![Example of BREED from Pierce et al. [78]. Compounds 1 and 2 are HIV protease inhibitors whose binding modes were reported in PDB structures 1HPV and 1HXB. The protein structure backbones were aligned, creating the 3D compound alignment shown. Several bond pairs are aligned within the distance and angle tolerances defined by BREED, and 22 new molecules are generated by swaps on these bonds. Two of the results are compounds 3 and 4, which were tested and were found to have an IC50 of 160 nM and a Ki of 42 nM, respectively.](https://www.researchgate.net/profile/Woody-Sherman/publication/40023295/figure/fig4/AS:394166199701504@1470987953483/Fig-4-Example-of-BREED-from-Pierce-et-al-78-Compounds-1-and-2-are-HIV-protease_Q320.jpg)
![Images from Loving et al. [44] highlighting the importance of protein and ligand preparation prior to docking. Each image shows the receptor residues and the native ligand crystal pose in gray carbons. The lowest-energy pose predicted by Glide XP 5.0 with fragmentspecific settings is shown in green carbons. Hydrogen bonds made by the docked pose are shown with yellow dotted lines.](https://www.researchgate.net/profile/Woody-Sherman/publication/40023295/figure/fig5/AS:394166199701506@1470987953512/Fig-5-Images-from-Loving-et-al-44-highlighting-the-importance-of-protein-and_Q320.jpg)
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Computational Approaches for Fragment-Based and De Novo Design
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Fragment-based and de novo design strategies have been used in drug discovery for years. The methodologies for these strategies are typically discussed separately, yet the applications of these techniques overlap substantially. We present a review of various fragment-based discovery and de novo design protocols with an emphasis on successful applications in real-world drug discovery projects. Furthermore, we illustrate the strengths and weaknesses of the various approaches and discuss how one method can be used to complement another. We also discuss how the incorporation of experimental data as constraints in computational models can produce novel compounds that occupy unique areas in intellectual property (IP) space yet are biased toward the desired chemical property space. Finally, we present recent research results suggesting that computational tools applied to fragment-based discovery and de novo design can have a greater impact on the discovery process when coupled with the right experiments.
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... As for the distribution of the CB 2 receptor in the body, this receptor was found first, as mentioned above, in the immune system (e.g., spleen, tonsils, thymus, B and T lymphocytes, monocytes, and other cells of the lymphoid system [143][144][145]). This is why a priori this receptor was identified as the "peripheral" (or "immune") receptor of the endocannabinoid system [119]. ...
... This is why a priori this receptor was identified as the "peripheral" (or "immune") receptor of the endocannabinoid system [119]. However, further studies identified it in other peripheral tissues (e.g., lung, liver, cardiovascular tissues, gastrointestinal tract, testes, bones [54,[143][144][145]). More recently, the CB 2 receptor has been also found in neural tissues, where it is expressed in a more restricted way than CB 1 , only in a few neuronal subpopulations where it apparently plays an opposite role compared to CB 1 receptors [146,147]. ...
... Computer-based de novo design methods have been explored as an idea to facilitate molecular decisionmaking in medicinal chemistry since the 1990s [132][133][134][135][136][137]. Many de novo design methods either employ a set of explicit chemical transformations (e.g., virtual reaction schemes) [138][139][140][141] o assembly rules (e.g., fragment growing and linking) [142][143][144][145]. Some de novo design approaches are referred to as inverse QSAR, where existing QSAR models are used to identify the descriptor values corresponding to a desired property and leverage this information for molecule generation [146,147]. ...
A still unsolved, although critical, issue in endocannabinoid research is the mechanism by which the lipophilic anandamide (AEA) moves from its site of synthesis, crosses the aqueous milieu, and reaches the different intracellular membrane compartments, where its metabolic and signaling pathways take place. The difficulty of studying intracellular AEA transport and distribution results from the lack of specific probes and techniques to track and visualize this bioactive lipid within the cells. Herein, we describe the use of a biotinylated, non-hydrolyzable derivative of AEA (biotin-AEA, b-AEA) for visualizing the subcellular distribution of this endocannabinoid by means of confocal fluorescence microscopy.Key wordsEndocannabinoidsAnandamideSubcellular distributionGlass coverslipsBiotinylated derivative of anandamideConfocal microscopy
... De novo ligand design can commence as soon as the binding site is located. To achieve automated ligand design in binding sites, there are numerous specialized programmes [41,42]. Combinatorial explosion is an obstacle that is faced by de novo design, and it is essential to significantly reduce the huge search space. ...
... Since they can effectively sample the search space and reduce the size of the search space, combinatorial search methods are seen to be the most effective way to solve the combinatorial problem [43]. Simulated annealing ( [44,45]; genetic algorithms [46][47][48], molecular dynamics [49,50], evolutionary-based [46,51] and particle swarm algorithms [52] are well-known combinatorial techniques that are frequently employed in the development of new drugs [42]. ...
Alzheimer’s disease, amyotrophic lateral sclerosis, Parkinson’s disease, and Huntington’s disease are all examples of neurodegenerative disorders (NDs), which currently do not have a treatment that can reverse their progression. These diseases have an effect on the lives of millions of people all around the world. Researchers from all around the world are facing a tremendous challenge when it comes to the development of medicines that might fulfill this unfulfilled therapeutic requirement. The huge number of possible ligands that need to be examined in biological assays can be reduced by using computer-aided drug design (CADD) methodologies, which in turn reduces the amount of money, time, and effort that is required to generate novel drugs. In this chapter, we provide an introduction to CADD and analyzed the progress that has been achieved in using CADD and other forms of computational studies for NDs. Additionally, we will discuss some of the challenges that have been encountered in this line of research. We offer an up-to-date assessment of prospective therapy targets for a variety of NDs.
... This concept serves as a valuable tool for the strategic optimization of drug candidates' absorption and clearance. Clearance, in medical terms, gauges the efficiency of the kidney in filtering and eliminating toxins (Loving et al. 2010). ...
In this investigation, we undertook a comprehensive Quantitative Structure-Activity Relationship (QSAR) analysis using both modern artificial neural network methods (ANN) and classical multiple linear regression methods (MLR). Our primary focus was on revealing the intricate connection between antidiabetic activity and the molecular structure of thirty-nine imidazolidine-2, 4-dione derivatives. The B3LYP hybrid functional and 6-31G (d) basis set computed electronic properties at the quantum level. Rigorous benchmarking against experimental data validated the reliability of our quantum theory approach. Our statistical model effectively predicted activities closely aligned with experimental antidiabetic activities, quantified by the IC50 values. They also revealed a significant superiority of the artificial neural network architecture (6-4-1) over the multiple linear regression method. This study stands as a meaningful contribution to the field, providing valuable insights for designing new antidiabetic drugs, particularly as potential inhibitors of tyrosine phosphate 1B (PTP1B). The explicit articulation of our primary aim and emphasis on the study’s significance underscore its potential impact on advancing drug development within the realm of antidiabetic therapeutics.
... The method proposed by Devi et al. (2014) used a GA guided by the scalarisation of two objective functions, drug-likeness (by Lipinski's Rule of 5 Lipinski (2004)) and similarity (by Tanimoto similarity Loving et al. (2010)) to a known reference molecule from the e-Drug3D database. The weights of the objectives are set a priori. ...
de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimisation process, dnDD is naturally categorised as a many-objective optimisation problem (ManyOOP), where more than three objectives must be simultaneously optimised. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimisation methodologies. Herein, we cover the application of multi- and many-objective optimisation methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyse how molecular properties used in the optimisation process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimisation for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.
Please cite this article as: Edache EI, Uzairu A, Mamza PA, Shallangwa GA, Ibrahim MT, Evaluation of novel Anti-SARS-CoV-2 compounds by targeting nucleoprotein and envelope protein through homology modeling, docking simulations, ADMET, and molecular dynamic simulations with the MM/GBSA calculation, Intelligent Pharmacy (2024), doi: https://doi.
de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.
Designing and developing new drugs is an expensive and time-consuming process, and there is a need to discover new tools or approaches that can optimize this process. Applied Computer-Aided Drug Design: Models and Methods compiles information about the main advances in computational tools for discovering new drugs in a simple and accessible language for academic students to early career researchers. The book aims to help readers understand how to discover molecules with therapeutic potential by bringing essential information about the subject into one volume.
Key Features
. Presents the concepts and evolution of classical techniques, up to the use of modern methods based on computational chemistry in accessible format.
. Gives a primer on structure- and ligand-based drug design and their predictive capacity to discover new drugs.
. Explains theoretical fundamentals and applications of computer-aided drug design.
. Focuses on a range of applications of the computations tools, such as molecular docking; molecular dynamics simulations; homology modeling, pharmacophore modeling, quantitative structure-activity relationships (QSAR), density functional theory (DFT), fragment-based drug design (FBDD), and free energy perturbation (FEP).
. Includes scientific reference for advanced readers
G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.
Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure–activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.Key wordsEndocannabinoid SystemComputational ChemistryMachine LearningStructure-Based Drug DesignVirtual ScreeningQSARDe Novo Drug Design
The dynamic ligand design (DLD) algorithm, an automated method for the creation of novel ligands, links up small functional groups that have been placed in energetically favorable positions in the binding site of a target molecule. The positions and orientations of the small functional groups can be determined using the multi-copy simultaneous search approach (MCSS) or experimental data. In this work the original DLD methodology is extended by using a modified version of the pseudo-potential energy function. A novel simulated annealing protocol is presented for optimizing the pseudo-potential energy of ligands in the binding site; the protocol is expected to be applicable to other optimization problems. The utility of the method is illustrated by designing an inhibitor for endothiapepsin. The binding affinity of the inhibitor is assessed using a thermodynamic cycle that decomposes the binding free energy into a sum of translational, rotational, configurational, hydrophobic, and electrostatic contributions. The calculations suggest that the designed molecule will bind endothiapepsin with high affinity. Proteins 2000;40:258–289.
Nikolay P Todorov, Principal Research Scientist, De Novo Pharmaceuticals, received an MSc degree in Biotechnology and Bio-organic Chemistry from the University of Sofia, Bulgaria, in 1991 for his studies of the thermodynamics of enzymatically catalyzed peptide synthesis. During the course of the degree he also joined the Shemyakin Institute of Bioorganic Chemistry in Moscow. In 1995 he obtained a PhD degree in Pharmacology from the University of Cambridge, UK, for his work was on the development of computational technology for de novo ligand design for receptors with known crystal structure. Later he joined the TeknoMed project, a collaborative venture between Imperial College, London, Rhône-Poulenc Rorer and the University of Cambridge for the application of emerging technologies to the discovery of new medicines from genomic data. Several patents have been granted for novel ligands designed with the application of the de novo design technology. In April 2000 Dr Todorov joined as a founding member De Novo Pharmaceuticals, a spin-off company from the University of Cambridge supported by a number of venture capital trusts. The research interests of Dr Todorov lie in the application of mathematical and computational methods to biological problems.
Eubacterial tRNA-guanine transglycosylase (TGT) is involved in the hyper-modification of cognate tRNAs leading to the exchange of G34 at the wobble position in the anticodon loop by preQ1 (2-amino-5-(aminomethyl)pyrrolo[2,3-d]pyrimidin-4(3H)-one) as part of the biosynthesis of queuine (Q). Mutation of the tgt gene in Shigella flexneri results in a significant loss of pathogenicity of the bacterium, revealing TGT as a new target for the design of potent drugs against Shigellosis. The X-ray structure of Zymomonas mobilis TGT in complex with preQ1 was used to search for new putative inhibitors with the computer program LUDI. An initial screen of the Available Chemical Directory, a database compiled from commercially available compounds, suggested several hits. Of these, 4-aminophthalhydrazide (APH) showed an inhibition constant in the low micromolar range. The 1.95 Å crystal structure of APH in complex with Z. mobilis TGT served as a starting point for further modification of this initial lead.
Raf kinase, an enzyme which acts downstream in the Ras signaling pathway, is involved in cancerous cell proliferation. Thus, small molecule inhibitors of Raf kinase activity may be important agents for the treatment of cancer. A novel class of Raf-1 inhibitors was discovered, using a combination of medicinal and combinatorial chemistry approaches. This effort culminated in the identification of the clinical candidate BAY 43-9006, currently undergoing Phase I clinical trials. The present review summarizes the medicinal chemistry development of ureas as highly potent inhibitors of Raf-1 kinase.
The thermodynamic integration technique to evaluate free energy differences by molecular dynamics simulations is analyzed. The hydration of the ions Na+ , K+ , Ca++ , F−, Cl−, and Br− is used as the process to illustrate the potential utility of the method. A neon–water system is used as a reference system. The parameters that influence the performance and accuracy of the thermodynamic integration, in which the potential interaction parameters are gradually and continuously changed, are studied. These parameters include the total simulation time, the magnitude of the time step for the numerical integration of the equations of motion, the system size, and the cutoff radii for the intermolecular interactions. Fast convergence is found for the Gibbs free energy difference between Ne and Na+ with respect to total simulation time. The time step and system size are relatively unimportant. The use of cutoff radii, for the ion–water but especially unfortunately also the water–water intermolecular interactions, seriously influences the results obtained. A simple correction for the use of cutoff radii cannot be made. Results are compared to experimental values.
A theoretical study is made of the equations of state of argon and nitrogen, at high temperatures and densities. The intermolecular potential used is of the Lennard‐Jones form, with an adjustable rigid sphere cutoff. A perturbation theory is developed, by which the thermodynamic properties of one system may be related to those of a slightly different system and to the difference in the intermolecular potentials of the two systems. In this article, the unperturbed system is a rigid sphere fluid, and the Lennard‐Jones potential is the perturbation. The results are in fair agreement with experiment, and also lead to an experimental test of the theoretical rigid sphere equation of state.
Noncovalent interactions are important in many physiological processes of complexation which involve all components of the living cells. Here we report an approach to computationally study the interaction free energies in protein−protein complexes which allows from a single simulation an estimate of the individual contribution of each residue to the binding. We developed this new techniquecomputational alanine scanningand applied it to study the interactions of the oncoprotein Mdm2 to the N-terminal stretch of tumor suppressor protein p53. Excellent agreement has been found between the calculated and experimental data. This residue mutation methodology could prove to be a useful general design tool for moleculesnucleotides, peptides, lipids, or any other organic compoundoptimized for interactions or stability, since one can qualitatively estimate the free energy consequences of many mutations from a single molecular dynamics trajectory.
With the use of an NMR-based method, potent (IC50 < 25 nM) nonpeptide inhibitors of the matrix metalloproteinase stromelysin (MMP-3) were discovered. The method, called SAR by NMR (for structure−activity relationships by nuclear magnetic resonance), involves the identification, optimization, and linking of compounds that bind to proximal sites on a protein. Using this technique, two ligands that bind weakly to stromelysin (acetohydroxamic acid, KD = 17 mM; 3-(cyanomethyl)-4‘-hydroxybiphenyl, KD = 0.02 mM) were identified. On the basis of NMR-derived structural information, the two fragments were connected to produce a 15 nM inhibitor of this enzyme. This compound was rapidly discovered (less than 6 months) and required only a minimal amount of chemical synthesis. These studies indicate that the SAR by NMR method can be effectively applied to enzymes to yield potent lead inhibitorsan important part of the drug discovery process.
