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Lean-Docking: Exploiting Ligands’ Predicted Docking Scores to Accelerate Molecular Docking

  • Tokyo University

Abstract and Figures

In structure-based virtual screening (SBVS), a binding site on a protein structure is used to search for ligands with favorable nonbonded interactions. Because it is computationally difficult, docking is time-consuming and any docking user will eventually encounter a chemical library that is too big to dock. This problem might arise because there is not enough computing power or because preparing and storing so many three-dimensional (3D) ligands requires too much space. In this study, however, we show that quality regressors can be trained to predict docking scores from molecular fingerprints. Although typical docking has a screening rate of less than one ligand per second on one CPU core, our regressors can predict about 5800 docking scores per second. This approach allows us to focus docking on the portion of a database that is predicted to have docking scores below a user-chosen threshold. Herein, usage examples are shown, where only 25% of a ligand database is docked, without any significant virtual screening performance loss. We call this method "lean-docking". To validate lean-docking, a massive docking campaign using several state-of-the-art docking software packages was undertaken on an unbiased data set, with only wet-lab tested active and inactive molecules. Although regressors allow the screening of a larger chemical space, even at a constant docking power, it is also clear that significant progress in the virtual screening power of docking scores is desirable.
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Lean-Docking: Exploiting Ligands’ Predicted
Docking Scores to Accelerate Molecular Docking
Francois Berengera,,Ashutosh Kumara,Kam Y. J. Zhang,and Yoshihiro
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Japan
Laboratory for Structural Bioinformatics, Center for Biosystems Dynamics Research,
RIKEN, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
aShared first author.
In Structure-Based Virtual Screening (SBVS), a binding site on a protein structure
is used to search for ligands with favorable non-bonded interactions. Because it is
computationally hard, docking is time-consuming and any docking user will eventually
encounter a chemical library which is too big to dock. This problem might arise be-
cause there is not enough computing power, or because preparing and storing so many
3D ligands requires too much space. In this study, however, we show that quality
regressors can be trained to predict docking scores from molecular fingerprints. While
typical docking has a screening rate of less than one ligand per second on one CPU core,
our regressors can predict about 5800 docking scores per second. This approach allows
us to focus docking on the portion of a database which is predicted to have docking
scores below a user-chosen threshold. Here, usage examples are shown, where only 25%
of a ligand database is docked, without any significant virtual screening performance
loss. We call this method “lean-docking”. To validate lean-docking, a massive docking
campaign using several state-of-the-art docking software was undertaken on an unbi-
ased dataset, with only wet-lab tested active and inactive molecules. While regressors
allow the screening of a larger chemical space, even at constant docking power, it is
also clear that significant progress in the virtual screening power of docking scores is
The conformation and pose of a small molecule in a protein binding pocket can be predicted
by several methods including molecular docking,1–4 de novo design5,6 and molecular dynamics
simulation.7–9 Among these methods, molecular docking is one of the most popular and has
been successfully applied not only to predict the binding mode but also to rank-order a
library of small molecules for cherry picking in inhibitor discovery campaigns.10–14 Owing to
the utility of molecular docking in drug discovery, several improvements have been reported in
the last decade.15–19 Despite significant developments, routine docking of screening libraries
with more than a billion compounds is still beyond reach of most people, due to the huge
computing power and storage required. Moreover, most docking programs lack the speed and
efficiency necessary to execute ultra-large virtual screening campaigns. This resource gap is
further widening as the size of commercially available chemical space is increasing rapidly,20
due to the rise of automated synthesis and increased availability of diverse building blocks.
In this context, docking protocols that can quickly screen ultra-large chemical libraries in the
absence of large computing and storage capacity would be of immense use in drug discovery.
To speed-up molecular docking of large chemical libraries, different filtering approaches
based on physicochemical properties, drug-likeness, similarity to known inhibitors etc. have
been proposed. Although, these approaches may lead to faster screening of large compound
libraries, many interesting scaffolds may be filtered out. In order to fully exploit the po-
Table 1: Overview of methods to accelerate docking (in chronological order of publication).
Machine-learning method
Name Reference Classifier Regressor Algorithm Molecular encoding Validation dataset Docking software Claimed acceleration Remarks
Progressive Docking Cherkasov et. al. (2006) N/A PLS Iterative 58 molecular descriptors Glide 1.2x to 2.6x Maintains 80% to 99% of hit recovery rates
Spresso Yanagisawa et. al. (2017) N/A Sequential 102 proteins from DUD-E Glide HTVS 200x
Conformal Prediction Svenson et. al. (2017) N/A Iterative 97 molecular descriptors 41 proteins from DUD-E Glide 10.6x
VirtualFlow Gorgulla et. al. (2020) N/A N/A Distributed Ready-to-dock ligands
Deep Docking Gentile et. al. (2020) N/A Iterative FRED 50x
Lean Docking This work N/A Linear SVR Sequential
3 proteins: SHBG, SARS
3CLPro, HIV1 reverse
Sum of
docking scores
Rigid molecular
Fragmenting the whole database to screen
and docking all unique fragments is required
Random forests
based conformal
Wet-lab assay to label training set molecules
Kelch-like ECH-associated
protein 1 (KEAP1)
Smina Vinardo,
AutoDock Vina
Scaling behaviour
linear in the number
of cores
Access to a cloud computing platform /
supercomputer required
Feed-forward Deep
Neural Network
1024 bits Morgan
fingerprints (radius=2)
12 proteins: ADORA2A,
GLIC, Nav1.7, PPARγ,
Training set of 250,000 to 1M docked
molecules recommended
Counted atom pairs
15 proteins: LIT-PCBA
Gold, Autodock-
Vina, MOE-Dock
(partial data),
Glide (partial
4x to 41x without loss
of top-scoring actives
(depending on
docking VS
performance on given
protein); raw scoring
compared to CCDC
Gold > 58000x
Training set of 10,000 docked molecules
tential of expanded chemical space by maximizing the number of evaluated compounds,
several docking reduction approaches have been proposed (Table 1). In “Progressive Dock-
ing”,21 QSAR models predicting Glide docking scores are used iteratively to avoid docking
molecules predicted as non-binders. The authors report an acceleration between 1.2 and 2.6
times, while maintaining 80 to 100% hit recovery rates. Svensson et al. 22 and colleagues use
a conformal predictor classifier trained on a small set of assayed molecules. The authors ret-
rospectively validate their protocol on DUD-E,23 showing that 57% of the remaining actives
could be identified while screening only 9.4% of the remaining database (acceleration of 10.6
times). The inspirational “Deep Docking” approach24 (DD) uses deep-learning classifiers to
predict ligands with a low docking score and reduce the number of docking calculations. DD
was used to screen 1.36 billion molecules from the ZINC1525 database and its authors re-
ported an acceleration of 50 times compared to a classical docking campaign. Another work
at accelerating docking, due to Yanagisawa and colleagues,26 used docked ligand fragments
to pre-screen ligands that will require docking. They reported a 200 times acceleration com-
pared to standard docking. Another interesting approach to accelerate docking is distributed
computing. Gorgulla et al. 27 and colleagues created the VirtualFlow open-source program
for this purpose and screened more than one billion commercially-available molecules using
Autodock-Vina28 and Smina Vinardo.29 They eventually identified submicromolar protein-
protein interaction inhibitors for nuclear factor erythroid-derived 2-related factor 2 (NRF2)
and Kelch-like ECH-associated protein 1 (KEAP1). They report an acceleration linear in
the number of cores used during the distributed computation. There are good reviews about
ligand-based virtual screening30 or the combination of ligand- and structure-based meth-
ods.31,32 Jastrzębski et al. used an edge-attention graph convolutional network33,34 to train
a regressor of docking scores for the GLIDE docking software on the human protein 5-HT1B
(PDB:4IAQ; R2= 0.68). Some authors33,35 even manage to predict the binding mode of 2D
ligands. A docking score takes into account calculated non-covalent 3D interactions between
a ligand and a protein. Regression models can be trained to capture a mapping from 2D
ligands ato docking scores (for a given protein binding site and docking software). While this
might be counter-intuitive, there are publications indicating that predicting docking scores
from 2D ligands is possible.21,33,36 Related, but not exactly about docking, a random-forest
using only 2D ligand-based features could predict the mean binding affinity of a ligand for
its protein targets.36 Lyu and colleagues37 have docked 170 million make-on-demand com-
pounds against AmpC β-lactamase and the D4dopamine receptor. Their study provides key
insights about docking scores in a large library screen, with hundreds of molecules ordered
and tested after the docking screen, so that the total number of actives in a library could be
estimated by a statistical model. Cieplinski and colleagues38 have tried to predict docking
scores for the SMINA docking software39 on a few protein targets; another example showing
that this task is deemed useful.
In this article, three significant contributions are presented. i) A massive docking cam-
paign using several state-of-the-art software, on the recently published LIT-PCBA dataset.40
ii) How to train quality regressors of docking scores for 15 protein targets and four docking
software. Such regressors can score about 5800 ligands per second on a single CPU core. iii)
The lean-docking protocol to accelerate docking by several folds, with a low risk of loosing
top-scoring active molecules. However, the utility of this protocol ultimately depends on the
virtual screening power of docking scores on a given protein target.
aStrictly speaking and as noted by a reviewer, a molecular graph without spatial coordinates is 1D.
Lean-docking: docking only a fraction of a large chemical database
Figure 1: Lean-docking protocol overview.
In order to exploit fast predictors of docking scores, the lean-docking protocol is pro-
posed (Figure 1). Since training a regressor requires docking a partition of up to 10,000
ligands randomly selected from the database to screen, the first quartile docking score from
those ligands can be used as a rather conservative threshold to separate promising molecules
(predicted scores under the thresholdb) from the rest (molecules predicted to score above
the threshold). Such a protocol allows to divide several times the required docking power,
while it can have a negligible impact on the number of active molecules per N top-scoring
bDocking scores are negative.
The LIT-PCBA dataset
Table 2: Number of PDB structures, molecules and random hit-rate for the 15 protein targets
of the AVE-unbiased LIT-PCBA dataset.
Training Validation
Protein PDBs Actives Inactives Random Actives Inactives Random
hit-rate hit-rate
ADRB2 8 13 234,363 0.00006 4 78,120 0.00005
ALDH1 8 4,032 77,606 0.04939 1,344 25,868 0.04939
ESR1+ 15 10 4,188 0.00238 3 1,395 0.00215
ESR1- 15 77 3,711 0.02033 25 1,237 0.01981
FEN1 1 277 266,552 0.00104 92 88,850 0.00103
GBA 6 125 222,039 0.00056 41 74,013 0.00055
IDH1 14 30 271,537 0.00011 9 90,512 0.00010
KAT2A 3 146 261,411 0.00056 48 87,137 0.00055
MAPK1 15 231 46,972 0.00489 77 15,657 0.00489
MTORC1 11 73 24,729 0.00294 24 8,243 0.00290
OPRK1 1 18 202,362 0.00009 6 67,454 0.00009
PKM2 9 410 184,143 0.00222 136 61,380 0.00221
PPARG 15 21 3,909 0.00534 6 1,302 0.00459
TP53 6 60 3,126 0.01883 19 1,042 0.01791
VDR 2 498 199,906 0.00248 165 66,635 0.00247
Docking experiments were conducted on the 15 protein targets of the LIT-PCBA dataset40
(Laboratoire d’Innovation Thérapeutique - PubChem Assays dataset), in its Asymmetric
Validation Embedding (AVE41) unbiased version (Table 2). An unbiasing procedure was
used to split protein target ligands into a training and a validation set. LIT-PCBA was
designed to benchmark ligand and structure-based virtual screening methods. It was care-
fully curated from 149 PubChem42 assays. It contains 7844 confirmed actives and 407,381
confirmed inactive molecules.
Rigid-protein flexible-ligand docking
Docking was performed using CCDC Gold,43 Autodock-Vina,28 FRED,44 Glide45 and MOE
(Chemical Computing Group). Since LIT-PCBA contains multiple receptor PDBs per tar-
get, Gold, Autodock-Vina and FRED docking was performed on each PDB and the lowest
scoring pose (among all PDBs of a given protein) was retained. Due to the availability
of limited license tokens, Glide and MOE docking was performed for 6 out of 15 targets
(ESR1+, ESR1-, MAPK1, MTORC1, PPARG and TP53) using only the highest resolution
PDB. First, the lowest energy 3D conformer of each ligand was generated with OpenEye
OMEGA.46 For each docking program, ligands and receptors for the dataset proteins were
prepared according to standard practice as mentioned below. Binding site for each dock-
ing method was defined using the co-crystallized ligand of receptor PDB in the dataset.
Docking with Gold. Ligands were further prepared with the OpenEye OEChem toolkit.
Hydrogens were added and charges were assigned using AM1BCC.47,48 Receptors were pre-
pared with UCSF Chimera49 v1.14. Hydrogens were added and partial charges were assigned
using AMBER ff14SB.50 Docking was performed using the default scoring function and evo-
lutionary parameters. A maximum of 10 poses per ligand were generated and the lowest
scoring one was retained.
Docking with Autodock-Vina. Ligands and receptors were prepared using the protein
and ligand preparation scripts from AutoDock Tools51 v1.5.4. Receptors were prepared by
adding hydrogens, assigning Gasteiger52 partial charges before saving coordinates in the
PDBQT format. Ligands were prepared using the same protocol. The search space was
restricted to a cubic box with 25Å side-length. Docking was performed using the default
search exhaustiveness (eight). Ten poses maximum per ligand were generated and the lowest
scoring one was retained.
Docking with FRED. Conformational ensemble for FRED docking was generated using
OMEGA. A maximum of 200 conformations per compound were generated. Receptors for
FRED docking were prepared using Spruce4Docking utility program (OpenEye Scientific
Software). Ligands were scored using Chemgauss4 scoring function and a single pose per
compound was saved.
Docking with Glide. The protein structures for Glide molecular docking were prepared
using Maestro (Schrodinger Inc.) by adding hydrogens and assigning protonation states of
charged residues at neutral pH. Ligands were prepared using LigPrep (Schrodinger Inc.).
Tautomeric and ionization states of all ligands were determined using Epik.53 Molecular
docking was performed using the standard precision mode of Glide. Ligands were scored
using Glidescore with Epik penalties and a single pose per compound was saved.
Docking with MOE-Dock. The protein structures for MOE-Dock docking were prepared
using QuickPrep utility in MOE. Molecular docking was performed using rigid receptor
docking protocol. Ligand conformations were generated on-the-fly and were placed into the
binding pocket using Triangle Matcher placement method which were then scored using Lon-
don dG scoring function. Thirty poses were retained for each compound and were subjected
to rigid receptor refinement. The poses were energy minimized in the binding pocket and
were scored using GBVI/WSA dG scoring function. Finally, a single pose per compound
was saved.
A regressor to predict docking scores
Ligands were docked using the previously described protocol. To train a regressor for a given
protein target, a random partition of at most 10,000 ligands was drawn from all the training
set ligands docked on this target. If a target’s training set had less than 10k molecules,
all of them were used to train the regressor. Prior to regression modeling, 2D ligands were
standardized using Francis Atkinson’s standardizer.54 After standardization, ligands were
encoded using unfolded counted atom pairs (molenc software55) using the ChEMBL2456
feature dictionary (17,368 features). Only heavy atoms of a molecule are considered. An
atom pair consists of two atom types and their shortest distance (in bonds) on the molec-
ular graph. Distances go from zero and up to the graph diameter of a molecule. An atom
type is the tuple made of its number of pi electrons, atomic symbol, number of adjacent
(bonded) heavy atoms and formal charge. Stereochemistry is ignored. Because such a fin-
gerprint (a sparse vector of positive integers) is high dimensional and a simple but fast
regressor was wanted, the L2-regularized linear Support Vector Regressor (SVR) from lib-
linear57 was used (linwrap58 software). Only the Cparameter was optimized using 10 folds
cross-validation; leaving the parameter to its default value of zero. Cwas chosen in the set
{0.001,0.002,0.005,0.01,0.02,0.05,0.1,0.2,0.5,1,2,5,10,20,50}. It is not claimed that this
molecular fingerprint and linear-SVR combination is the best possible regressor. However,
it was good enough in practice. The test computer runs Ubuntu Linux on a 2.10GHz Intel
Xeon CPU. It takes about 80 seconds to train a regressor on a single core (40s on 16 cores;
scaling is not linear). A trained model can predict about 5800 docking scores per second on a
single core. AUC values and ROC curves were computed by the croc-curve Python script.59
R2and BEDROC values were computed by the Classification and Regression Performance
Metrics library.60
Training regressors to predict docking scores
Our results suggest that it is possible to train quality regressors of docking scores for all
protein targets of the dataset (Figure 2). High accuracy was achieved for almost all targets
with a validation R2>= 0.78. The only exception is OPRK1, with TR2 = VR2 = 0.65. The
average validation R2was found to be 0.85, which suggests that good regressors were trained
irrespective of the protein target. A vast majority of the predictions are within +/-10 of
their actual Gold scores. For example, it is the case for 97% of the predictions on the largest
validation set (IDH1; Figure 3 and Supporting Information Figure S1 for all targets). After
inspecting Figures 3 and S1, we don’t see an easy and natural way to define an applicability
domain for the regressors. Interested readers might look into the recent literature for some
pointers.61 Moreover, the validation set R2being very close to the training set one indicates
that models are not overfitting the training set. The biggest difference between TR2 and
VR2 is 0.02 only, on the TP53 target. Also, note that the size of the regressor’s training
set used was chosen to be 10,000 molecules maximum (Figure 4), which means that most
Figure 2: Regressors trained using 10 folds cross validation on a random partition of at
most 10,000 molecules from the training set of each protein target (T:10xCV, green circles),
then used to predict the validation set (V, purple circles). The protein target name, best C
parameter, training and validation set R2(TR2 and VR2) are shown at the bottom right of
each plot. Lines of best fit are also shown.
0 0.2 0.4 0.6 0.8 1
Tanimoto distance to nearest in training set
Gold score prediction error
Figure 3: Docking score prediction error on the validation set of IDH1 (largest dataset).
Density scatter plot for the actual minus predicted Gold docking score for all molecules in
the validation set, as a function of the Tanimoto distance to the nearest molecule in the
training set. Plots for all targets can be seen in Supporting Information Figure S1.
100 200 500 1k 2k 5k 10k 20k
10 Folds Cross Validation R2 +/- σ
Training Set Size
Figure 4: Performance of the regressor as a function of the training set size in 10 folds
cross-validation experiments over all protein targets. The regressor training set of size N (N
[100,200,500,1k,2k,5k,10k,20k]) was drawn randomly in each experiment. If a target has
only M < N docked molecules, then only M molecules are used to train the regressor.
regressor training sets are several times smaller than the whole available training set (e.g.
The power of such regressors to retrieve top-scoring molecules in docking can be seen in
Supporting Information Figure S2.
The virtual screening power of predicted docking scores
Table 3: Performance of Gold docking and corresponding regressors on the LIT-PCBA
validation set. Interesting cells have a green background (AUC >= 0.6,BEDROC >= 0.1,
EF >= 2 or R2>= 0.8).
V set Gold Docking QSAR
ADRB2 0.246 0.000 0.00 0.00 0.00 0.244 0.000 0.00 0.00 0.00 0.88 0.89
ALDH1 0.584 0.126 1.49 1.83 1.76 0.582 0.125 2.02 1.75 1.78 0.93 0.93
ESR1+ 0.523 0.046 0.00 0.00 0.00 0.525 0.033 0.00 0.00 0.00 0.93 0.92
ESR1- 0.580 0.032 0.00 0.00 0.00 0.566 0.035 0.00 0.00 0.00 0.96 0.96
FEN1 0.370 0.041 0.00 0.00 1.52 0.360 0.037 0.00 1.09 0.65 0.78 0.79
GBA 0.631 0.164 9.76 7.32 2.93 0.633 0.166 7.32 6.10 3.90 0.85 0.86
IDH1 0.628 0.233 11.11 11.11 4.44 0.653 0.216 11.11 11.11 4.44 0.82 0.83
KAT2A 0.426 0.060 2.08 1.04 1.25 0.451 0.059 2.08 1.04 1.25 0.80 0.80
MAPK1 0.625 0.084 1.31 0.66 1.58 0.634 0.099 2.63 1.98 1.84 0.88 0.88
MTORC1 0.511 0.008 0.00 0.00 0.00 0.556 0.035 0.00 0.00 0.00 0.84 0.85
OPRK1 0.715 0.177 0.00 0.00 3.33 0.724 0.201 0.00 8.33 3.33 0.65 0.65
PKM2 0.548 0.064 2.21 1.47 1.18 0.519 0.048 0.00 1.10 0.88 0.81 0.80
PPARG 0.709 0.173 0.00 8.18 3.32 0.721 0.172 0.00 8.18 3.32 0.95 0.95
TP53 0.722 0.225 5.79 2.76 3.34 0.707 0.180 0.00 2.76 3.34 0.89 0.87
VDR 0.365 0.011 0.00 0.00 0.12 0.360 0.015 0.62 0.62 0.37 0.80 0.80
avg 0.546 0.096 2.25 2.29 1.65 0.549 0.095 1.72 2.94 1.67 0.85 0.85
stddev 0.136 0.077 3.55 3.46 1.45 0.138 0.072 3.15 3.52 1.54 0.08 0.08
median 0.580 0.064 0.00 0.66 1.52 0.566 0.059 0.00 1.10 1.25 0.85 0.86
EF1% EF2% EF5% EF1% EF2% EF5% R2
train R2
Predicted docking scores possess some of the virtual screening power of actual dock-
ing scores. While this is not the intended use of the models (promising molecules should
be docked in the end), it shows that when docking works, the corresponding predictor of
docking scores also works (Figures 2 and 5). Figure 5 clearly shows ROC curves with sim-
ilar trend when comparing the docking curve (in green) to the corresponding regressor (in
blue). Our proposed protocol is to first screen with ligand-based models, because they are
so fast. However, at the end of a structure-based virtual screening pipeline, ligands should
Figure 5: ROC curves for CCDC Gold (green line) or the corresponding regressor (blue line)
on the LIT-PCBA validation set; example legend at bottom right.
be available as protein-ligand complexes, so that other structure-based filtering is possible
(rescoring,62–66 pharmacophore filter,67 molecular dynamics simulation after docking,68 etc.).
Also, experiments show that docking usually has a better very early enrichment (EF1%) than
the corresponding regressor (Table 3).
Docking only 25% of a large chemical library
Table 4: Number of active molecules found among the N top-scoring molecules (LIT-PCBA
validation set; N in {100, 200, 300, ...}). #ligands: number of ligands to dock depending on
the protocol used; #err: number of ligands which failed in docking or LBVS preparation;
lean-docking 25% (lean) or classical docking (docked). Threshold: docking score threshold
used by lean-docking. N/A appears when there are not enough molecules to compare sets of
equal sizes. Tani: Tanimoto score between the sets of actives retrieved by the two methods
(NaN means “Not a Number” / impossible to compute).
target method #ligands #err threshold 100 200 300 500 1000 2000 3000 5000
ADRB2 lean 19683 161 -80.83 0 0 0 0 0 0 0 0
docked 77963 0 NaN 0 NaN 0 NaN 0 NaN 0 NaN 0 NaN 0 NaN 0 NaN
ALDH1 lean 6951 96 -80.29 8 18 21 42 86 167 223 352
docked 27116 8 1.00 18 1.00 21 1.00 42 1.00 86 1.00 167 1.00 222 0.96 345 0.91
ESR1+ lean 337 33 -64.77 0 1 1 N/A N/A N/A N/A N/A
docked 1365 0 NaN 1 1.00 1 1.00 N/A NaN N/A NaN N/A NaN N/A NaN N/A NaN
ESR1- lean 345 32 -65.03 1 3 7 N/A N/A N/A N/A N/A
docked 1230 1 1.00 3 1.00 7 1.00 N/A NaN N/A NaN N/A NaN N/A NaN N/A NaN
FEN1 lean 20879 2547 -53.55 0 0 0 0 0 0 4 7
docked 86395 0 NaN 0 NaN 0 NaN 0 NaN 0 NaN 0 NaN 3 0.75 7 1.00
GBA lean 18498 153 -63.72 2 2 2 3 5 6 6 8
docked 73901 2 1.00 2 1.00 2 1.00 3 1.00 5 1.00 6 1.00 6 1.00 8 1.00
IDH1 lean 23039 358 -79.71 0 0 0 0 2 2 2 3
docked 90163 0 NaN 0 NaN 0 NaN 0 NaN 2 1.00 2 1.00 2 1.00 2 0.67
KAT2A lean 20341 215 -71.14 0 1 1 1 1 2 3 4
docked 86970 0 NaN 1 1.00 1 1.00 1 1.00 1 1.00 2 1.00 3 1.00 4 1.00
MAPK1 lean 3760 72 -72.66 0 1 1 4 8 20 24 N/A
docked 15662 0 NaN 1 1.00 1 1.00 4 1.00 9 0.89 19 0.86 23 0.88 N/A NaN
MTORC1 lean 1979 0-91.96 0 0 0 0 1 N/A N/A N/A
docked 8267 0 NaN 0 NaN 0 NaN 0 NaN 1 1.00 N/A NaN N/A NaN N/A NaN
OPRK1 lean 14704 81 -71.37 0 0 0 0 0 0 2 3
docked 67379 0 NaN 0 NaN 0 NaN 0 NaN 0 NaN 0 NaN 0 0.00 2 0.67
PKM2 lean 15544 77 -93.44 1 1 1 3 4 4 8 13
docked 61439 1 1.00 1 1.00 1 1.00 3 1.00 4 1.00 4 1.00 8 1.00 14 0.93
PPARG lean 314 32 -65.53 2 2 3 N/A N/A N/A N/A N/A
docked 1276 2 1.00 2 1.00 3 1.00 N/A NaN N/A NaN N/A NaN N/A NaN N/A NaN
TP53 lean 299 18 -64.27 8 9 N/A N/A N/A N/A N/A N/A
docked 1043 8 1.00 9 1.00 N/A NaN N/A NaN N/A NaN N/A NaN N/A NaN N/A NaN
VDR lean 15463 154 -81.55 0 0 0 0 0 1 1 2
docked 66646 0 NaN 0 NaN 0 NaN 0 NaN 0 NaN 1 1.00 1 1.00 3 0.67
10809.1 268.6 -73.3 1.5 1.0 2.5 1.0 2.6 1.0 4.8 1.0 9.7 1.0 20.2 1.0 27.3 0.8 43.6 0.9
stddev 8645.9 615.5 10.8 2.7 0.0 4.7 0.0 5.4 0.0 11.8 0.0 24.2 0.0 49.3 0.0 65.6 0.3 109.1 0.1
median 14704.0 81.0 -71.4 0.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 2.0 1.0 3.5 1.0 4.0 0.9
44454.3 268.6 N/A 1.5 1.0 2.5 1.0 2.6 1.0 4.8 1.0 9.8 1.0 20.1 1.0 26.8 0.8 42.8 0.9
stddev 35464.0 615.5 N/A 2.7 0.0 4.7 0.0 5.4 0.0 11.8 0.0 24.2 0.0 49.3 0.0 65.4 0.3 106.9 0.1
median 61439.0 81.0 N/A 0.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 1.0 2.0 1.0 3.0 1.0 4.0 0.9
Tani100 Tani200 Tani300 Tani500 Tani1000 Tani2000 Tani3000 Tani5000
In Table 4, the effect of using the proposed protocol to dock only 25% of a chemical library
is shown. A docking score threshold was extracted from the first quartile of the regressor
training set (a maximum of 10,000 randomly-chosen molecules with docking scores). In
lean-docking, while all molecules have their docking scores predicted by the ligand-based
regressor, only molecules with a predicted docking score less than the threshold will be
docked. The number of active molecules among the N top-scoring molecules was counted (N
in {100, 200, 300, ...}). While lean-docking allowed to dock about four times less molecules
compared to classical docking, there was no significant impact on the number of true actives
among top-scoring docked molecules.
The AVE-unbiased LIT-PCBA dataset. The docking campaign on this dataset unveiled
that it is a very hard dataset for docking. However, the authors of LIT-PCBA also noticed40
that their docking campaign only had a median EF1% >2on five targets (ADRB2, FEN1,
GBA, OPRK1, PPARG), out of the final 15 protein targets. Another drawback of this
dataset is that several protein targets have less than 10 actives in the validation set (ADRB2:
4, ESR1+: 3, IDH1: 9, OPRK1: 6, PPARG: 6); which creates awkward ROC curves (cf.
those five targets in Figure 5). Also, most targets have several PDBs (Table 2), which
make an exhaustive docking campaign (as was undertaken here) very costly computationally.
For CCDC Gold, more than 15 106ligands have been docked in total. This required an
approximate 40,000 hours of supercomputer time, and approximately three times more for
Advantages. Predicting docking scores using a trained regressor is significantly faster
than a real docking screen. We estimate the raw scoring speed to be about 58,000 times
faster than CCDC Gold. Lean-docking allows to screen virtual chemical libraries whose size
is far beyond the computational power or storage capacity of a standard docking user. In our
experiments, one only needs to dock a rather small set of 10,000 ligands on a given target
binding-site in order to train a regressor. We did not observe a significant improvement of the
regressor’s R2performance if 20,000 docked ligands were used (Figure 4). Using a docking
score threshold, this regressor can be used to select a smaller portion of the database for
actual docking. Compared to recent methods using deep-learning to accelerate docking,24,33
lean-docking requires a rather small training set and a regressor can be optimized and trained
in less than two minutes on a single core of our test computer. Hence, lean-docking should
be particularly applicable in situations where users only have access to limited computing
Drawbacks. The method we propose does not predict the binding mode of a ligand;
only a docking score. See Chupakhin et al.35 or Jastrzębski et al. 33 for methods that predict
a binding mode. In our experience, not all docking programs and protein binding sites are
amenable to regression modeling. For example, we tried but failed for OpenEye FRED (Supporting Information Figure S3). On all but one protein target (MTORC1, 10
folds cross validation training set R2=0.17 only), the best regressor has a negative R2. The
Chemgauss4 scoring function of FRED depends heavily on 3D information.69,70 This score
might be impossible to predict by a regressor and from a molecular graph alone. FRED’s
scores might be predictable for a given binding site from a ligand 3D conformer (associated
with a 3D-sensitive molecular encoding). However, having to prepare conformers of the huge
library one wants to screen would lessen the practicality of lean-docking. For MOE-Dock
(Chemical Computing Group), although we have only partial data (Supporting Information
Figure S4), very good regressors were obtained for five out of six targets (ESR1-, MAPK1,
MTORC1, PPARG and TP3) and an acceptable regressor for ESR1+. For Schrodinger Glide
Release 2018.3,45 we also have partial data (Supporting Information Figure S5). On four
(ESR1+, ESR1-, PPARG, TP53) of the six targets analyzed, it seems that it is possible to get
an acceptable regressor. Although we were able to see some trend for MAPK1 and MTORC1
trend, adequate regressors were not obtained probably due to our partial docking protocol
(using the highest resolution PDB only). MAPK1 is a challenging target for docking due to
the activation loop dynamics and the selection of a suitable receptor conformation is required
owing to the conformation selective nature of its inhibitors. MTORC1 might be difficult
under the partial docking protocol due to its large binding pocket. In an unrelated project,
it was found that CCDC Gold has a tendency to give very low docking scores to molecules
which are rather big, highly flexible and hydrophobic. Unfortunately, a regressor can capture
this tendency. Since finding such molecules is not the goal of a docking campaign, it was
found that filtering out PAINS71 compounds from the database that was screened removed
most of those unwanted molecules.
-100 -90 -80 -70 -60 -50 -40 -30 -20 -10
Target protein: GBA
1 2 3
Number of molecules
Gold docking score
Classical docking
Figure 6: Distribution of docking scores for docking versus lean-docking 25% on the GBA
target (validation set). Green arrows: actives docked upon lean-docking. Blue arrows:
actives whose docking was skipped because of lean-docking.
Is it possible to accelerate more than four times? The previously mentioned
predicted docking score threshold must not be set too low, unless the user knows the docking
scores of several known inhibitors of the protein target, under a given docking protocol. In
the experiments on the whole LIT-PCBA dataset, the first quartile docking score was used to
divide by four the required docking power for a given chemical library; a rather conservative
choice. Even in a case where docking works, 25% can skip the docking of some known actives
(Figure 6). However, for a protein target where docking works well, and where there are
several known actives, it is possible to accelerate much more. For example, on the ALDH1
target, docking can be accelerated up to 41 times, using a threshold docking score of -94.74
(computed on the regressor’s left-out training set). In this specific case, also with no loss in
terms of active molecules found among the top-scoring molecules upon docking the validation
In this study, a massive docking campaign was undertaken on the LIT-PCBA dataset, using
several docking software. Regressors could be trained to predict docking scores from 2D
ligands, on most protein targets and with good accuracy (average training set 10 folds cross
validation R2= 0.851; validation set R2= 0.852). We also show a use case of such predicted
docking scores, to significantly reduce the docking power required to screen a large chemical
library, without any significant impact on the virtual screening power (in terms of number
of actives among top-scoring docked molecules).
The authors are interested to know if users of the protocol encounter docking programs
for which it seems a regressor cannot be trained for a given protein target, or confirmation of
the opposite event. The authors are especially interested in open-source docking programs
or with a free license for academia. In our hands, CCDC Gold, Autodock-Vina (Supporting
Information Figure S6) and MOE-Dock (Supporting Information Figure S4) were amenable
to regression modeling, on several protein targets.
There might be other creative use-cases for accurate but fast prediction of docking scores
from 2D ligands. For example, to help in the computational design of ligands with intended
polypharmacology, or to obtain a cheap docking oracle.72 However, the biggest step forward
would be to improve the virtual screening power of docking scores.
AM1BCC: AM1 semi-empirical quantum mechanical wave-function with Bond-Charge Cor-
rections; AMBER: molecular dynamics package; AUC: Area Under the receiver operating
characteristic Curve; BEDROC: Boltzmann-enhanced receiver operating characteristic curve
(early recovery biased ROC curve); AVE: Asymmetric Validation Embedding;41 CCDC:
Cambridge Crystallographic Data Centre; ChEMBL: Chemistry database of the European
Molecular Biology Laboratory; CPU: Central Processing Unit; DUD-E: Directory of Useful
Decoys, Enhanced;23 DD: Deep Docking;24 EF: Enrichment Factor; ff14SB: a force field;50
LIT-PCBA: Laboratoire d’Innovation Thérapeutique - PubChem Assays dataset;40 MOE:
Molecular Operating Environment; PAINS: Pan Assay Interference Compounds; PDB: Pro-
tein Data Bank; PDBQT: file format for docking software (atomic coordinates, partial
charges and atom types); ROC: Receiver Operating Characteristic; R2: Coefficient of De-
termination; SMINA: docking software39 fork of Autodock-Vina; SVR: Support Vector Re-
gression; TR2: Test R2; UCSF: University of California San-Francisco; VR2: Validation R2;
ZINC: ZINC Is Not Commercial (free online database of commercially-available compounds
for virtual screening).
Data and Software Availability
Dataset (SMILES, molecular fingerprints and docking scores):
(accessed 2021-03-08). Molecular standardizer: (ac-
cessed 2019-06-01; pip package chemo-standardizer). Molecular encoder:
UnixJunkie/molenc (accessed 2021-01-25). Software to train regressors to predict docking
scores: (accessed 2021-01-25).
Supporting Information
Supporting Information Available: Density scatter plots for Gold regressors (Figure S1),
recovery plots for Gold (Figure S2), regression plot for FRED (Figure S3), regression plots for
MOE-Dock (Figure S4), regression plots for Glide (Figure S5), regression plots for Autodock-
Vina (Figure S6).
This work was supported by JST AIP-PRISM [grant number JPMJCR18Y5] and JSPS
KAKENHI [grant numbers 18H03334 and 18H02395]. We acknowledge RIKEN ACCC for
computing resources on the Hokusai BigWaterfall supercomputer. FB acknowledges the use
of ChemAxon JChem 20.13 (accessed 2020-11-25). We thank Dr.
Andrew Robertson from Kyushu university for improving the abstract.
Competing interests
The authors declare no competing financial interest.
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Graphical TOC Entry
-100 -90 -80 -70 -60 -50 -40 -30 -20 -10
Target protein: GBA
1 2 3
Number of molecules
Gold docking score
Classical docking
Distribution of docking scores for classical dock-
ing versus lean-docking 25%. The top three low
scoring active molecules are shown on the left.
Green arrows: actives docked upon lean-docking.
Blue arrows: remaining actives.
... Lean docking uses a regressor trained on 10 k docked ligands 30 . Validation on the LIT PCBA 31 dataset have shown that lean docking can accelerate screening between 4 to 41 times (depending on docking screening performance on a given protein target) without loss of top-scoring true actives. ...
Full-text available
Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.
... Gasteiger partial charges were augmented using ligand atoms. On the target proteins, calculations for docking were performed [13]. ...
E. coli is one of the most important organisms that cause urinary tract infection (UTI) in more than 95% of patients with UTI. The aim of this study was to search for inhibitors of (fimH) by a docking method using computer programs and websites specialized for this purpose. Methods. This study involved 63 samples with positive E. coli collected from patients with UTI from February 2021 to October 2021 at the Iraqi hospital in Karbala. Full laboratory investigation for E. coli was made to detect FimH and predictsuitable inhibitors. The Fast Identification System VITEK-2, compact DNA extraction system, and PCR Molecular docking were used. Studies of FimH inhibitor for animals were performed as well. Results. FimH was found in most E. coli isolates, namely in 61 (96.82%) of 63 samples. The principle of the experiment is dependent on activated infection on animals with/without feeding with our drug (chamomile), and then the counted E. coli in their urine chamomile appears to be a good FimH inhibitor, with a docking score of -9.4, and to be able to reduce UTI in roughly 50 percent of rats examined. Conclusions. The chamomile was predicted as a suitable inhibitor of (fi mH) and then tested on rats. The results showed its good inhibitory properties.
... [9] Based on the lock-key model, molecular docking is a method for virtual screening drug targets and predicting active components by simulating the interaction between ligands and receptors and predicting the binding mode and intensity of ligands and receptors based on molecular principles. [10] The purpose of this study is to explore the potential mechanisms of Duhuo Jisheng decoction and provide a reference for further research and clinical application of the remedy. ...
As a classic remedy for treating Osteoarthritis (OA), Duhuo Jisheng decoction has successfully treated countless patients. Nevertheless, its specific mechanism is unknown. This study explored the active constituents of Duhuo Jisheng decoction and the potential molecular mechanisms for treating OA using a Network Pharmacology approaches. Screening active components and corresponding targets of Duhuo parasite decoction by traditional Chinese medicine systems pharmacology database and analysis platform database. Combining the following databases yielded OA disease targets: GeneCards, DrugBank, PharmGkb, Online Mendelian Inheritance in Man, and therapeutic target database. The interaction analysis of the herb-active ingredient-core target network and protein–protein interaction protein network was constructed by STRING platform and Cytoscape software. Gene ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were carried out. PyMOL and other software were used to verify the molecular docking between the essential active components and the core target. 262 active ingredients were screened, and their main components were quercetin, kaempferol, wogonin, baicalein, and beta-carotene. 108 intersection targets of disease and drug were identified, and their main components were RELA, FOS, STAT3, MAPK14, MAPK1, JUN, and ESR1. Gene ontology analysis showed that the key targets were mainly involved in biological processes such as response to lipopolysaccharide, response to xenobiotic stimulus, and response to nutrient levels. The results of Kyoto Encyclopedia of Genes and Genomes analysis show that the signal pathways include the AGE − RAGE signaling pathway, IL − 17 signaling pathway, TNF signaling pathway, and Toll − like receptor signaling pathway. Molecular docking showed that the main active components of Duhuo parasitic decoction had a good bonding activity with the key targets in treating OA. Duhuo Jisheng decoction can reduce the immune-inflammatory reaction, inhibit apoptosis of chondrocytes, strengthen proliferation and repair of chondrocytes and reduce the inflammatory response in a multi-component-multi-target-multi-pathway way to play a role in the treatment of OA.
... In structure-based virtual screening, molecular docking programs are often used to explore potential conformations of ligands upon binding [12][13][14][15][16][17][18][19]. Molecular docking consists of two basic steps: conformation sampling and scoring [20][21][22][23][24][25]. ...
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The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein–ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein–ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository
... 3−5 They predict the binding pose and affinity of the protein-ligand complex and are designed to be computationally efficient for screening of large compound libraries. 6,7 The affinity score of each ligand can rank-order a library of compounds to predict in silico which ones are most likely to bind to the target. Virtual screening methods can reduce the number of in vitro experiments needed to identify lead targets for drug discovery projects. ...
Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools suffer from inaccurate scoring functions with widely varying performance on different proteins. To enable more accurate ranking of active over inactive ligands in virtual screening, we created a machine learning consensus docking tool, MILCDock, that uses predictions from five traditional molecular docking tools to predict the probability a ligand binds to a protein. MILCDock was trained and tested on data from both the DUD-E and LIT-PCBA docking datasets and shows improved performance over traditional molecular docking tools and other consensus docking methods on the DUD-E dataset. LIT-PCBA targets proved to be difficult for all methods tested. We also find that DUD-E data, although biased, can be effective in training machine learning tools if care is taken to avoid DUD-E's biases during training.
... The hydrogen atoms were then added to the KEAP1 protein structure to prepare the structure for docking experiments, along with removal of water from the protein structure. DS 2.5 was then used to define the active site of the KEAP1 crystal structure and calculate the docking site, which is covered with a grid box sized 18.6 Å × 18.6 Å × 18.6 Å. Quercetin was set as the ligand for KEAP1 protein, and the molecular docking was performed at different binding ratios using the lib-Dock method [32]. The docking energies were obtained to evaluate the docking affinity of quercetin to KEAP1. ...
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Natural antioxidants represented by quercetin have been documented to be effective against atherosclerosis. However, the related mechanisms remain largely unclear. In this study, we identified a novel anti-atherosclerotic mechanism of quercetin inhibiting macrophage pyroptosis by activating NRF2 through binding to the Arg483 site of KEAP1 competitively. In ApoE−/− mice fed with high fat diet, quercetin administration attenuated atherosclerosis progression by reducing oxidative stress level and suppressing macrophage pyroptosis. At the cellular level, quercetin suppressed THP-1 macrophage pyroptosis induced by ox-LDL, demonstrated by inhibiting NLRP3 inflammasome activation and reducing ROS level, while these effects were reversed by the specific NRF2 inhibitor (ML385). Mechanistically, quercetin promoted NRF2 to dissociate from KEAP1, enhanced NRF2 nuclear translocation as well as transcription of downstream antioxidant protein. Molecular docking results suggested that quercetin could bind with KEAP1 at Arg415 and Arg483. In order to verify the binding sites, KEAP1 mutated at Arg415 and Arg483 to Ser (R415S and R483S) was transfected into THP-1 macrophages, and the anti-pyroptotic effect of quercetin was abrogated by Arg483 mutation, but not Arg415 mutation. Furthermore, after administration of adeno associated viral vector (AAV) with AAV-KEAP1-R483S, the anti-atherosclerotic effects of quercetin were almost abolished in ApoE−/− mice. These findings proved quercetins suppressed macrophage pyroptosis by targeting KEAP1/NRF2 interaction, and provided reliable data on the underlying mechanism of natural antioxidants to protect against atherosclerosis.
Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.
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Drug design is a crucial step in the drug discovery cycle. Recently, various deep learning-based methods design drugs by generating novel molecules from scratch, avoiding traversing large-scale drug libraries. However, they depend on scarce experimental data or time-consuming docking simulation, leading to overfitting issues with limited training data and slow generation speed. In this study, we propose the zero-shot drug design method DESERT (Drug dEsign by SkEtching and geneRaTing). Specifically, DESERT splits the design process into two stages: sketching and generating, and bridges them with the molecular shape. The two-stage fashion enables our method to utilize the large-scale molecular database to reduce the need for experimental data and docking simulation. Experiments show that DESERT achieves a new state-of-the-art at a fast speed. 1
Molecular recognition is part of several chemical‐biological processes, and is the interaction between macromolecules (such as proteins and ligands) through noncovalent bonds. This phenomenon has been extensively studied for developing new drugs. Molecular modeling is an affordable method (compared with laboratory experiments) for predicting which macromolecules may interact and, through molecular docking, which will form a stable complex. Molecular docking has two main components: (1) search algorithm and (2) scoring function. The search algorithm studies the conformational space of the ligand at the binding site. The scoring function is a mathematical model that evaluates the interaction energy of each complex, and it could be empirical by using databases of ligand‐protein complexes. Results of the search algorithm are satisfactory compared with experimental data, but the scoring function still must improve its performance. Due to the complexity of analysis and management of databases, accurate predictions are difficult to obtain. Machine learning can contribute to achieve better results for predicting macromolecular interactions. Computational predictions of the interaction between macromolecules complexes enhance the development of applied technology in medicine.
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Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule “from bench to a bedside”. While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols. In order to address this challenge, we herein introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking billions of molecular structures in a rapid, yet accurate fashion. The DD approach utilizes quantitative structure–activity relationship (QSAR) deep models trained on docking scores of subsets of a chemical library to approximate the docking outcome for yet unprocessed entries and, therefore, to remove unfavorable molecules in an iterative manner. The use of DD methodology in conjunction with the FRED docking program allowed rapid and accurate calculation of docking scores for 1.36 billion molecules from the ZINC15 library against 12 prominent target proteins and demonstrated up to 100-fold data reduction and 6000-fold enrichment of high scoring molecules (without notable loss of favorably docked entities). The DD protocol can readily be used in conjunction with any docking program and was made publicly available.
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On average, an approved drug today costs $2-3 billion and takes over ten years to develop1. In part, this is due to expensive and time-consuming wet-lab experiments, poor initial hit compounds, and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening (SBVS) has the potential to mitigate these problems. With SBVS, the quality of the hits improves with the number of compounds screened2. However, despite the fact that large compound databases exist, the ability to carry out large-scale SBVSs on computer clusters in an accessible, efficient, and flexible manner has remained elusive. Here we designed VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large ligand libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we have prepared the largest and freely available ready-to-dock ligand library available, with over 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened over 1 billion compounds and discovered a small molecule inhibitor (iKeap1) that engages KEAP1 with nanomolar affinity (Kd = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. We also identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify binders with high affinity for target proteins.
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Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.
Ligand-based drug design has recently benefited from the development of deep generative models. These models enable extensive explorations of the chemical space and provide a platform for molecular optimization. However, the vast majority of current methods does not leverage the structure of the binding target, which potentiates the binding of small molecules and plays a key role in the interaction. We propose an optimization pipeline that leverages complementary structure-based and ligand-based methods. Instead of performing docking on a fixed chemical library, we iteratively select promising compounds in the full chemical space using a ligand-centered generative model. Molecular docking is then used as an oracle to guide compound optimization. This allows for iterative generation of compounds that fit the target structure better and better, without prior knowledge about bioactives. For this purpose, we introduce a new graph to Selfies Variational Autoencoder (VAE) which benefits from an 18-fold faster decoding than the graph to graph state of the art, while achieving a similar performance. We then successfully optimize the generation of molecules toward high docking scores, enabling a 10-fold enrichment of high-scoring compounds found with a fixed computational cost.
Docking is one of the most important steps in virtual screening pipelines and it is an established method for examining potential interactions between ligands and receptors. However, this method is computationally expensive and it is often among last steps of the process of compound libraries evaluation. In this work, we investigate the feasibility of an innovative procedure of learning a deep neural network to predict the docking output directly from a two-dimensional compound structure. The developed protocol is orders of magnitude faster than typical docking software and it returns ligand-receptor complexes encoded in the form of the interaction fingerprint. Its speed and efficiency unlocks the application possibilities, such as screening compound libraries of vast size on the basis of contact patterns or docking score (derived on the basis of predicted interaction schemes).. We tested our approach on several G protein-coupled receptor targets and 4 CYP enzymes in retrospective virtual screening experiments and a variant of graph convolutional network appeared to be most effective in emulating docking output. The method can be widely used by the community by using the code available at Supporting Information.
Comparative evaluation of virtual screening methods requires a rigorous benchmarking procedure on diverse, realistic, and unbiased datasets. Recent investigations from numerous research groups unambiguously demonstrate that artificially constructed ligand sets classically used by the community (e.g. DUD, DUD-E, MUV) are unfortunately biased by both obvious and hidden chemical biases, therefore overestimating the true accuracy of virtual screening methods. We herewith present a novel dataset (LIT-PCBA) specifically designed for virtual screening and machine learning. LIT-PCBA relies on 149 dose-response PubChem bioassays that were additionally processed to remove false positives, assay artifacts, and keep active and inactive compounds within similar molecular property ranges. To ascertain that the dataset is suited to both ligand-based and structure-based virtual screening, target sets were restricted to single protein targets for which at least one X-ray structure is available in complex with ligands of the same phenotype (e.g. inhibitor, inverse agonist) as that of the PubChem active compounds. Preliminary virtual screening on the 21 remaining target sets with state-of-the-art orthogonal methods (2D fingerprint similarity, 3D shape similarity, molecular docking) enabled us to select 15 target sets for which at least one of the three screening methods is able to enrich the top 1%-ranked compounds in true actives by at least a factor of two. The corresponding ligand sets (training, validation) were finally unbiased by the recently described asymmetric validation embedding (AVE) procedure to afford the LIT-PCBA dataset, consisting in 15 targets, 7844 confirmed active and 407381 confirmed inactive compounds. The dataset mimics experimental screening decks in terms of hit rate (ratio of active to inactive compounds) and potency distribution. It is available online at for download and for benchmarking novel virtual screening methods, notably those relying on machine learning.
Structure-based virtual screening (SBVS) relies on classical scoring functions that often fail to reliably discriminate binders from non-binders. In this work, we present a high-throughput protein-ligand complex MD simulations that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in DUD-E (directory of useful decoy, enhanced) dataset. MD trajectories are processed by evaluating ligand binding stability using RMSD (root-mean-square deviations). We select 56 protein targets (of 7 different protein classes) and 560 ligands (280 actives, 280 decoys) and show 22% improvement in ROC AUC (area under the curve, receiver operating characteristics curve), from an initial value of 0.68 (AutoDock Vina) to a final value of 0.83. MD simulation demonstrates a robust performance across all 7 different protein classes. In addition, predicted ligand binding modes are moderately refined during MD simulations. These results systematically validate the reliability of physics-based approach to evaluate protein-ligand binding interactions.
Given a particular descriptor/method combination, we find some QSAR datasets are very predictive by random-split cross-validation, while others are not. Recent literature in modelability suggests that the limiting issue for predictivity is in the data, not the QSAR methodology, and the limits are due to activity cliffs. Here we investigate, on in-house data, the relative usefulness of experimental error, distribution of the activities, and activity cliff metrics in determining how predictive a dataset is likely to be. We include unmodified in-house datasets, datasets that should be perfectly predictive based only on the chemical structure, datasets where the distribution of activities is manipulated, and datasets that include a known amount of added noise. We find that activity cliff metrics determine predictivity better than other metrics we investigated, whatever the type of dataset, consistent with the modelability literature. However, such metrics cannot distinguish real activity cliffs from apparent activity cliffs due to uncertainties in the activities. We also show that a number of modern QSAR methods, and some alternative descriptors, are equally bad at predicting the activities of compounds on activity cliffs, consistent with the assumptions behind “modelability.” Finally, we relate time-split predictivity with random-split predictivity.
Chitinases not only play vital roles in human innate immune system but also are essential for the development of pathogenic fungi and pests. Chitinase inhibitors are efficient tools to investigate the elusive role of human chitinases and to control pathogens and pests. Via hierarchical virtual screening, we have discovered a series of chitinase inhibitors with a novel scaffold that have high inhibitory activity and selectivity against human and insect chitinases. The most potent human chitotriosidase inhibitor, compound 40 exhibited a Ki of 49 nM and the most potent inhibitor of the insect pest chitinase OfChi-h, compound 53 exhibited a Ki of 9 nM. The binding of these two most potent inhibitors was confirmed by X-ray crystallography. In a murine model of bleomycin-induced pulmonary fibrosis, compound 40 was found to suppress the chitotriosidase activity by 60%, leading to a significant increase in inflammatory cells and suggesting chitotriosidase played a protective role.
Motivation: Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein-ligand complex, with limited information about the chemical or topological properties of the ligand itself. Results: We demonstrate that the performance of machine learning scoring functions are consistently improved by the inclusion of diverse ligand-based features. For example, a Random Forest (RF) combining the features of RF-Score v3 with RDKit molecular descriptors achieved Pearson correlation coefficients of up to 0.836, 0.780 and 0.821 on the PDBbind 2007, 2013 and 2016 core sets, respectively, compared to 0.790, 0.746 and 0.814 when using the features of RF-Score v3 alone. Excluding proteins and/or ligands that are similar to those in the test sets from the training set has a significant effect on scoring function performance, but does not remove the predictive power of ligand-based features. Furthermore a RF using only ligand-based features is predictive at a level similar to classical scoring functions and it appears to be predicting the mean binding affinity of a ligand for its protein targets. Availability and implementation: Data and code to reproduce all the results are freely available at Supplementary information: Supplementary data are available at Bioinformatics online.