, with fully independent synthetic teams
employing dierent design strategies in isolation. Beginning with
the same initial set of 39 training molecules, the two procedures
each made eight rounds of molecular selections, each consisting of
ten molecules, with the single dierence being the selection
strategy. If we consider the distribution of experimental activities of
the next 80 molecules actually made after the initial 39 in the
training set, we deviate from the ideal comparison. First, the
project chemists were interested in addressing issues beyond just
activity against GyrB. The considerations included activity against
ParE, physical properties of compounds, complexities of synthesis
given existing routes and materials, and a host of other items.
Clearly, however, they were interested in maximizing activity
information well beyond what the QMOD modeling procedures
had, including crystallographic guidance and knowledge of other
inhibitors of the ATP binding sites of gyrase. Bearing this in mind,
it is interesting to consider the comparison between the QMOD
selections in the standard procedure and the activities of the next
80 molecules actually synthesized after the initial 39. Figure 7
Figure 4. Initial QMOD binding site model is shown (right), derived from 39 training molecules. The probes comprising the pocket are shown in
atom-colored thin sticks with surfaces. Training compound 2 is shown in yellow, with 2D at left and in its predicted optimal pose at right.
Compound 9 (number 47 in the synthetic series) was predicted with high con dence to have a pK
i
of 8.2, very close to the experimental value of 7.9
(shown at right in atom colored sticks).
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shows the three distributions, each of which is highly statistically
dierent from one another. The QMOD activity selections (green
curve) were enriched for highly active compounds, the QMOD
novelty selections (blue curve) showed a wide range of activities,
and the next 80 project-synthesized compounds (red curve) had
high variance in activity but lacked a signicant fraction of highly
active selections. This comparison is not meant to suggest that the
QMOD selection approach is denitively better in some sense
than the eorts of human designers. The comparison provides
context for what the space of designable compounds looked like
within a xed frame of temporal exploration measured in numbers
Figure 8 provides additional detail, showing the experimental
activities in temporal selection order for the QMOD standard
protocol, the control protocol with no novelty bias, and the
next 80 molecules synthesized. Figure 8a shows the trajectory
of activity observed with the 40 QMOD standard activity-based
selections, nearly all of which had activity greater than 7.0 pK
i
.
Toward the end of the eight rounds of selection, nearly all
molecules had potencies of 8.0 or higher. The corresponding
novelty selections (Figure 8b) exhibit much wider dispersion,
with both high- and low-activity molecules being selected across
the entire sequence. Notably, maximally active molecules were
chosen earlier through novelty-based selection than through
activity-based selection in the standard procedure. Again, for
contextual purposes, and with the caveats described above,
Figure 8c shows the sequence of experimental activities for
molecules in the synthetic sequence numbered 40119. The
high dispersion and downward trend were probably driven by
many factors, but clearly there were challenges in meeting
multiple design criteria while maintaining or increasing activity
against GyrB. The QMOD control procedure (Figure 8d)
exhibited stable performance, reliably picking a preponderance
of molecules with activity greater than a pK
i
of 7.5. Recall that
while the distributions corresponding to plots AC were all
signicantly dierent, conditions A and D produced indis-
tinguishable distributions in a statistical sense.
Eects of Selection Strategy on Structural Diversity of
Chosen Winners. The molecular pools selected with and
without a novelty bias exhibited indistinguishable distributions
of GyrB activity. However, the actual value of a given pool of
active inhibitors is aected by chemical composition. A single
active inhibitor along with several nearly identical variants will
generally be less useful that the same inhibitor along with
several equipot ent but structu rally dierent variants. We
dened a threshold of pK
i
7.5 to identify molecules with
desirably high activity (winners) and compared the structural
diversity of the winners chosen within the dierent selection
procedures. The standard selection procedure that included
novelty and activity found structurally diverse active molecules.
The plots in Figure 9 show the distribution of pairwise 2D
(left) and 3D (right) similarities of the winners. The diversity of
Figure 5. Molecular novelty computation compares the interaction score prole of the training molecules in their explored poses (yellow surface,
Panel A) to that of a new molecules probable poses (blue surface, Panel B). The scoring proles are computed against the pocketmol (green
surface) and antipocketmol (red surface), which occupies space that would otherwise be empty. Compound 10, from the initial batch of 50 candidate
ligands, contained a novel substitution (shown in blue). This substituent has a natural clash with the pocketmol when aligned to training molecules
(blue arrow). The pocketmol incorrectly placed a wall there due to inadequate exploration within the training set. The clash produced a tilted pose
(not shown), resulting in a low-condence prediction that was signicantly lower than the experimental value.
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winners resulting from the standard QMOD procedure is shown
in green, and that resulting from the control procedure without
novelty is shown in magenta. The distributions of 2D similarity
diered primarily in the tails, with the standard procedure show-
ing very few highly similar winning pairs compared with the
control procedure. Also, the standard procedure identied a small
population of divergent pairs that were missed by the control
procedure. The 3D similarity distributions exhibited much more
substantial dierences, with a very signicant shift toward lower
mutual similarity within the population of winners from the
standard procedure. Figure 9 shows an example of a typical highly
similar pair (compounds 11 and 12) from the control procedure
along with a structurally divergent pair (compounds 13 and 14)
from the standard procedure. The protrusion of 13 (lower right, in
blue) is particularly stark. Notably, inhibitors containing 7-position
substitutions also possessed markedly improved activity against
ParE,
5
with dual-inhibition of GyrB and ParE being desirable in
the context of antibacterial development.
The use of a novelty bias in compound selection drove the
computational exploration of structural diversity. This is easily seen
in the evolutionary design tree shown in Figure 10. Two selection
pathways are depicted that led to two structurally dierent, yet
active, gyrase inhibitors. In round 2 (left side of Figure 10), 15
(dashed arrow) was selected for novelty because of the new
interactions made with the model from the benzyl-ester
substitution at position 7 of the benzimidazole. In round 7, 16
was selected for activity, where condence was derived from 15.In
round 8, 17 was selected condently based on similarity to 16.By
the nal round, QMOD had converged on making condent and
accurate predictions for position 7-substituted molecules (e.g., the
prediction error for 17 was just 0.3 log units and was predicted
with a condence value of 0.98). On the right-hand side of Figure
10, a separate branch of selections without a substituent at position
7 was also elaborated. In round 3, 18 was selected for activity
(similar to 3). In round 8, QMOD identied one of the most
active compounds in the entire set. Compound 19 was accurately
predicted with high condence (similar to 18). Molecules 17 and
19 are examples of the most active and structurally dissimilar
molecules in the entire pool.
A signicant driver of the 3D structural diversity in the
standard procedure arose based on the discovery of multiple
active inhibitors (e.g., compound 13)withsignicant 7-position
substituents. Figure 11 shows the surface envelope of the winners
from the standard selection procedure (green) along with that
from the control procedure (magenta). These poses were derived
by docking into an experimentally determined GyrB protein
structure to provide a common target for visualization of the
spatial exploration of the binding pocket. The corresponding
circled areas identify the binding pocket space that was explored
based on active selection of novel molecules that was missed
when focusing solely on activity. One of the pitfalls in exploring a
binding poc ket without the benet of an experimentally
determined protein structure is that the degree to which the
pocket can be dened is driven purely based on synthesis and
assay of compounds. In this purely apples-to-apples comparison
of two computationally driven selection procedures, it was clear
that a quantitatively driven strategy to explore space beyond what
had been mapped led to the discovery of a cavity capable of
oering increases in inhibitor activity. The class of 7-position
substituted inhibitors showed notably better dual-inhibition
proles,
5
illustrating a concrete biological benet of this type of
structural diversity.
Figure 6. (A) Distribution of experimentally measured activity for the QMOD standard procedure, comparing the 40 molecules chosen based on
predictions of high activity (green curve) and the 40 molecules chosen based on structural novelty (blue curve). (B) Comparison between the
QMOD standard procedure (green curve) and the control procedure (magenta curve), which made selections based solely on activity predictions.
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In addition to considering the two variants of the QMOD
approach, we also ran a descriptor-based QSAR approach that
combined 2D molecular ngerprints with the random forest
learning method (termed RF).
1315
Two procedures using
the RF approach were run, paralleling the two procedures used
by QMOD (see Figure 1). Selection of novel molecules with
the RF approach was done by clustering compounds in the
selection pool based on their ngerprints and identifying cluster
centers. Among the pools of molecules selected for activity by
either the QMOD or RF method, whether or not active novelty
bias was employed, no signicant dierences in the distribu-
tions of experimental activities were found (KS test p-value >0.05
in all pairwise comparisons).
However, the RF approach, either with or without a novelty
component within the selection procedure, produced far less
diverse pools of winners. Figure 12 (left plot) shows the 3D
similarity distributions of pairwise winner comparisons for the
two QMOD variants and the two RF variants. Use of diverse
ngerprint cluster centers failed to make an impact on the
structural diversity of winners for the RF approach (KS test
p-value = 0.33). However, while the QMOD standard approach
produced a much more diverse pool of winners than the control
approach without active novelty selections, the QMOD control
approach produced a signicantly more structurally diverse
pool of winners than either RF procedure (KS p-value 0.01).
The lack of diversity is directly evident in the histogram of
synthetic sequence numbers shown in Figure 12 (right plot),
with the RF approach exhibiting just two primary peaks corres-
ponding to early- and midproject. The QMOD approach ex-
hibited four peaks, including a set of active inhibitors from late
in the project. Compounds 13, 16, and 17 (Figures 9 and 10)
all corresponded to the rightmost peak, and all of which were
made after any experimentally active selections from the RF
procedures.
From the middle peak of winners in the synthetic sequence
order was a winner shared between the QMOD and RF
approaches (sequence #219). Among the winners from the RF
protocol, 55% had extremely high 3D similarity to that single
compound (8.50), compared with just 12% of the QMOD
control winners. The RF procedure was certainly successful in
identifying active inhibitors, but the procedure, even with a
novelty bias, ended up strongly over-represented with multiple
examples of highly similar molecules.
One property of sophisticated regression methods such as
random forest learning is that many aspects of the population
statistics of a training set are well-modeled in order to reduce
errors when tested on new data. The models are explicitly
aected by both the prevalence of output values and particular
features. In a molecular modeling application, it is frequently
the case that one specically designs molecules that literally
reach beyond those whose behavior has been modeled.
Consider two design candidate molecules, both of which will
turn out to be highly active. Suppose that one of the molecules
is highly similar to a pre-existing training molecule in terms of
its computed features and one is not. A sophisticated correlative
machine such as a random forest predictor will correctly assign
a high activity to the former active ligand. But, it will tend to
predict a value for the latter ligand that is close to the maximum
likelihood value based on the distribution of training molecules
activities (typically close to the mean or median activity). A
midrange prediction for an unknown is a wise play in a
probabilistic sense, but it reects no knowledge of the
structureactivity relationship. This near neighbor eect
manifested itself here very directly. The compounds that were
correctly ranked highly during the selection process for the RF
method tended to be structurally similar to pre-existing active
compounds.
To test this directly, we constructed an RF model using the
same nal training molecules as were used for the nal QMOD
standard model. Both methods identied active compounds
among their top 10 ranked predictions (mean experimental pK
i
in both cases of 8.0). However, the 2D structural similarity of
the top-ranked RF molecules to the training molecules was
much higher than for the QMOD approach (KS p-value
0.001). This was also seen in the reverse direction. Among the
test compounds with pK
i
7.9 (the most active group of
compounds), there was signicant variation in the 2D similarity
of each compound to its nearest training neighbor. The set of
10 furthest neighbors from the training set were arguably the
most interesting compounds from the perspective of requiring
an accurate computational prediction. They had a mean
experimental activity of 8.2. For these, the RF predictions
averaged just 7.0, with just a single compound predicted to have
pK
i
7.5. For QMOD, the predictions averaged 7.8, with 7/10
compounds predicted to have pK
i
7.5. The full set of training
compounds had experimental activity with mean 6.9 ± 0.92 and
median activity of 7.1. The RF prediction simply regressed to
the wisest guess of activity for the most dicult compounds,
making use of information on the population of potencies of
the training molecules. The QMOD predictive methodology
has no ability to make use of population-based information, but
despite that, for these dicult compounds, made predictions
that correctly identied most as highly active.
Figure 7. Three distributions of experimental activities shown are all
highly signicantly dierent from one another: 40 compounds selected
for activity (green), 40 selected for novelty (blue), and the next 80
actually synthesized after the 39 that formed the QMOD initial
training set (red).
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One of the surprising aspects of the results is that multiple
approaches yielded quite similar population and correlation
statistics in terms of the activities of the molecules chosen
under dierent selection protocols. These approaches would all
be reasonably characterized as working well on that basis.
However, when considering the characteristics of the structures
of the pool of active selected molecules, very sharp dierences
arose.
Active Learning: Abstract versus Physical Models.
What we have described in terms of explicit design bias toward
novel compounds is related to other active learning approaches,
both in the broader machine learning eld as well as within
computer-aided drug discovery (see the review by Kell
16
for a
17
used active learning in
combination with support-vector machine (SVM) classiers to
iteratively construct QSAR models with the goal of identifying
active compounds quickly. They found that a selection strategy
of seeking highly condent actives (similar to our potency
selections) was eective for nding active ligands and that a
strategy of decision-boundary selections was most eective for
improving the QSAR models themselves. The study treated
activity as a binary variable and did not structure the selection
activity alone and did not assess questions of structural
diversity. Fujiwara et al.
18
studied active learning in the context
of virtual screening and considered the question of structural
diversity. As with the Warmuth study, compound activity was
considered as a binary variable and temporal considerations
Figure 8. Experimental activity of molecules selected is plotted against selection order under dierent protocols. The bars indicate standard
deviations within local windows, and the curves represent a smoothed window-average for each trajectory.
Journal of Medicinal Chemistry Article
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were not taken into account. They showed advantages for
combining a diversity-driven model building strategy with a
selection method that sought new ligands on which dierent
models produced maximally divergent predictions.
We have explicitly focused on procedures designed to mimic
the constraints of a lead optimization exercise, with real-valued
compound activities and temporally ordered chemical space
exploration. Our direct comparison of the QMOD approach
with a parallel random-forest approach exposed dierences that
relate to the assumptions underpinning a physical QMOD
model compared with an abstract mathematical model. The
central assumption made by machine-learning methods such
as the random-forest approach or support-vector machines is
that training and testing examples are drawn randomly from
the same population. So, the distributional characteristics of
the activities of molecule s and of the structural descriptors
are a ssumed to be th e same. Under conditions where t hese
assumptions are true, such methods can produce reliably
accurate predictions, where the distribution o f test errors will
match estim ates made by te chniques such as cross-validation.
The detailed algorithmic underpinnings of suc h methods
actively game these assumptions, in order, for example, to
reduce the eect of putative outliers in a training set on learned
decision boundaries. However, in a lead optimization exercise,
both the structural characteristics and activity proles of
compounds made later will be quite dierent (by design!) than
those of compounds made earlier. With the RF approach, even
when making active selection of structurally diverse molecules,
no increase in structural diversity among the highly active
selected molecules was observed (see Figure 12, red and blue
curves in the left-hand plot).
In order for the iterative selection/test/renement procedure
to identify a pool of highly active molecules that are also
structurally diverse, two things must be true. First, the selection
strategy should incorporate structural diversity. Second, the
predictive modeling method must be able to incorporate
information from novel compounds so as to correctly identify
new compounds that are both active and structurally novel
compared with previously known actives. Recall from Figure 6,
the structurally novel molecules included signicant numbers
Figure 9. Structural diversity among the molecules selected using the QMOD procedure that included an active novelty component was signicantly
higher in both 2D (left) and 3D (left). At bottom, example pairs of molecules are given from the control procedure (left) and the standard procedure
(right). This comparison considered all molecular selections from each procedure, whether derived from an activity prediction or one from novelty, a
total 80 molecules each for the standard procedure and the control procedure.
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with low activity. It is not enough merely to seek novelty in a
selection procedure. The predictive models must be capable of
making risky bets in order to discover a pool of highly active
molecules that exhibit a wide range of structural characteristics.
A pro-diversity bias alone, as with the novelty-biased RF method,
does not guarantee a diverse pool of actives at the end of iterative
Figure 10. Examples of molecular selection based on novelty or on high-condence predictions of high activity give rise to a branched pattern of
chemical exploration.
Figure 11. Structural diversity among the molecules selected using the QMOD procedure that included an active novelty component was
signicantly higher in both 2D (left) and 3D (right).
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lead optimization. The QMOD approach makes use of each
training molecule to come up with a single physical model. A
molecule whose high activity and unusual descriptors might be
essentially shrugged o by an RF or SVM learning machine
will be incorporated into a QMOD pocketmol in a manner that
maximizes model parsimony while also explaining the high
activity. Because the QMOD model is capable of correctly pre-
dicting activity values at or beyond the extremum observed
during training, and because it may do so for structurally novel
molecules, the iterative procedure that combined predictions of
potency with selections of novel molecules produced a diverse
pool of winners.
Figure 12. Structural diversity among the winners chosen by the RF procedures was much lower than for QMOD (left plot). This lack of diversity
stemmed from the lack of diverse selections from the overall project chemical population (right plot).
Figure 13. Relationship of the nal QMOD standard pocket model to the GyrB binding site. Compound 20 in its optimal predicted QMOD pose
(atom color) had rmsd of 0.5 Å from the experimentally determined bound state (yellow). Alignment of the QMOD pocketmol and optimal ligand
poses to the protein structure was done with a single alignment transformation that produced a close alignment of the benzimidazole inhibitor core.
Congurational deviations are reected primarily in the pendant moieties.
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Relationship of the Induced Binding Pockets with the
GyrB ATP Binding Site. The foregoing discussion has
qualities of the ligands produced by dierent selection schemes.
While there were clearly benets to the QMOD approach over
the pure machine-learning RF method, perhaps the most salient
advantage from a molecular design perspective is depicted in
Figure 13. The QMOD approach induces the structure of an
actual binding pocket, and that pocket has a direct relationship
to the true biological active site that was responsible for the
activity patterns observed. The QMOD pocket forms a funnel-
like shape, with an open area corresponding to where solvent
exists. Compound 20 is shown in its predicted conformation
along with the experimentally determined one, reecting no
signicant deviations and capturing all pendant conformational
ips correctly.
In total, 11 structures of bound inhibitors were aligned to
one another based on protein pocket similarity,
19
and the
predicted poses from the QMOD approach were compared to
the bound congurations using the alignment from Figure 13.
The predicted poses from the QMOD nal pocketmol had
mean rmsd of 1.2 Å, with all but 2 having rmsd less than 1.5 Å.
Note that rms deviation is somewhat dicult to interpret here.
Barring a grossly dierent QMOD prediction of the
benzimidazole core, which moved very little in the GyrB
structures, the measured rmsd would tend to be relatively small.
Another measurement of concordance between the pocketmol
and protein compares the contact patterns for each ligand to
the pocketmol or to the protein. The degree of concordance
can be quantied by permutation of atom numbers. Given that
a particular set of a ligands atoms have contact with the
pocketmol and another set has contact with the protein, we can
count the number of contacts that are shared. If we randomize
the atom numbering order many times for the pocketmol-
bound ligand, we can count the number of times that the
number of shared contacts is greater than or equal to the
observed number in order to estimate the likelihood of this
occurring by chance. In all but three of the eleven cases, there
was a statistically signicant relationship in the contact patterns
(p < 0.05).
Figure 14 shows additional detail, illustrating the direct
correspondence between pocketmol probes and key moieties
on the protein. The left-hand view highlights the reason behind
the conformational choice for the methyl-ester substituent of
compound 20, which was correctly predicted (marked with a
blue arc). The carbonyl ester oxygen makes a hydrogen bond
with the NH probe of the pocketmol, which parallels the
same interaction with Asn-1046. The terminal methyl of the
ester makes a hydrophobic interaction with a methane
pocketmol probe, paralleling an interaction with Ile-1094.
The right-hand view highlights two carbonyl probes that mimic
the eect of Asp-1073 and two NH probes that mimic Arg-
1136. This degree of qualitative correspondence between
pocketmol and protein is typical of our previous work.
2,3
Figure 15 shows the analogous depiction of compound 20,
but using the nal QMOD pocketmol that arose from the
Figure 14. QMOD standard procedure yielded a pocket model where there was a direct correspondence of many probes to particular atoms in the
actual GyrB binding pocket. Pocketmol probes that do not interact with compound 20 (atom color) have been omitted from the display for clarity,
and the protein has been trimmed to highlight areas of correspondence. The two views shown are ipped front to back.
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control procedure. Recall that the structural variation of the
nal pool of active selected ligands was much reduced and that
the spatial probing of the binding pocket bordered by Asn-1046
and Ile-1094 was shallow (see Figure 11). The prediction for 20
was both numerically poor (low by 2.1 log units) and predicted
the incorrect orientation of the 7-position methyl ester. The
induced pocket here was unable to correctly accommodate the
substituent, also showing a shift of the central scaold away
from its optimal position. While there were areas of good
correspondence, especially with respect to the surface shape of
the base of the binding pocket, the model induction process is
sharply limited by the set of selected compounds. For the 11
concordant contact patterns (compared with 8/11 for the
QMOD standard predictions). In operational use of such
modeling methods during lead optimization, mindful produc-
tion of chemical variations that explicitly probe the edges of a
model can produce signicant improvements in the corre-
spondence of rened models with biological reality.
For completeness, because we had bona f ide structures of the
GyrB binding pocket, we also made a comparison of the
QMOD predictions to docking and scoring the nal pool of
unselected molecules. Using a single structure and the score of
the top-ranked docking pose for each inhibitor did not produce
a signicant rank correlation. It is conceivable that a more
sophisticated procedure such as MM-PBSA
20
might have
yielded a reasonable correlation. Brown and Muchmore
reported an average RMSE for predicted pK
i
using MM-
PBSA on three targets of 0.75 (range 0.660.89) using linearly
rescaled predictions to account for extreme slope and intercept
deviations between computation and experimental values. The
QMOD nal standard model yielded 0.76 RMSE with no
scaling correction on the 317 remaining unselected molecules,
which is clearly comparable. Molecules pairs whose activity was
dierent by 0.5 pK
i
units or greater were correctly ranked more
than 70% of the time (p 0.001). Rank correlation of this
quality is challenging because over 80% of the experimental
activity values fell within 1.5 log units of one another and over
90% within 2.0 units. It is encouraging that a method such as
QMOD, with no information of any kind regarding either the
bound conguration of ligands or of the actual binding site
composition and geometry, could produce predictions of both
activity and bound pose that are competitive with sophisticated
structure-based methods.
CONCLUSIONS
We believe that this study has approached the QSAR modeling
question in a novel manner. We explored how dierent
computational selection strategies shaped and produced
dierent synthetic trajectories. There were four primary results.
First, the iterative QMOD procedure rapidly converged on
models that reliably identied highly active molecules. Second,
explicit computational selection of novel molecules directly lead
to a much more structurally diverse pool of active inhibitors,
despite not producing a pool with a dierent distribution of
experimental activities than a control procedure with no novelty
focus. Third, the induced binding site model showed strong
Figure 15. QMOD pocket model that resulted from the procedure lacking an explicit novelty bias produced a poor prediction for compound 20
(atom color). The depiction here is analogous to that from Figure 14.
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concordance with the experimentally determined binding site,
both in terms of absolute predicted poses as well as ligand/
pocket contact patterns. Fourth, direct comparison with
descriptor-based QSAR methods showed that while such
models yielded similar distributions of activity among selected
molecules, the structural diversity of selected active molecules
was much lower than for QMOD. QMOD identied examples
of active molecules across the entire arc of the projects
chemical exploration, while the descriptor-based approaches
instead produced many examples of highly similar minor
variants clustered around the midpoint of the projects history.
There are two major lessons to be learned from this work,
which we hope to further validate on additional systems in the
future. First, there appears to be a signicant hidden cost to
reliance upon molecular design strategies that do not actively
seek to probe new chemical functionality in a spatial sense.
While such strategies may well identify compounds with
desirable properties, they may completely miss the identi-
cation of entire classes of active compounds. Here, for
example, strong activity against GyrB and ParE was exhibited by
compounds discovered through the selection procedure that
sought three-dimensional structural novelty in order to test the
physical boundaries of the evolving models. Second, statistical
regression methods whose fundamental basis for prediction
relies upon correlations between features and desired output
values impose hidden costs. They do so by being strongly
dependent upon the existence of near-neighbors with known
activity in order to accurately predict a new compound to have
similar activity. In molecular activity optimization, eort is often
placed on design goals toward or even beyond the extreme end
of the distribution of known molecular activities. Truly active
molecules that are structurally novel in the descriptor space
being used by a correlative machine will be underpredicted as a
consequence of the gaming strategy employed by statistical
regression methods.
The issues of conrmation bias and correlation fallacies
discussed in a recent perspective
4
appear naturally in the
iterative application of predictive modeling for design of active
molecules. Given a method that depends on noncausative
correlations to predict activity, selection of the molecules
predicted to be active will tend to automatically self-conrm,
because only those candidate molecules that are highly similar
to known molecules with high activity will tend to be top-
ranked. The structurally novel compounds that would have
been shown to be active remain invisible in practice, because
they will have been predicted to have middling activity. In
typical machine-learning problems, inductive bias issues will
show up in the distribution of prediction errors on dierent
types of test objects. In the case of medicinal chemistry lead
optimization, such bias issues may altogether suppress the
synthesis of molecules that do not conrm the hypothesis, so
no errors may become apparent.
By making use of a dierent molecular selection strategies,
each of which is nominally equally accurate in aggregate
behavior, very dierent outcomes will arise from repeated
temporal iteration. The resulting molecules having the high
activity sought during optimization will reect the hidden or
explicit biases embedded in the predicti ve modeling ap-
proaches. An approach whose basis for prediction mimics the
protein ligand binding process, coupled with an explicit
selection strategy designed to expand model coverage, will
tend to identify a diverse pool of molecules. The structural
diversity will most likely manifest itself in properties that were
not directly optimized. When making use of purely correlative
learning machines, the unseen cost can manifest itself as a
numerous but narrow pool of molecules. Given the challenging
problem of drug discovery, we would argue that generation of a
diverse pool is generally the more desirable outcome.
EXPERIMENTAL SECTION
Molecular and Activity Data. Overall, 426 compounds formed
the data set for the study. All were previously synthesized and tested as
part of a lead optimization project.
5
Three-dimensional molecule
structures were provided as an SDF le. The standard Surex
procedure was used to protonate, ring-search, and minimize the
ligands (sf-sim +misc_ring -misc_outconfs 5 +fp prot gyrasemols.sdf
gyr). This resulted in up to ve conformations per inhibitor, which
were then provided to the QMOD procedure, in which all molecular
poses were produced. Assays were performed as reported in Charifson
et al.,
5
and assay values were converted into molar pK
i
units (9.0 being
equivalent to a K
i
of 1 nM). The molecules were named based on the
actual lead optimization projects synthetic sequence order (e.g.,
gyrase000001 to gyrase000426).
Computational Procedures. The QMOD procedure is fully
automatic, requiring no human choice points. For this work, default
parameters were used, employing Surex QMOD version 1.5. There
were two signicant algorithmic introductions in this version,
compared with that reported in the last methodologically focused
study.
3
First, the notion of model parsimony has been included
directly in the search for optimal binding pocket models. Second, a
procedure for computing molecular novelty for candidate models was
implemented (see Figure 5).
QMOD denes model parsimony based on the degree to which
training molecules that have similar potencies also quantitatively share
similar optimal bound poses. This is expressed in terms of a weighted
sum of pairwise similarities of all nal ligand poses, where molecule
pairs with similar activity receive higher weight than those with
dierent activity values. Parsimony was introduced as a means to
choose from among models of nominally equivalent residual training
errors.
3
Here, model parsimony has been made part of the model
generation process itself. The procedure that is used to select probes
for inclusion in a pocketmol simultaneously optimizes the tto
experimental data as well as model parsimony. The standard procedure
for producing a de novo pocketmol requires a single command (sf-
qmod.exe runsetup SetupFile) that produces a script that will generate
initial alignment hypotheses, full alignments of training ligands, and
nal pocketmols. The setup le contains information on pathnames to
training ligands and their activities, which ligands to use for hypothesis
generation, and modications to default parameters for model building
if desired. By default, three models are generated, each using dierent
probe densities. The model with the highest parsimony was selected
for iterative renement.
The initial induced model was then used for testing the next
window of molecules and selections were made automatically based on
two criterion: molecules predicted with high condence to be the most
active, and molecules predicted as the most novel. The transition
between rounds involved the addition of selected molecules to the
training data and a series of automated steps required for preparation
of the next model renement round (as with initial model building,
QMOD produces a script based on the list of new molecules and
activities). The automated preparation involved compression of the
training ligand poses explored during model induction and testing.
The compression scheme seeks the highest scoring poses against the
pocketmol while enfo rcing conformationa l d iversity a mong the
retained poses. As with the initial model, alignments are produced
for the new molecules along with a corresponding pool of new probes.
The new molecules alignments and the new probes are added to the
pose and probe pools, respectively. The next round of model
renement begins with the previous optimal pocketmol and repeats
the standard learning procedure using the amended probe and pose
pools.
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Novelty is quantied in a three-dimensional sense by measuring the
degree to which a new molecule explores the space of the binding
pocket with new chemical functionality. Statistics are computed based
on the interactions between the explored pool of training ligand poses
with the pocketmol and the unoccupied space near the pocketmol
(termed the antipocketmol). The explored pool of training ligand
poses encompasses the nal optimal poses of each training ligand and
also includes all poses for each that are highly 3D similar to the nal
pose of any training molecule. The antipocketmol is constructed such
that it borders the explored pose pool and provides a symmetrical
nonoverlapping representation of the pocketmol, highlighting regions
of the binding pocket that have not been explored or modeled. For
each pocketmol and antipocketmol probe, the mean and standard
deviation of scores of the explored training pose pool are computed.
These statistics form a baseline interaction prole of the induced
model for each probe. Upon tting a new test molecule into the
pocketmol, pose variations that share high 3D similarity to any of the
optimal training poses are cached, and the mean score for each probe
is computed. Molecular novelty for a test molecule is the average of
the Z-scores for the test molecule probe mean scores, using the
statistics derived from the training data to provide the mean and
standard deviation for each probe s Z-score normalization. So,
molecules that interact with the pocketmol and surrounding region
differently than the training ligands receive a higher novelty score than
otherwise. This denition of novelty is highly context dependent and
quite dierent from pure molecular similarity computations. For
example, a single methyl group addition to a training molecule will
generally have very low impact on a similarity computation. However,
if the methyl group pushes into unexplored space (which may or may
not contain a pocketmol probe), the novelty score will tend to be high.
By default (and for all experiments reported), QMOD makes use of
the highest-scoring alignment hypothesis upon which t o base
alignments of other training ligands. Additional controls were carried
out using alternative hypothesis alignments used for seeding the initial
ligand alignment during de novo model induction. We identied the
ve most dissimilar hypothesis alignments (data not shown) from the
original alignment used in the standard run (see Figure 3 Panel A) and
repeated the iterative modeling protocol as described above (see
Figure 1). Results from these alternative starting points revealed
similar performance with respect to enrichment of highly active
molecules from those compounds selected, convergence on selecting
active inhibitors over time, and identifying structurally diverse active
compounds when actively selecting for structurally novel molecules.
QMODs performance proved to be robust in the presence of alternate
initial alignment conditions.
As a control procedure, we employed the random forest machine
learning technique.
1315
It is an ensemble classication approach that
constructs multiple decision trees using a random sampling approach
in order to minimize generalization errors. We used the Random
Forest method implemented in version 4.62 of the randomForest
package for the R software (version 2.12.2). MDL 320 ngerprints
21
were generated using the ngerprint packages implemented by Mesa
Analytics (www.mesaac.com). The iterative procedure paralleled that
used for QMOD, making use of default parameters for the RF learning
procedure. To mimic the novelty procedure, we performed K-means
clustering (with K = 5) among the pool of molecules from which
selections could be made and chose the cluster centers. This provided
diverse structures according to the features employed by the classier.
AUTHOR INFORMATION
Corresponding Author
*E-mail: ajain@jainlab.org. Phone: 415-502-7242.
Notes
The authors declare the fo llowing competing nancial
interest(s): Dr. Jain has a nancial interest in BioPharmics
LLC, a biotechnology company whose main focus is in the
development of methods for computational modeling in drug
discovery. Tripos Inc. has exclusive commercial distribution
rights for the Surex platform, licensed from BioPharmics LLC.
ACKNOWLEDGMENTS
The authors gratefully acknowledge NIH for partial funding of
the work (grant GM070481) and Dr. Ann E. Cleves for helpful
ABBREVIATIONS
QMOD, Surex Quantitative Modeling; GyrB, DNA gyrase;
ParE, Topisomerase IV; RF, random forest classier; SVM,
support-vector machine
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• ##### Extrapolative prediction using physically-based QSAR
• "When data are plentiful and interpolation is valuable , application of such methods makes sense. Even in more complex cases, use of such methods can provide baseline performance estimates, as had been done previously for the data sets described here, and as we have done previously [6][7][8]. Methods requiring manual 3D alignment can be useful to go beyond what is possible with 2D methods to achieve a degree of extrapolation, as was shown in the work by Sutherland et al. in the work that described the data sets under study here [8]. However, there are practical challenges in constructing complex alignments and limitations in their breadth of application on new molecules. "
[Show abstract] [Hide abstract] ABSTRACT: Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction . Here, we apply QMOD to a 3D-QSAR benchmark dataset and show broad applicability to a diverse set of targets. Testing new ligands within the QMOD model employs automated flexible molecular alignment, with the model itself defining the optimal pose for each ligand. QMOD performance was compared to that of four approaches that depended on manual alignments (CoMFA, two variations of CoMSIA, and CMF). QMOD showed comparable performance to the other methods on a challenging, but structurally limited, test set. The QMOD models were also applied to test a large and structurally diverse dataset of ligands from ChEMBL, nearly all of which were synthesized years after those used for model construction. Extrapolation across diverse chemical structures was possible because the method addresses the ligand pose problem and provides structural and geometric means to quantitatively identify ligands within a model’s applicability domain. Predictions for such ligands for the four tested targets were highly statistically significant based on rank correlation. Those molecules predicted to be highly active ($$\hbox {pK}_i \ge 7.5$$) had a mean experimental $$\hbox {pK}_i$$ of 7.5, with potent and structurally novel ligands being identified by QMOD for each target.
Full-text · Article · Feb 2016
• ##### A structure-guided approach for protein pocket modeling and affinity prediction
• "For this work, default parameters were used, employing Surflex-QMOD version 1.5. There were two significant algorithmic variations investigated here, compared with that reported in the most recent study [17]. First, an initialization protocol was added that incorporates multistructure docking and data integration that uses bound ligand poses to guide the generation of an alignment hypothesis. "
[Show abstract] [Hide abstract] ABSTRACT: Binding affinity prediction is frequently addressed using computational models constructed solely with molecular structure and activity data. We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure-activity relationships. The Surflex-QMOD approach has been shown to produce accurate predictions of binding affinity by constructing an interpretable physical model of a binding site with no experimental binding site structural information. We introduce a method to integrate protein structure information into the model induction process in order to construct more robust physical models. The structure-guided models accurately predict binding affinities over a broad range of compounds while producing more accurate representations of the protein pockets and ligand binding modes. Structure-guidance for the QMOD method yielded significant performance improvements, both for affinity and pose prediction, especially in cases where predictions were made on ligands very different from those used for model induction.
Full-text · Article · Nov 2013
• ##### QSAR Modeling: Where have you been? Where are you going to?
[Show abstract] [Hide abstract] ABSTRACT: Quantitative structure−activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations , successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
Full-text · Article · Jan 2013 · Journal of Computer-Aided Molecular Design
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