Computer-aided Drug Discovery Research Articles

Perspective on computer aided drug discovery for anti-SARS-CoV-2

Objective To explore theresearch progress of the computer aided drug discovery for SARS-CoV-2 drugs. Methods Comprehensively searching internetresour-ces including news report,research papers in scientific journals and academic preprint websites. Results Since the outbreak of COVID-19, researchers in China and all over the world rapidly start to search drugs for anti-SARS-CoV-2and some progresses were made. This paper reviewed the progress of computer aided discovery of SARS-CoV-2 drugs. Conclusions Both docking and bioinformatics based methods had achievements and a number of candidate drugs have been identified, but more novel targets should be considered in the future. Key words: Novel coronavirus; SARS-CoV-2; Drug discovery

Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines

ABSTRACT Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.

Targeting SARS-CoV-2 RNA-dependent RNA polymerase: An in silico drug repurposing for COVID-19.

Background: The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), took more lives than combined epidemics of SARS, MERS, H1N1, and Ebola. Currently, the prevention and control of spread are the goals in COVID-19 management as there are no specific drugs to cure or vaccines available for prevention. Hence, the drug repurposing was explored by many research groups, and many target proteins have been examined. The major protease (M pro), and RNA-dependent RNA polymerase (RdRp) are two target proteins in SARS-CoV-2 that have been validated and extensively studied for drug development in COVID-19. The RdRp shares a high degree of homology between those of two previously known coronaviruses, SARS-CoV and MERS-CoV. Methods: In this study, the FDA approved library of drugs were docked against the active site of RdRp using Schrodinger's computer-aided drug discovery tools for in silico drug-repurposing. Results: We have shortlisted 14 drugs from the Standard Precision docking and interaction-wise study of drug-binding with the active site on the enzyme. These drugs are antibiotics, NSAIDs, hypolipidemic, coagulant, thrombolytic, and anti-allergics. In molecular dynamics simulations, pitavastatin, ridogrel and rosoxacin displayed superior binding with the active site through ARG555 and divalent magnesium. Conclusion: Pitavastatin, ridogrel and rosoxacin can be further optimized in preclinical and clinical studies to determine their possible role in COVID-19 treatment.

A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure-activity relationship modeling.

Over the past decade, rapid development in biological and chemical technologies such as high-throughput screening, parallel synthesis, has been significantly increased the amount of data, which requires the creation and the integration of new analytical methods, especially deep learning models. Recently, there is an increasing interest in deep learning utilization in computer-aided drug discovery due to its exceptional successful application in many fields. The present work proposed a natural language processing approach, based on embedding deep neural networks. Our method aims to transform the Simplified Molecular Input Line Entry System format into word embedding vectors to represent the semantics of compounds. These vectors are fed into supervised machine learning algorithms such as convolutional long short-term memory neural network, support vector machine, and random forest to build up quantitative structure-activity relationship models on toxicity data sets. The obtained results on toxicity data to the ciliate Tetrahymena pyriformis (IGC50 ), and acute toxicity rat data expressed as median lethal dose of treated rats (LD50 ) show that our approach can eventually be used to predict the activities of chemical compounds efficiently. All material used in this study is available online through the GitHub portal (https://github.com/BoukeliaAbdelbasset/NLPDeepQSAR.git).

Insights of structure-based pharmacophore studies and inhibitor design against Gal3 receptor through molecular dynamics simulations

Our present work studies the structure-based pharmacophore modeling and designing inhibitor against Gal3 receptor through molecular dynamics (MD) simulations extensively. Pharmacophore models play a key role in computer-aided drug discovery like in the case of virtual screening of chemical databases, de novo drug design and lead optimization. Structure-based methods for developing pharmacophore models are important, and there have been a number of studies combining such methods with the use of MD simulations to model protein’s flexibility. The two potential antagonists SNAP 37889 and SNAP 398299 were docked and simulated for 250 ns and the results are analyzed and carried for the structure-based pharmacophore studies. This helped in identification of the subtype selectivity of the binding sites of the Gal3 receptor. Our work mainly focuses on identifying these binding site residues and to design more potent inhibitors compared to the previously available inhibitors through pharmacophore models. The study provides crucial insight into the binding site residues Ala2, Asp3, Ala4, Gln5, Phe24, Gln79, Ala80, Ile82, Tyr83, Trp88, His99, Ile102, Tyr103, Met106, Tyr157, Tyr161, Pro174, Trp176, Arg181, Ala183, Leu184, Asp185, Thr188, Trp248, His251, His252, Ile255, Leu256, Phe258, Trp259, Tyr270, Arg273, Leu274 and His277, which plays a significant role in the conformational changes of the receptor and helps to understand the inhibition mechanism. Communicated by Ramaswamy H. Sarma

Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD.

Machine learning is an active area of research in computer science by the availability of big data collection of all sorts prompting interest in the development of novel tools for data mining. Machine learning methods have wide applications in computer-aided drug discovery methods. Most incredible approaches to machine learning are used in drug designing, which further aid the process of biological modelling in drug discovery. Mainly, two main categories are present which are Ligand-Based Virtual Screening (LBVS) and Structure-Based Virtual Screening (SBVS), however, the machine learning approaches fall mostly in the category of LBVS. This study exposits the major machine learning approaches being used in LBVS. Moreover, we have introduced a protocol named FP-CADD which depicts a 4-steps rule of thumb for drug discovery, the four protocols of computer-aided drug discovery (FP-CADD). Various important aspects along with SWOT analysis of FP-CADD are also discussed in this article. By this thorough study, we have observed that in LBVS algorithms, Support Vector Machines (SVM) and Random Forest (RF) are those which are widely used due to high accuracy and efficiency. These virtual screening approaches have the potential to revolutionize the drug designing field. Also, we believe that the process flow presented in this study, named FP-CADD, can streamline the whole process of computer-aided drug discovery. By adopting this rule, the studies related to drug discovery can be made homogeneous and this protocol can also be considered as an evaluation criterion in the peer-review process of research articles.

Drug Discovery Targeting the Disorder-To-Order Transition Regions through the Conformational Diversity Mimicking and Statistical Analysis.

Intrinsically disordered proteins exist as highly dynamic conformational ensembles of diverse forms. However, the majority of virtual screening only focuses on proteins with defined structures. This means that computer-aided drug discovery is restricted. As a breakthrough, understanding the structural characteristics of intrinsically disordered proteins and its application can open the gate for unrestricted drug discovery. First, we segmented the target disorder-to-order transition region into a series of overlapping 20-amino-acid-long peptides. Folding prediction generated diverse conformations of these peptides. Next, we applied molecular docking, new evaluation score function, and statistical analysis. This approach successfully distinguished known compounds and their corresponding binding regions. Especially, Myc proto-oncogene protein (MYC) inhibitor 10058F4 was well distinguished from others of the chemical compound library. We also studied differences between the two Methyl-CpG-binding domain protein 2 (MBD2) inhibitors (ABA (2-amino-N-[[(3S)-2,3-dihydro-1,4-benzodioxin-3-yl]methyl]-acetamide) and APC ((R)-(3-(2-Amino-acetylamino)-pyrrolidine-1-carboxylic acid tert-butyl ester))). Both compounds bind MBD2 through electrostatic interaction behind its p66α-binding site. ABA is also able to bind p66α through electrostatic interaction behind its MBD2-binding site while APC-p66α binding was nonspecific. Therefore, structural heterogeneity mimicking of the disorder-to-order transition region at the peptide level and utilization of the new docking score function represent a useful approach that can efficiently discriminate compounds for expanded virtual screening toward intrinsically disordered proteins.

The Use of Methods of Computer-Aided Drug Discovery in the Development of Topoisomerase II Inhibitors: Applications and Future Directions.

Topoisomerase II (TopoII) is an enzyme essential for cellular metabolism and replication as it regulates DNA topology. Since inhibition of TopoII induces cell death, it is a well-established drug target in cancer therapy; several broadly used anticancer drugs including etoposide and doxorubicin are TopoII inhibitors. However, these therapeutics tend to cause severe side effects and suffer from relatively low ligand affinity, leaving TopoII targeting with small molecules an active area of research. In recent years computer-aided drug discovery (CADD) approaches have been actively used to expand knowledge on the role of TopoII in cancer and to develop novel strategies for its inhibition. Herein, we overview studies that employed structure-based approaches such as docking and molecular dynamic simulations, as well as ligand-based approaches, such as QSAR (quantitative structure-activity relationship) modeling among others, to gain understanding in TopoII targeting with existing drugs and to search for novel drug candidates.

Computer-aided Discovery of a New Nav1.7 Inhibitor for Treatment of Pain and Itch.

Voltage-gated sodium channel Nav1.7 has been validated as a perspective target for selective inhibitors with analgesic and anti-itch activity. The objective of this study was to discover new candidate compounds with Nav1.7 inhibitor properties. The authors hypothesized that their approach would yield at least one new compound that inhibits sodium currents in vitro and exerts analgesic and anti-itch effects in mice. In silico structure-based similarity search of 1.5 million compounds followed by docking to the Nav1.7 voltage sensor of Domain 4 and molecular dynamics simulation was performed. Patch clamp experiments in Nav1.7-expressing human embryonic kidney 293 cells and in mouse and human dorsal root ganglion neurons were conducted to test sodium current inhibition. Formalin-induced inflammatory pain model, paclitaxel-induced neuropathic pain model, histamine-induced itch model, and mouse lymphoma model of chronic itch were used to confirm in vivo activity of the selected compound. After in silico screening, nine compounds were selected for experimental assessment in vitro. Of those, four compounds inhibited sodium currents in Nav1.7-expressing human embryonic kidney 293 cells by 29% or greater (P < 0.05). Compound 9 (3-(1-benzyl-1H-indol-3-yl)-3-(3-phenoxyphenyl)-N-(2-(pyrrolidin-1-yl)ethyl)propanamide, referred to as DA-0218) reduced sodium current by 80% with a 50% inhibition concentration of 0.74 μM (95% CI, 0.35 to 1.56 μM), but had no effects on Nav1.5-expressing human embryonic kidney 293 cells. In mouse and human dorsal root ganglion neurons, DA-0218 reduced sodium currents by 17% (95% CI, 6 to 28%) and 22% (95% CI, 9 to 35%), respectively. The inhibition was greatly potentiated in paclitaxel-treated mouse neurons. Intraperitoneal and intrathecal administration of the compound reduced formalin-induced phase II inflammatory pain behavior in mice by 76% (95% CI, 48 to 100%) and 80% (95% CI, 68 to 92%), respectively. Intrathecal administration of DA-0218 produced acute reduction in paclitaxel-induced mechanical allodynia, and inhibited histamine-induced acute itch and lymphoma-induced chronic itch. This study's computer-aided drug discovery approach yielded a new Nav1.7 inhibitor that shows analgesic and anti-pruritic activity in mouse models.

Relevance of Molecular Docking Studies in Drug Designing

Molecular Docking is used to positioning the computer-generated 3D structure of small ligands into a receptor structure in a variety of orientations, conformations and positions. This method is useful in drug discovery and medicinal chemistry providing insights into molecular recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery (CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment of targets and ligand. Over the last decade, advances in the field of computational, proteomics and genomics have also led to the development of different docking methods which incorporate protein-ligand flexibility and their different binding conformations. Receptor flexibility accounts for more accurate binding pose predictions and a more rational depiction of protein binding interactions with the ligand. Protein flexibility has been included by generating protein ensembles or by dynamic docking methods. Dynamic docking considers solvation, entropic effects and also fully explores the drug-receptor binding and recognition from both energetic and mechanistic point of view. Though in the fast-paced drug discovery program, dynamic docking is computationally expensive but is being progressively used for screening of large compound libraries to identify the potential drugs. In this review, a quick introduction is presented to the available docking methods and their application and limitations in drug discovery.

Discovering Anti-Cancer Drugs via Computational Methods.

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful, and promising technology for faster, cheaper, and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the different subareas of the computer-aided drug discovery process with a focus on anticancer drugs.

Computational pharmacokinetic analysis on some newly designed 2-anilinopyrimidine derivative compounds as anti-triple-negative breast cancer drug compounds

IntroductionWorldwide, cancer of the breast is the most commonly diagnosed disease and the second leading cause of cancer-related mortality amongst women yearly (Miller et al., 2016). Computer-aided drug discovery (CADD) is a fundamental shortcut in drug discovery arena. CADD tools ascertain key molecule for testing, predicting the effectiveness, the possible side effect, and also assist in upgrading drug-likeliness of drug molecules (Leelananda and Lindert, 2016). The propose of carrying out this research is to design new 2-anilinopyrimidine derivative compounds based on the interaction of the derivative compounds (ligand) and thyroid hormone receptor (TRβ1), and also analyze their pharmacokinetic properties as drug compounds that would be used by the pharmaceuticals against triple-negative breast cancer (MDA-MB-468 cell line).ResultsThree compounds (12, 17, and 18) had the highest docking score ranging from − 7.3 to − 7.4 kcal/mol. This showed that the compounds (ligands) bind tightly with the active site of the thyroid hormone receptor (TRβ1). Based on their tight interactions with the receptor, the compounds were chosen as lead compounds in the design of fourteen new compounds by incorporating some fragments found to bind intensely with the active site of the thyroid hormone receptor (TRβ1). All the newly designed compounds passed the pharmacokinetic analysis (adsorption, distribution, metabolism, excretion, and other physicochemical test) passed the drug-likeness test, and they also adhered to the Lipinski rule of five.ConclusionsNew derivative compounds of 2-anilinopyrimidine against MDA-MB-468 cell line were designed based on the information obtained from the molecular docking studies. Furthermore, the pharmacokinetics analysis (adsorption, distribution, metabolism, excretion (ADME) and other physicochemical properties) carried out on the newly designed compounds showed this compounds can be made into oral drugs for patients with triple-negative breast cancer (MBA-MD-468 cell line) as they serve as most promising inhibitors against thyroid hormone receptor (TRβ1).

AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization

We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4.

A comprehensive automated computer-aided discovery pipeline from genomes to hit molecules

Big data generation through sequencing of genomes and proteomes has led to over thousands of whole genomes and millions of protein sequences. However, utilization of this data to generate drug-like molecules for curing diseases remains a challenge. We propose here, Dhanvantari, a comprehensive software suite which automates the journey from genomes to hit molecules via its various modules such as i) gene finding, ii) computational structural study of target proteins and iii) virtual screening/identification of hit molecules for computer aided drug discovery. The pipeline has five possible entry points. Validation of the protocol is performed on 10 major life-threatening diseases reported by WHO covering 33 different protein targets and 111 FDA approved drugs. Three complete case studies from genomes to hits are also performed using the pipeline where the reported FDA drugs against the pathogen have been successfully recovered. The proposed software suite promises to deliver some new opportunities and insights into “genome-based” drug discovery along with the classical structure-based approaches to discover novel potential drug-like molecules. The entire protocol requires ~6–12 h. Individual steps, however, get completed within a few minutes. The pipeline can be freely accessed at http://www.scfbio-iitd.res.in/software/dhanvantari_new/Home.html.

Repurposing Clinical Drugs as AdoMetDC Inhibitors Using the SCAR Strategy.

With the escalating costs in drug development, discovering new uses of approved drugs, i.e., drug repurposing, has attracted increasing interest. Spermidine and spermine are important polyamines for most cells and their biosynthesis are strictly regulated by the polyamine metabolic network. In cancerous cells and tumor environments, the concentrations of polyamines are much higher than in normal cells. During the synthesis of spermidine and spermine, an amino-propyl group is provided by decarboxylated S-adenosylmethionine, and the latter is generated from S-adenosylmethionine by AdoMetDC (AdoMet decarboxylase). Therefore, as a rate-limiting enzyme in the biosynthesis of spermidine and spermine, AdoMetDC has been an attractive drug target in cancer studies. In the last decades, many AdoMetDC inhibitors have been discovered, and several AdoMetDC inhibitors are under clinical trials, but unfortunately, none of them have been approved yet. To overcome the high costs in time and money for discovering de novo inhibitors, we set out to repurpose clinic drugs as AdoMetDC inhibitors. We used steric-clashes alleviating receptors (SCAR), a computer-aided drug discovery strategy developed by us recently for in silico screening. By combining computational screening and experimental validation, we successfully identified two approved drugs that have inhibitory potency on AdoMetDC’s enzymatic activity. SCAR was previously shown to be suitable for the discovery of both covalent and non-covalent inhibitors, and this work further demonstrated the value of the SCAR strategy in drug repurposing.

TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches.

The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer’s disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders.

Exploring the computational methods for protein-ligand binding site prediction

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein–ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein–ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

Antiviral screening of Clerodol derivatives as COV 2 main protease inhibitor in Novel Corona Virus Disease: In silico approaches

Plant based medicines are gaining much importance nowadays because of some drawbacks that are associated with the use of modern medicines. Discovery of new therapeutics is a very challenging, expensive and time consuming process. With the number of approved drugs declining steadily combined with increasing costs, a rational approach is needed to facilitate, expedite and streamline the drug discovery process. In this regard In silico identification for potential hits has become a popular approach in computer-aided drug discovery. Here we consider molecular docking approach in the development of novel antiviral inhibitors for COVID 19. COV- 2 main protease contain a non-structural protein (PDB ID: 6LU7) which serve as the potential targets for the studies. Terpenoids can inhibit the COVID-19 main protease that present in the Corona virus which is an essential viral enzyme. The COVID-19 main protease of the Corona virus species is one of the attractive targets in corona associated diseases. By considering the above observations, an attempt for molecular docking studies of terpenoids present in Clerodendrum paniculatum with COVID-19 main protease (PDB ID: 6LU7) by using in silico studies by molinspiration online tool and evaluated for their antiviral activity. Clerodol is an important triterpenoid present in Clerodendrum paniculatum leaves act as a potential inhibitor. Clerodol showed binding energy-5.74 kcal/mol. Pyrrole derivative of clerodol have highest binding energy-7.4 Kcal/mol. But all derivatives of Clerodol have binding energies more than the standard. The compounds showed good activity against Corona virus comparable to that of standard drug baricitinib. Thus, Clerodendrum paniculatum leaves appears to be an effective material for development of antiviral drugs against corona. So that it may serve as a reference for further studies

Flat or angular? The impact of the nitrogen atom hybridization on the docking results for arylpiperazine derivatives as an example

Structure-based virtual screening has become a standard approach in modern drug discovery. It involves molecular docking and estimation of the ligand binding likelihood based on the scoring function. Surprisingly, in the search for new pharmaceuticals, one of the bottlenecks can be the ligand’s 3D structure prediction, especially for molecules with amine groups. In our work, the impact of the nitrogen atom hybridization in arylpiperazine derivatives on the results of docking to serotonin receptor type 7 is discussed. Our docking/re-docking studies show that the nitrogen lone pair may be involved in weak ligand–protein interactions. The presented results suggest that assumption of amine group planarity in the arylpiperazine 3D structure prediction may be a misleading factor in computer-aided drug discovery, influencing active conformation prediction. With our paper, we would like to raise awareness that in the case of compounds with amine groups, special care must be taken in the 3D ligand’s structure preparation for molecular docking studies.

Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations.

Recent years have seen progress in druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug‐like probe molecules to characterize their drug‐binding abilities, if any. An important consecutive step is to analyze the trajectories to construct pharmacophore models (PMs) to use for virtual screening of libraries of small molecules. While considerable success has been observed in this type of computer‐aided drug discovery, a systematic tool encompassing multiple steps from druggability simulations to pharmacophore modeling, to identifying hits by virtual screening of libraries of compounds, has been lacking. We address this need here by developing a new tool, Pharmmaker, building on the DruGUI module of our ProDy application programming interface. Pharmmaker is composed of a suite of steps: (Step 1) identification of high affinity residues for each probe molecule type; (Step 2) selecting high affinity residues and hot spots in the vicinity of sites identified by DruGUI; (Step 3) ranking of the interactions between high affinity residues and specific probes; (Step 4) obtaining probe binding poses and corresponding protein conformations by collecting top‐ranked snapshots; and (Step 5) using those snapshots for constructing PMs. The PMs are then used as filters for identifying hits in structure‐based virtual screening. Pharmmaker, accessible online at http://prody.csb.pitt.edu/pharmmaker/, can be used in conjunction with other tools available in ProDy.