Recipes for the selection of experimental protein conformations for virtual screening

Computational evaluation and benchmark study of 342 crystallographic holo-structures of SARS-CoV-2 Mpro enzyme

The coronavirus disease 19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global health crisis with millions of confirmed cases and related deaths. The main protease (Mpro) of SARS-CoV-2 is crucial for viral replication and presents an attractive target for drug development. Despite the approval of some drugs, the search for effective treatments continues. In this study, we systematically evaluated 342 holo-crystal structures of Mpro to identify optimal conformations for structure-based virtual screening (SBVS). Our analysis revealed limited structural flexibility among the structures. Three docking programs, AutoDock Vina, rDock, and Glide were employed to assess the efficiency of virtual screening, revealing diverse performances across selected Mpro structures. We found that the structures 5RHE, 7DDC, and 7DPU (PDB Ids) consistently displayed the lowest EF, AUC, and BEDROCK scores. Furthermore, these structures demonstrated the worst pose prediction results in all docking programs. Two structural differences contribute to variations in docking performance: the absence of the S1 subsite in 7DDC and 7DPU, and the presence of a subpocket in the S2 subsite of 7DDC, 7DPU, and 5RHE. These findings underscore the importance of selecting appropriate Mpro conformations for SBVS, providing valuable insights for advancing drug discovery efforts.

Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.

Automated benchmarking of combined protein structure and ligand conformation prediction

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.

Equivariant Flexible Modeling of the Protein-Ligand Binding Pose with Geometric Deep Learning

Flexible modeling of the protein-ligand complex structure is a fundamental challenge for in silico drug development. Recent studies have improved commonly used docking tools by incorporating extra-deep learning-based steps. However, such strategies limit their accuracy and efficiency because they retain massive sampling pressure and lack consideration for flexible biomolecular changes. In this study, we propose FlexPose, a geometric graph network capable of direct flexible modeling of complex structures in Euclidean space without the following conventional sampling and scoring strategies. Our model adopts two key designs: scalar-vector dual feature representation and SE(3)-equivariant network, to manage dynamic structural changes, as well as two strategies: conformation-aware pretraining and weakly supervised learning, to boost model generalizability in unseen chemical space. Benefiting from these paradigms, our model dramatically outperforms all tested popular docking tools and recently advanced deep learning methods, especially in tasks involving protein conformation changes. We further investigate the impact of protein and ligand similarity on the model performance with two conformation-aware strategies. Moreover, FlexPose provides an affinity estimation and model confidence for postanalysis.

Discovery of a novel cardiac-specific myosin modulator using artificial intelligence-based virtual screening

Direct modulation of cardiac myosin function has emerged as a therapeutic target for both heart disease and heart failure. However, the development of myosin-based therapeutics has been hampered by the lack of targeted in vitro screening assays. In this study we use Artificial Intelligence-based virtual high throughput screening (vHTS) to identify novel small molecule effectors of human β-cardiac myosin. We test the top scoring compounds from vHTS in biochemical counter-screens and identify a novel chemical scaffold called 'F10' as a cardiac-specific low-micromolar myosin inhibitor. Biochemical and biophysical characterization in both isolated proteins and muscle fibers show that F10 stabilizes both the biochemical (i.e. super-relaxed state) and structural (i.e. interacting heads motif) OFF state of cardiac myosin, and reduces force and left ventricular pressure development in isolated myofilaments and Langendorff-perfused hearts, respectively. F10 is a tunable scaffold for the further development of a novel class of myosin modulators.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τ _b =0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τ _b =0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τ_b = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τ_b = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

An ensemble docking-based virtual screening according to different TRPV1 pore states toward identifying phytochemical activators

Identifying phytochemical activators for TRPV1 using ensemble-based virtual screening, machine learning, and MD simulation.

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

The rapid advances of 3D techniques for the structural determination of proteins and the development of numerous computational methods and strategies have led to identifying highly active compounds in computer drug design. Molecular docking is a method widely used in high-throughput virtual screening campaigns to filter potential ligands targeted to proteins. A great variety of docking programs are currently available, which differ in the algorithms and approaches used to predict the binding mode and the affinity of the ligand. All programs heavily rely on scoring functions to accurately predict ligand binding affinity, and despite differences in performance, none of these docking programs is preferable to the others. To overcome this problem, consensus scoring methods improve the outcome of virtual screening by averaging the rank or score of individual molecules obtained from different docking programs. The successful application of consensus docking in high-throughput virtual screening highlights the need to optimize the predictive power of molecular docking methods.

Actives-Based Receptor Selection Strongly Increases the Success Rate in Structure-Based Drug Design and Leads to Identification of 22 Potent Cancer Inhibitors

Computer-aided drug design, an important component of the early stages of the drug discovery pipeline, routinely identifies large numbers of false positive hits that are subsequently confirmed to be experimentally inactive compounds. We have developed a methodology to improve true positive prediction rates in structure-based drug design and have successfully applied the protocol to twenty target systems and identified the top three performing conformers for each of the targets. Receptor performance was evaluated based on the area under the curve of the receiver operating characteristic curve for two independent sets of known actives. For a subset of five diverse cancer-related disease targets, we validated our approach through experimental testing of the top 50 compounds from a blind screening of a small molecule library containing hundreds of thousands of compounds. Our methods of receptor and compound selection resulted in the identification of 22 novel inhibitors in the low μM-nM range, with the most potent being an EGFR inhibitor with an IC₅₀ value of 7.96 nM. Additionally, for a subset of five independent target systems, we demonstrated the utility of Gaussian accelerated molecular dynamics to thoroughly explore a target system's potential energy surface and generate highly predictive receptor conformations.

Protein-Ligand Docking in the Machine-Learning Era

Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein-ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set.

Development of an Automatic Pipeline for Participation in the CELPP Challenge

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining—whenever possible—empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein–ligand complexes, which will be addressed in future versions of the pipeline.

AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens

Structure-based, virtual High-Throughput Screening (vHTS) methods for predicting ligand activity in drug discovery are important when there are no or relatively few known compounds that interact with a therapeutic target of interest. State-of-the-art computational vHTS necessarily relies on effective methods for pose sampling and docking and generating an accurate affinity score from the docked poses. However, proteins are dynamic; in vivo ligands bind to a conformational ensemble. In silico docking to the single conformation represented by a crystal structure can adversely affect the pose quality. Here, we introduce AtomNet PoseRanker (ANPR), a graph convolutional network trained to identify and rerank crystal-like ligand poses from a sampled ensemble of protein conformations and ligand poses. In contrast to conventional vHTS methods that incorporate receptor flexibility, a deep learning approach can internalize valid cognate and noncognate binding modes corresponding to distinct receptor conformations, thereby learning to infer and account for receptor flexibility even on single conformations. ANPR significantly enriched pose quality in docking to cognate and noncognate receptors of the PDBbind v2019 data set. Improved pose rankings that better represent experimentally observed ligand binding modes improve hit rates in vHTS campaigns and thereby advance computational drug discovery, especially for novel therapeutic targets or novel binding sites.

Dynophore-Based Approach in Virtual Screening: A Case of Human DNA Topoisomerase IIα

In this study, we utilized human DNA topoisomerase IIα as a model target to outline a dynophore-based approach to catalytic inhibitor design. Based on MD simulations of a known catalytic inhibitor and the native ATP ligand analog, AMP-PNP, we derived a joint dynophore model that supplements the static structure-based-pharmacophore information with a dynamic component. Subsequently, derived pharmacophore models were employed in a virtual screening campaign of a library of natural compounds. Experimental evaluation identified flavonoid compounds with promising topoisomerase IIα catalytic inhibition and binding studies confirmed interaction with the ATPase domain. We constructed a binding model through docking and extensively investigated it with molecular dynamics MD simulations, essential dynamics, and MM-GBSA free energy calculations, thus reconnecting the new results to the initial dynophore-based screening model. We not only demonstrate a new design strategy that incorporates a dynamic component of molecular recognition, but also highlight new derivates in the established flavonoid class of topoisomerase II inhibitors.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins.

Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning

One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them. However, there is still no agreement on how to combine the ensemble docking results to obtain the final ligand ranking. A common choice is to use consensus strategies to aggregate the ensemble docking scores, but these strategies exhibit slight improvement regarding the single-structure approach. Here, we claim that using machine learning (ML) methodologies over the ensemble docking results could improve the predictive power of SBVS. To test this hypothesis, four proteins were selected as study cases: CDK2, FXa, EGFR, and HSP90. Protein conformational ensembles were built from crystallographic structures, whereas the evaluated compound library comprised up to three benchmarking data sets (DUD, DEKOIS 2.0, and CSAR-2012) and cocrystallized molecules. Ensemble docking results were processed through 30 repetitions of 4-fold cross-validation to train and validate two ML classifiers: logistic regression and gradient boosting trees. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performance case achieved with single-structure docking. We provide statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Identification of Potential Binders of Mtb Universal Stress Protein (Rv1636) Through an in silico Approach and Insights Into Compound Selection for Experimental Validation

Millions of deaths caused by Mycobacterium tuberculosis (Mtb) are reported worldwide every year. Treatment of tuberculosis (TB) involves the use of multiple antibiotics over a prolonged period. However, the emergence of resistance leading to multidrug-resistant TB (MDR-TB) and extensively drug-resistant TB (XDR-TB) is the most challenging aspect of TB treatment. Therefore, there is a constant need to search for novel therapeutic strategies that could tackle the growing problem of drug resistance. One such strategy could be perturbing the functions of novel targets in Mtb, such as universal stress protein (USP, Rv1636), which binds to cAMP with a higher affinity than ATP. Orthologs of these proteins are conserved in all mycobacteria and act as “sink” for cAMP, facilitating the availability of this second messenger for signaling when required. Here, we have used the cAMP-bound crystal structure of USP from Mycobacterium smegmatis, a closely related homolog of Mtb, to conduct a structure-guided hunt for potential binders of Rv1636, primarily employing molecular docking approach. A library of 1.9 million compounds was subjected to virtual screening to obtain an initial set of ~2,000 hits. An integrative strategy that uses the available experimental data and consensus indications from other computational analyses has been employed to prioritize 22 potential binders of Rv1636 for experimental validations. Binding affinities of a few compounds among the 22 prioritized compounds were tested through microscale thermophoresis assays, and two compounds of natural origin showed promising binding affinities with Rv1636. We believe that this study provides an important initial guidance to medicinal chemists and biochemists to synthesize and test an enriched set of compounds that have the potential to inhibit Mtb USP (Rv1636), thereby aiding the development of novel antitubercular lead candidates.

An Integrative in silico Drug Repurposing Approach for Identification of Potential Inhibitors of SARS-CoV-2 Main Protease

Considering the urgent need for novel therapeutics in ongoing COVID-19 pandemic, drug repurposing approach might offer rapid solutions comparing to de novo drug design. In this study, we designed an integrative in silico drug repurposing approach for rapid selection of potential candidates against SARS-CoV-2 Main Protease (Mpro ). To screen FDA-approved drugs, we implemented structure-based molecular modelling techniques, physiologically-based pharmacokinetic (PBPK) modelling of drugs disposition and data mining analysis of drug-gene-COVID-19 association. Through presented approach, we selected the most promising FDA approved drugs for further COVID-19 drug development campaigns and analysed them in context of available experimental data. To the best of our knowledge, this is unique in silico study which integrates structure-based molecular modeling of Mpro inhibitors with predictions of their tissue disposition, drug-gene-COVID-19 associations and prediction of pleiotropic effects of selected candidates.

EDock-ML: A web server for using ensemble docking with machine learning to aid drug discovery

EDock-ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account for receptor flexibility in molecular docking. Machine learning improves the use of the resulting docking scores to evaluate whether a compound is likely to be useful. EDock-ML takes a bottom-up approach in which machine-learning models are developed one protein at a time to improve predictions for the proteins included in its database. Because the machine-learning models are intended to be used without changing the docking and model parameters with which the models were trained, novice users can use it directly without worrying about what parameters to choose. A user simply submits a compound specified by an ID from the ZINC database (Sterling, T.; Irwin, J. J., J Chem Inf Model 2015, 55[11], 2,324-2,337.) or upload a file prepared by a chemical drawing program and receives an output helping the user decide the likelihood of the compound to be active or inactive for a drug target. EDock-ML can be accessed freely at edock-ml.umsl.edu.

Decision Making in Structure-Based Drug Discovery: Visual Inspection of Docking Results

Molecular docking is a computational method widely used in drug discovery. Due to the inherent inaccuracies of molecular docking, visual inspection of binding modes is a crucial routine in the decision making process of computational medicinal chemists. Despite its apparent importance for medicinal chemistry projects, guidelines for the visual docking pose assessment have been hardly discussed in the literature. Here, we review the medicinal chemistry literature with the aim of identifying consistent principles for visual inspection, highlighting cases of its successful application, and discussing its limitations. In this context, we conducted a survey reaching experts in both academia and the pharmaceutical industry, which also included a challenge to distinguish native from incorrect poses. We were able to collect 93 expert opinions that offer valuable insights into visually supported decision-making processes. This perspective shall motivate discussions among experienced computational medicinal chemists and guide young scientists new to the field to stratify their compounds.

Structure-based virtual screening of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as endocrine disruptors of androgen receptor activity using molecular docking and machine learning

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew's correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

Structure-based drug repositioning over the human TMPRSS2 protease domain: search for chemical probes able to repress SARS-CoV-2 Spike protein cleavages

In December 2019, a new coronavirus was identified in the Hubei province of central china and named SARS-CoV-2. This new virus induces COVID-19, a severe respiratory disease with high death rate. A putative target to interfere with the virus is the host transmembrane serine protease family member II (TMPRSS2). This enzyme is critical for the entry of coronaviruses into human cells by cleaving and activating the spike protein (S) of SARS-CoV-2. Repositioning approved, investigational and experimental drugs on the serine protease domain of TMPRSS2 could thus be valuable. There is no experimental structure for TMPRSS2 but it is possible to develop quality structural models for the serine protease domain using comparative modeling strategies as such domains are highly structurally conserved. Beside the TMPRSS2 catalytic site, we predicted on our structural models a main exosite that could be important for the binding of protein partners and/or substrates. To block the catalytic site or the exosite of TMPRSS2 we used structure-based virtual screening computations and two different collections of approved, investigational and experimental drugs. We propose a list of 156 molecules that could bind to the catalytic site and 100 compounds that may interact with the exosite. These small molecules should now be tested in vitro to gain novel insights over the roles of TMPRSS2 or as starting point for the development of second generation analogs.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

Using machine learning to improve ensemble docking for drug discovery

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activity, researchers have not yet come up with effective ways to use these scores to classify compounds into actives and inactives. This shortcoming has led to the decrease, rather than an increase in the performance of classifying compounds when more structures are added to the ensemble. Previously, we suggested machine learning, implemented in the form of a naïve Bayesian model could alleviate this problem. However, the naïve Bayesian model assumed that the probabilities of observing the docking scores to different structures to be independent. This approximation might prevent it from achieving even higher performance. In the work presented in this paper, we have relaxed this approximation when using several other machine learning methods-k nearest neighbor, logistic regression, support vector machine, and random forest-to improve ensemble docking. We found significant improvement.

Ensemble docking-based virtual screening toward identifying inhibitors against Wee1 kinase

Aim: Wee1 kinase plays a key role in the arrest of G2/M checkpoint that prevents mitotic entry in response to DNA damage. This work is to discover potent Wee1 inhibitors which can be considered valuable. Materials & Methods: Herein, Ensemble docking using multiple crystal structures was considered an effective strategy in the virtual screening. The performance of 17 scoring functions obtained from different docking software was evaluated for molecular docking. Results: Two novel compounds B1 and A2 were identified as Wee1 inhibitors with IC₅₀ values of 10.23 ± 0.505 and 8.72 ± 0.323 μM, respectively. Further cell viability assay demonstrated that the two active compounds exhibited good anticancer activities. Conclusion: This provides a meaningful starting point for further structure optimization to discover more potent Wee1 inhibitors.

Targeting the C-Terminal Domain Small Phosphatase 1

The human C-terminal domain small phosphatase 1 (CTDSP1/SCP1) is a protein phosphatase with a conserved catalytic site of DXDXT/V. CTDSP1’s major activity has been identified as dephosphorylation of the 5th Ser residue of the tandem heptad repeat of the RNA polymerase II C-terminal domain (RNAP II CTD). It is also implicated in various pivotal biological activities, such as acting as a driving factor in repressor element 1 (RE-1)-silencing transcription factor (REST) complex, which silences the neuronal genes in non-neuronal cells, G1/S phase transition, and osteoblast differentiation. Recent findings have denoted that negative regulation of CTDSP1 results in suppression of cancer invasion in neuroglioma cells. Several researchers have focused on the development of regulating materials of CTDSP1, due to the significant roles it has in various biological activities. In this review, we focused on this emerging target and explored the biological significance, challenges, and opportunities in targeting CTDSP1 from a drug designing perspective.

Highly Flexible Ligand Docking: Benchmarking of the DockThor Program on the LEADS-PEP Protein-Peptide Data Set

Protein-peptide interactions play a crucial role in many cellular and biological functions, which justify the increasing interest in the development of peptide-based drugs. However, predicting experimental binding modes and affinities in protein-peptide docking remains a great challenge for most docking programs due to some particularities of this class of ligands, such as the high degree of flexibility. In this paper, we present the performance of the DockThor program on the LEADS-PEP data set, a benchmarking set composed of 53 diverse protein-peptide complexes with peptides ranging from 3 to 12 residues and with up to 51 rotatable bonds. The DockThor performance for pose prediction on redocking studies was compared with some state-of-the-art docking programs that were also evaluated on the LEADS-PEP data set, AutoDock, AutoDock Vina, Surflex, GOLD, Glide, rDock, and DINC, as well as with the task-specific docking protocol HPepDock. Our results indicate that DockThor could dock 40% of the cases with an overall backbone RMSD below 2.5 Å when the top-scored docking pose was considered, exhibiting similar results to Glide and outperforming other protein-ligand docking programs, whereas rDock and HPepDock achieved superior results. Assessing the docking poses closest to the crystal structure (i.e., best-RMSD pose), DockThor achieved a success rate of 60% in pose prediction. Due to the great overall performance of handling peptidic compounds, the DockThor program can be considered as suitable for docking highly flexible and challenging ligands, with up to 40 rotatable bonds. DockThor is freely available as a virtual screening Web server at https://www.dockthor.lncc.br/ .

ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance

Protein flexibility and solvation pose major challenges to docking algorithms and scoring functions. One established strategy for addressing these challenges is to use multiple protein conformations for docking (all-against-all ensemble docking). Recent studies have shown that the performance of ensemble docking can be improved by selecting the most relevant protein structures for docking. In search for a robust approach to protein structure selection, we have come up with an integrated mAchine Learning AnD DockINg approach (ALADDIN). ALADDIN employs a battery of random forest classifiers to select, individually for each compound of interest, from an ensemble of protein structures, the single most suitable protein structure for docking. ALADDIN outperformed the best single-structure docking runs, ensemble docking and a similarity-based docking approach on three out of four investigated targets, with up to 0.15, 0.11 and 0.16 higher area under the receiver operating characteristic curve (AUC) values, respectively. Only in the case of cytochrome P450 3A4, ALADDIN, like any of the other tested approaches, failed to obtain decent performance. ALADDIN can be particularly useful for structure-based virtual screening of malleable proteins, including kinases, some viral enzymes and anti-targets.

Improving ensemble docking for drug discovery by machine learning

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking. However, it is still unclear how best to use the docking scores from multiple structures to classify compounds into actives and inactives. Previous studies have also found that the performance of classification could decrease rather than increase with the number of structures included in the ensemble. Machine learning could help to alleviate these problems.

The compromise of virtual screening and its impact on drug discovery

Introduction: Docking and structure-based virtual screening (VS) have been standard approaches in structure-based design for over two decades. However, our understanding of the limitations, potential, and strength of these techniques has enhanced, raising expectations. Areas covered: Based on a survey of reports in the past five years, we assess whether VS: (1) predicts binding poses in agreement with crystallographic data (when available); (2) is a superior screening tool, as often claimed; (3) is successful in identifying chemical scaffolds that can be starting points for subsequent lead optimization cycles. Data shows that knowledge of the target and its chemotypes in postprocessing lead to viable hits in early drug discovery endeavors. Expert opinion: VS is capable of accurate placements in the pocket for the most part, but does not consistently score screening collections accurately. What matters is capitalization on available resources to get closer to a viable lead or optimizable series. Integration of approaches, subjective hit selection guided by knowledge of the receptor or endogenous ligand, libraries driven by experimental guides, validation studies to identify the best docking/scoring that reproduces experimental findings, constraints regarding receptor-ligand interactions, thoroughly designed methodologies, and predefined cutoff scoring criteria strengthen VS's position in pharmaceutical research.

Exponential consensus ranking improves the outcome in docking and receptor ensemble docking

Consensus-scoring methods are commonly used with molecular docking in virtual screening campaigns to filter potential ligands for a protein target. Traditional consensus methods combine results from different docking programs by averaging the score or rank of each molecule obtained from individual programs. Unfortunately, these methods fail if one of the docking programs has poor performance, which is likely to occur due to training-set dependencies and scoring-function parameterization. In this work, we introduce a novel consensus method that overcomes these limitations. We combine the results from individual docking programs using a sum of exponential distributions as a function of the molecule rank for each program. We test the method over several benchmark systems using individual and ensembles of target structures from diverse protein families with challenging decoy/ligand datasets. The results demonstrate that the novel method outperforms the best traditional consensus strategies over a wide range of systems. Moreover, because the novel method is based on the rank rather than the score, it is independent of the score units, scales and offsets, which can hinder the combination of results from different structures or programs. Our method is simple and robust, providing a theoretical basis not only for molecular docking but also for any consensus strategy in general.

Integrating Molecular Docking and Molecular Dynamics Simulations

Computational methods, applied at the early stages of the drug design process, use current technology to provide valuable insights into the understanding of chemical systems in a virtual manner, complementing experimental analysis. Molecular docking is an in silico method employed to foresee binding modes of small compounds or macromolecules in contact with a receptor and to predict their molecular interactions. Moreover, the methodology opens up the possibility of ranking these compounds according to a hierarchy determined using particular scoring functions. Docking protocols assign many approximations, and most of them lack receptor flexibility. Therefore, the reliability of the resulting protein-ligand complexes is uncertain. The association with the costly but more accurate MD techniques provides significant complementary with docking. MD simulations can be used before docking since a series of "new" and broader protein conformations can be extracted from the processing of the resulting trajectory and employed as targets for docking. They also can be utilized a posteriori to optimize the structures of the final complexes from docking, calculate more detailed interaction energies, and provide information about the ligand binding mechanism. Here, we focus on protocols that offer the docking-MD combination as a logical approach to improving the drug discovery process.

A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor

BACKGROUND

Virtual screening is vital for contemporary drug discovery but striking performance fluctuations are commonly encountered, thus hampering error-free use. Results and Methodology: A conceptual framework is suggested for combining screening algorithms characterized by orthogonality (docking-scoring calculations, 3D shape similarity, 2D fingerprint similarity) into a simple, efficient and expansible python-based consensus ranking scheme. An original experimental dataset is created for comparing individual screening methods versus the novel approach. Its utilization leads to identification and phosphoproteomic evaluation of a cell-active DYRK1α inhibitor.

CONCLUSION

Consensus ranking considerably stabilizes screening performance at reasonable computational cost, whereas individual screens are heavily dependent on calculation settings. Results indicate that the novel approach, currently available as a free online tool, is highly suitable for prospective screening by nonexperts.

Efficiency of Stratification for Ensemble Docking Using Reduced Ensembles

Molecular docking can account for receptor flexibility by combining the docking score over multiple rigid receptor conformations, such as snapshots from a molecular dynamics simulation. Here, we evaluate a number of common snapshot selection strategies using a quality metric from stratified sampling, the efficiency of stratification, which compares the variance of a selection strategy to simple random sampling. We also extend the metric to estimators of exponential averages (which involve an exponential transformation, averaging, and inverse transformation) and minima. For docking sets of over 500 ligands to four different proteins of varying flexibility, we observe that, for estimating ensemble averages and exponential averages, many clustering algorithms have similar performance trends: for a few snapshots (less than 25), medoids are the most efficient, while, for a larger number, optimal (the allocation that minimizes the variance) and proportional (to the size of each cluster) allocation become more efficient. Proportional allocation appears to be the most consistently efficient for estimating minima.

Online structure-based screening of purchasable approved drugs and natural compounds: retrospective examples of drug repositioning on cancer targets

Drug discovery is a long and difficult process that benefits from the integration of virtual screening methods in experimental screening campaigns such as to generate testable hypotheses, accelerate and/or reduce the cost of drug development. Current drug attrition rate is still a major issue in all therapeutic areas and especially in the field of cancer. Drug repositioning as well as the screening of natural compounds constitute promising approaches to accelerate and improve the success rate of drug discovery. We developed three compounds libraries of purchasable compounds: Drugs-lib, FOOD-lib and NP-lib that contain approved drugs, food constituents and natural products, respectively, that are optimized for structure-based virtual screening studies. The three compounds libraries are implemented in the MTiOpenScreen web server that allows users to perform structure-based virtual screening computations on their selected protein targets. The server outputs a list of 1,500 molecules with predicted binding scores that can then be processed further by the users and purchased for experimental validation. To illustrate the potential of our service for drug repositioning endeavours, we selected five recently published drugs that have been repositioned in vitro and/or in vivo on cancer targets. For each drug, we used the MTiOpenScreen service to screen the Drugs-lib collection against the corresponding anti-cancer target and we show that our protocol is able to rank these drugs within the top ranked compounds. This web server should assist the discovery of promising molecules that could benefit patients, with faster development times, and reduced costs and risk.

Selection of protein conformations for structure-based polypharmacology studies

Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design.

Plasticity of the Binding Site of Renin: Optimized Selection of Protein Structures for Ensemble Docking

Protein flexibility poses a major challenge to docking of potential ligands in that the binding site can adopt different shapes. Docking algorithms usually keep the protein rigid and only allow the ligand to be treated as flexible. However, a wrong assessment of the shape of the binding pocket can prevent a ligand from adapting a correct pose. Ensemble docking is a simple yet promising method to solve this problem: Ligands are docked into multiple structures, and the results are subsequently merged. Selection of protein structures is a significant factor for this approach. In this work we perform a comprehensive and comparative study evaluating the impact of structure selection on ensemble docking. We perform ensemble docking with several crystal structures and with structures derived from molecular dynamics simulations of renin, an attractive target for antihypertensive drugs. Here, 500 ns of MD simulations revealed binding site shapes not found in any available crystal structure. We evaluate the importance of structure selection for ensemble docking by comparing binding pose prediction, ability to rank actives above nonactives (screening utility), and scoring accuracy. As a result, for ensemble definition k-means clustering appears to be better suited than hierarchical clustering with average linkage. The best performing ensemble consists of four crystal structures and is able to reproduce the native ligand poses better than any individual crystal structure. Moreover this ensemble outperforms 88% of all individual crystal structures in terms of screening utility as well as scoring accuracy. Similarly, ensembles of MD-derived structures perform on average better than 75% of any individual crystal structure in terms of scoring accuracy at all inspected ensembles sizes.

High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble

Dynamic ensembles hold great promise in advancing RNA-targeted drug discovery. Here we subjected the transactivation response element (TAR) RNA from human immunodeficiency virus type-1 to experimental high-throughput screening against ~100,000 drug-like small molecules. Results were augmented with 170 known TAR-binding molecules and used to generate sublibraries optimized for evaluating enrichment when virtually screening a dynamic ensemble of TAR determined by combining NMR spectroscopy data and molecular dynamics simulations. Ensemble-based virtual screening scores molecules with an area under the receiver operator characteristic curve of ~0.85-0.94 and with ~40-75% of all hits falling within the top 2% of scored molecules. The enrichment decreased significantly for ensembles generated from the same molecular dynamics simulations without input NMR data and for other control ensembles. The results demonstrate that experimentally determined RNA ensembles can significantly enrich libraries with true hits and that the degree of enrichment is dependent on the accuracy of the ensemble.

Dynamic Docking: A Paradigm Shift in Computational Drug Discovery

Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.

ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research

Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

Ligand-biased ensemble receptor docking (LigBEnD): a hybrid ligand/receptor structure-based approach

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, in which a ligand in a wrong pose achieves a better docking score than that of native pose, arise as additional protein conformations are added. In the current study, we developed a new ligand-biased ensemble receptor docking method and composite scoring function which combine the use of ligand-based atomic property field (APF) method with receptor structure-based docking. This method helps us to correctly dock 30 out of 36 ligands presented by the D3R docking challenge. For the six mis-docked ligands, the cognate receptor structures prove to be too different from the 40 available experimental Pocketome conformations used for docking and could be identified only by receptor sampling beyond experimentally explored conformational subspace.

A cross docking pipeline for improving pose prediction and virtual screening performance

Pose prediction and virtual screening performance of a molecular docking method depend on the choice of protein structures used for docking. Multiple structures for a target protein are often used to take into account the receptor flexibility and problems associated with a single receptor structure. However, the use of multiple receptor structures is computationally expensive when docking a large library of small molecules. Here, we propose a new cross-docking pipeline suitable to dock a large library of molecules while taking advantage of multiple target protein structures. Our method involves the selection of a suitable receptor for each ligand in a screening library utilizing ligand 3D shape similarity with crystallographic ligands. We have prospectively evaluated our method in D3R Grand Challenge 2 and demonstrated that our cross-docking pipeline can achieve similar or better performance than using either single or multiple-receptor structures. Moreover, our method displayed not only decent pose prediction performance but also better virtual screening performance over several other methods.

Identification of Novel Non-secosteroidal Vitamin D Receptor Agonists with Potent Cardioprotective Effects and devoid of Hypercalcemia

Vitamin D regulates many biological processes, but its clinical utility is limited by its hypercalcemic effect. Using a virtual screening platform to search novel chemical probes that activate the vitamin D signaling, we report discovery of novel non-steroidal small-molecule compounds that activate the vitamin D receptor (VDR), but are devoid of hypercalcemia. A lead compound (known as VDR 4-1) demonstrated potent transcriptional activities in a VDR reporter gene assay, and significantly ameliorated cardiac hypertrophy in cell culture studies and in animal models. VDR 4-1 also effectively suppressed secondary hyperparathyroidism in 1α-hydroxylase knockout mice. In contrast to 1α,25-dihydroxyvitamin D₃ (1,25-D₃ or calcitriol), a naturally occurring VDR agonist, VDR 4-1 therapy even at high doses did not induce hypercalcemia. These findings were accompanied by a lack of upregulation of calcium transport genes in kidney and in the gut providing a mechanism for the lack of hypercalcemia. Furthermore, VDR 4-1 therapy significantly suppressed cardiac hypertrophy and progression to heart failure in both vitamin D deficient and normal mice without inducing significant hypercalcemia. In conclusion, we have identified a unique VDR agonist compound with beneficial effects in mouse models of hyperparathyroidism and heart failure without inducing significant hypercalcemia.

Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach

DNA methylation is an epigenetic change that results in the addition of a methyl group at the carbon-5 position of cytosine residues. DNA methyltransferase (DNMT) inhibitors can suppress tumour growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. Additionally, the standard drugs for DNMT inhibition are non-selective cytosine analogues with considerable cytotoxic side-effects. In the present study, we have designed a workflow by integrating various ligand-based and structure-based approaches to discover new agents active against DNMT1. We have derived a pharmacophore model with the help of available DNMT1 inhibitors. Utilising this model, we performed the virtual screening of Maybridge chemical library and the identified hits were then subsequently filtered based on the Naïve Bayesian classification model. The molecules that have returned from this classification model were subjected to ensemble based docking. We have selected 10 molecules for the biological assay by inspecting the interactions portrayed by these molecules. Three out of the ten tested compounds have shown DNMT1 inhibitory activity. These compounds were also found to demonstrate potential inhibition of cellular proliferation in human breast cancer MDA-MB-231 cells. In the present study, we have utilized a multi-step virtual screening protocol to identify inhibitors of DNMT1, which offers a starting point to develop more potent DNMT1 inhibitors as anti-cancer agents.

Cosolvent-Based Molecular Dynamics for Ensemble Docking: Practical Method for Generating Druggable Protein Conformations

Protein flexibility is a major hurdle in current structure-based virtual screening (VS). In spite of the recent advances in high-performance computing, protein-ligand docking methods still demand tremendous computational cost to take into account the full degree of protein flexibility. In this context, ensemble docking has proven its utility and efficiency for VS studies, but it still needs a rational and efficient method to select and/or generate multiple protein conformations. Molecular dynamics (MD) simulations are useful to produce distinct protein conformations without abundant experimental structures. In this study, we present a novel strategy that makes use of cosolvent-based molecular dynamics (CMD) simulations for ensemble docking. By mixing small organic molecules into a solvent, CMD can stimulate dynamic protein motions and induce partial conformational changes of binding pocket residues appropriate for the binding of diverse ligands. The present method has been applied to six diverse target proteins and assessed by VS experiments using many actives and decoys of DEKOIS 2.0. The simulation results have revealed that the CMD is beneficial for ensemble docking. Utilizing cosolvent simulation allows the generation of druggable protein conformations, improving the VS performance compared with the use of a single experimental structure or ensemble docking by standard MD with pure water as the solvent.