Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.

The Protein Data Bank

Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196

UniProt: the Universal Protein knowledgebase

Apweiler, R. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh131 · Cited by 7,593

ChEMBL: a large-scale bioactivity database for drug discovery

Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382

Docking and scoring in virtual screening for drug discovery: methods and applications

Kitchen, D. B., Decornez, H. Y., Furr, J. R., & Bajorath, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1549 · Cited by 4,354

ZINC − A Free Database of Commercially Available Compounds for Virtual Screening

Irwin, J. J., & Shoichet, B. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049714+ · Cited by 4,058

The Properties of Known Drugs. 1. Molecular Frameworks

Bemis, G. W., & Murcko, M. A. (1996) · Journal of Medicinal Chemistry · doi:10.1021/jm9602928 · Cited by 2,501

Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

Hawkins, P. C. D., Skillman, A. G., Warren, G. L., Ellingson, B. A., & Stahl, M. T. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100031x · Cited by 1,666

Benchmarking sets for molecular docking.

Huang, N., Shoichet, B. K., & Irwin, J. J. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0608356 · Cited by 1,318

Comparison of Shape-Matching and Docking as Virtual Screening Tools

Hawkins, P. C. D., Skillman, A. G., & Nicholls, A. O. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm0603365 · Cited by 1,023

The maximal affinity of ligands

Kuntz, I. D., Chen, K., Sharp, K. A., & Kollman, P. A. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.9997 · Cited by 859

Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand

DeLuca, S., Khar, K. R., & Meiler, J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0132508 · Cited by 150

A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification

Ryu, S., Kwon, Y., & Kim, W. Y. (2019) · Chemical Science · doi:10.1039/c9sc01992h · Cited by 152

Insights into the Structure, Function, and Ligand Discovery of the Large Neutral Amino Acid Transporter 1, LAT1

Singh, N., & Ecker, G. F. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19051278 · Cited by 145

Beware of machine learning-based scoring functions-on the danger of developing black boxes.

Gabel, J., Desaphy, J., & Rognan, D. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500406k · Cited by 143

Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Kim, J., Park, S., Min, D., & Kim, W. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22189983 · Cited by 147

Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

Vieira, T. F., & Sousa, S. F. (2019) · Applied Sciences · doi:10.3390/app9214538 · Cited by 150

BioLiP2: an updated structure database for biologically relevant ligand–protein interactions

Zhang, C., Zhang, X., Freddolino, L., & Zhang, Y. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad630 · Cited by 152

Comprehensive prediction of drug-protein interactions and side effects for the human proteome

Zhou, H., Gao, M., & Skolnick, J. (2015) · Scientific Reports · doi:10.1038/srep11090 · Cited by 138

DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design

García-Ortegón, M., Simm, G. N. C., Tripp, A., Hernández-Lobato, J. M., Bender, A., & Bacallado, S. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01334 · Cited by 151

Property-Unmatched Decoys in Docking Benchmarks

Stein, R. M., Ying, Y., Balius, T. E., O’Meara, M. J., Lyu, J., Young, J., Tang, K. G., Shoichet, B. K., & Irwin, J. J. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00598 · Cited by 143

Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery

(2023) · doi:10.1021/acs.jcim.2c01219 · Cited by 136

Improving protein–ligand docking and screening accuracies by incorporating a scoring function correction term

Zheng, L., Meng, J., Jiang, K., Lan, H., Wang, Z., Lin, M., Li, W., Guo, H., Wei, Y., & Mu, Y. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac051 · Cited by 141

DrugRep: an automatic virtual screening server for drug repurposing

Gan, J.-H., Liu, J., Liu, Y., Chen, S., Dai, W., Xiao, Z. J., & Cao, Y. (2022) · Acta Pharmacologica Sinica · doi:10.1038/s41401-022-00996-2 · Cited by 145

Identifying Structure–Property Relationships through SMILES Syntax Analysis with Self-Attention Mechanism

Zheng, S., Yan, X., Yang, Y., & Xu, J. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00803 · Cited by 131

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-020-0049-y · Cited by 146

PLANET: A Multi-objective Graph Neural Network Model for Protein–Ligand Binding Affinity Prediction

Zhang, X., Gao, H., Wang, H., Chen, Z., Zhang, Z., Chen, X., Li, Y., Qi, Y., & Wang, R. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00253 · Cited by 143

Sequence-based drug design as a concept in computational drug design

Chen, L., Fan, Z., Chang, J., Yang, R., Hou, H., Guo, H., Zhang, Y., Yang, T., Zhou, C., Sui, Q., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39856-w · Cited by 135

Improved Method of Structure-Based Virtual Screening via Interaction-Energy-Based Learning

Yasuo, N., & Sekijima, M. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00673 · Cited by 126

From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design

Škalič, M., Sabbadin, D., Sattarov, B., Sciabola, S., & Fabritiis, G. D. (2019) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.9b00634 · Cited by 134

Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data

Imrie, F., Bradley, A. R., van der Schaar, M., & Deane, C. M. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00350 · Cited by 127

Predicting or Pretending: Artificial Intelligence for Protein-Ligand Interactions Lack of Sufficiently Large and Unbiased Datasets

Yang, J., Shen, C., & Huang, N. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00069 · Cited by 128

AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification

Yazdani-Jahromi, M., Yousefi, N., Tayebi, A., Kolanthai, E., Neal, C. J., Seal, S., & Garibay, Ö. Ö. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac272 · Cited by 125

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Vuorinen, A., & Schuster, D. (2014) · Methods · doi:10.1016/j.ymeth.2014.10.013 · Cited by 132

Scientific Large Language Models: A Survey on Biological & Chemical Domains

Zhang, Q., Ding, K., Lv, T., Wang, X., Yin, Q., Zhang, Y., Jing, Y., Wang, Y., Li, X., Xiang, Z., et al. (2025) · ACM Computing Surveys · doi:10.1145/3715318 · Cited by 216

Virtual Screening Techniques in Drug Discovery: Review and Recent Applications

Rocha, S. F. L. D. S., Olanda, C. G., Fokoue, H. H., & Sant’Anna, C. M. R. (2019) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026619666190816101948 · Cited by 126

USRCAT: real-time ultrafast shape recognition with pharmacophoric constraints

Schreyer, A. M., & Blundell, T. L. (2012) · Journal of Cheminformatics · doi:10.1186/1758-2946-4-27 · Cited by 119

A review on compound-protein interaction prediction methods: Data, format, representation and model

Lim, S., Lu, Y., Cho, C. Y., Sung, I., Kim, J.-W., Kim, Y., Park, S., & Kim, S. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.03.004 · Cited by 120

Discovery of Multitarget-Directed Ligands against Alzheimer’s Disease through Systematic Prediction of Chemical–Protein Interactions

Fang, J., Li, Y., Liu, R., Pang, X., Li, C., Yang, R., He, Y., Lian, W., Liu, A., & Du, G. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500574n · Cited by 118

Benchmark of four popular virtual screening programs: construction of the active/decoy dataset remains a major determinant of measured performance

Chaput, L., Martinez‐Sanz, J., Saettel, N., & Mouawad, L. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0167-x · Cited by 118

RosENet: Improving Binding Affinity Prediction by Leveraging Molecular Mechanics Energies with an Ensemble of 3D Convolutional Neural Networks

Harrirou, H. H., Zhang, C., & Lemmin, T. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00075 · Cited by 117

Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects

Cortés‐Ciriano, I., Ain, Q. U., Subramanian, V., Lenselink, E. B., Méndez‐Lucio, O., IJzerman, A. P., Wohlfahrt, G., Prūsis, P., Malliavin, T. E., van Westen, G. J. P., et al. (2014) · MedChemComm · doi:10.1039/c4md00216d · Cited by 119

SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions

Xavier, M. M., Heck, G. S., de Ávila, M. B., Levin, N. M. B., Pintro, V. O., Carvalho, N. L., & de Azevedo, W. F. (2016) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207319666160927111347 · Cited by 116

BridgeDPI: a novel Graph Neural Network for predicting drug–protein interactions

Wu, Y., Gao, M., Zeng, M., Zhang, J., & Li, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac155 · Cited by 122

New avenues in artificial-intelligence-assisted drug discovery

Cerchia, C., & Lavecchia, A. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103516 · Cited by 117

Rocker: Open source, easy-to-use tool for AUC and enrichment calculations and ROC visualization

Lätti, S., Niinivehmas, S. P., & Pentikäinen, O. T. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0158-y · Cited by 113

Molecular docking and molecular dynamics simulation approach to screen natural compounds for inhibition of Xanthomonas oryzae pv. Oryzae by targeting peptide deformylase

Joshi, T., Joshi, T., Sharma, P., Chandra, S., & Pande, V. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1719200 · Cited by 113

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

Ganser, L. R., Lee, J. H., Rangadurai, A., Merriman, D. K., Kelly, M. L., Kansal, A. D., Sathyamoorthy, B., & Al‐Hashimi, H. M. (2018) · Nature Structural & Molecular Biology · doi:10.1038/s41594-018-0062-4 · Cited by 112

Molecular Dynamics in Mixed Solvents Reveals Protein–Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions

Arcon, J. P., Defelipe, L. A., Modenutti, C. P., Lopez, E. D., Álvarez-García, D., Barril, X., Turjanski, A. G., & Marti, M. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00678 · Cited by 115

Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design

Lin, E., Lin, C., & Lane, H. (2020) · Molecules · doi:10.3390/molecules25143250 · Cited by 113

Conformation Guides Molecular Efficacy in Docking Screens of Activated β-2 Adrenergic G Protein Coupled Receptor

Weiss, D. R., Ahn, S., Sassano, M. F., Kleist, A. B., Zhu, X., Strachan, R. T., Roth, B. L., Lefkowitz, R. J., & Shoichet, B. K. (2013) · ACS Chemical Biology · doi:10.1021/cb400103f · Cited by 112

Uni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening

Yu, Y., Cai, C., Wang, J., Bo, Z., Zhu, Z., & Zheng, H. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01145 · Cited by 129

Decoys Selection in Benchmarking Datasets: Overview and Perspectives

Réau, M. F., Langenfeld, F., Zagury, J., Lagarde, N., & Montès, M. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00011 · Cited by 118

Population-based De Novo Molecule Generation, Using Grammatical Evolution

Yoshikawa, N., Terayama, K., Sumita, M., Homma, T., Oono, K., & Tsuda, K. (2018) · Chemistry Letters · doi:10.1246/cl.180665 · Cited by 111

Machine learning consensus scoring improves performance across targets in structure-based virtual screening.

Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00153 · Cited by 107

Using attribution to decode binding mechanism in neural network models for chemistry

McCloskey, K., Taly, A., Monti, F., Brenner, M. P., & Colwell, L. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820657116 · Cited by 105

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition

Raschka, S., & Kaufman, B. (2020) · Methods · doi:10.1016/j.ymeth.2020.06.016 · Cited by 115

BACPI: a bi-directional attention neural network for compound–protein interaction and binding affinity prediction

Li, M., Lu, Z., Wu, Y., & Li, Y. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac035 · Cited by 107

Generating property-matched decoy molecules using deep learning

Imrie, F., Bradley, A. R., & Deane, C. M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab080 · Cited by 113

DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity

Zhang, H., Liao, L., Saravanan, K. M., Yin, P., & Wei, Y. (2019) · PeerJ · doi:10.7717/peerj.7362 · Cited by 107

POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening

Ansar, S., & Vetrivel, U. (2018) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2018.02.012 · Cited by 101

EDock: blind protein–ligand docking by replica-exchange monte carlo simulation

Zhang, W., Bell, E. W., Yin, M., & Zhang, Y. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00440-9 · Cited by 103

Molecular Docking and 3D-Pharmacophore Modeling to Study the Interactions of Chalcone Derivatives with Estrogen Receptor Alpha

Muchtaridi, M., Syahidah, H. N., Subarnas, A., Yusuf, M., Bryant, S. D., & Langer, T. (2017) · Pharmaceuticals · doi:10.3390/ph10040081 · Cited by 102

Interaction prediction in structure-based virtual screening using deep learning

Gonczarek, A., Tomczak, J. M., Zaręba, S., Kaczmar-Michalska, J., Dąbrowski, P., & Walczak, M. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.007 · Cited by 105

Comprehensive Survey of Consensus Docking for High-Throughput Virtual Screening

Blanes‐Mira, C., Fernández-Aguado, P., de Andrés-López, J., Fernández-Carvajal, A. M., Ferrer-Montiel, A. V., & Fernandez-Ballester, G. J. (2022) · Molecules · doi:10.3390/molecules28010175 · Cited by 113

A practical guide to machine-learning scoring for structure-based virtual screening

Tran‐Nguyen, V., Junaid, M., Simeon, S., & Ballester, P. J. (2023) · Nature Protocols · doi:10.1038/s41596-023-00885-w · Cited by 106

Ensemble Docking in Drug Discovery: How Many Protein Configurations from Molecular Dynamics Simulations are Needed To Reproduce Known Ligand Binding?

Evangelista, W., Ellingson, S. R., Smith, J. C., & Baudry, J. Y. (2019) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b11491 · Cited by 99

Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach

Morrone, J. A., Weber, J. K., Huynh, T., Luo, H., & Cornell, W. D. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00927 · Cited by 97

Machine-Learning-Guided Cocrystal Prediction Based on Large Data Base

Wang, D., Yang, Z., Zhu, B., Mei, X., & Luo, X. (2020) · Crystal Growth & Design · doi:10.1021/acs.cgd.0c00767 · Cited by 98

Machine learning approaches for elucidating the biological effects of natural products

Zhang, R., Li, X., Zhang, X., Qin, H., & Xiao, W. (2020) · Natural Product Reports · doi:10.1039/d0np00043d · Cited by 96

Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures

(2023) · doi:10.1021/acs.jcim.2c01270 · Cited by 95

Dynamic undocking and the quasi-bound state as tools for drug discovery

Carmona, S. R., Schmidtke, P., Luque, F. J., Baker, L. M., Matassova, N., Davis, B. J., Roughley, S. D., Murray, J. B., Hubbard, R. E., & Barril, X. (2016) · Nature Chemistry · doi:10.1038/nchem.2660 · Cited by 92

A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening

Gorgulla, C., Das, K. M., Leigh, K. E., Cespugli, M., Fischer, P. D., Wang, Z., Tesseyre, G., Pandita, S., Shnapir, A., Calderaio, A., et al. (2021) · iScience · doi:10.1016/j.isci.2020.102021 · Cited by 96

Graph Neural Networks in Modern AI-Aided Drug Discovery

Zhang, O., Lin, H., Zhang, X., Wang, X., Wu, Z., Ye, Q., Zhao, W., Wang, J., Ying, K., Kang, Y., et al. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00461 · Cited by 110

Deep learning with feature embedding for compound-protein interaction predictionPreprint

Wan, F., & Zeng, J. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/086033 · Cited by 97

DeepCPI: A Deep Learning-Based Framework for Large-Scale in Silico Drug Screening

Wan, F., Zhu, Y., Hu, H., Dai, A., Cai, X., Chen, L., Gong, H., Xia, T., Yang, D., Wang, M.-W., et al. (2019) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.04.003 · Cited by 92

Graph Neural Networks for Molecules

Wang, Y., Li, Z., & Farimani, A. B. (2023) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-031-37196-7_2 · Cited by 118

LiSiCA: A Software for Ligand-Based Virtual Screening and Its Application for the Discovery of Butyrylcholinesterase Inhibitors

Lešnik, S., Štular, T., Brus, B., Knez, D., Gobec, S., Janežič, D., & Konc, J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00136 · Cited by 91

LS-align: an atom-level, flexible ligand structural alignment algorithm for high-throughput virtual screening

Hu, J. S., Liu, Z., Yu, D., & Zhang, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty081 · Cited by 90

RNA –ligand molecular docking: Advances and challenges

Zhou, Y., Jiang, Y., & Chen, S. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1571 · Cited by 94

Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development

Kricker, J. A., Page, C. P., Gardarsson, F. R., Baldursson, Ó., Guðjónsson, Þ., & Parnham, M. J. (2021) · Pharmacological Reviews · doi:10.1124/pharmrev.121.000300 · Cited by 103

Toward Fully Automated High Performance Computing Drug Discovery: A Massively Parallel Virtual Screening Pipeline for Docking and Molecular Mechanics/Generalized Born Surface Area Rescoring to Improve Enrichment

Zhang, X., Wong, S., & Lightstone, F. C. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci4005145 · Cited by 91

Perceiver CPI: a nested cross-attention network for compound–protein interaction prediction

Nguyen, N.-Q., Jang, G., Kim, H., & Kang, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac731 · Cited by 83

Break Down in Order To Build Up: Decomposing Small Molecules for Fragment-Based Drug Design with e MolFrag

Liu, T., Naderi, M., Alvin, C., Mukhopadhyay, S., & Bryliński, M. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00596 · Cited by 87

Evaluation of consensus scoring methods for AutoDock Vina, smina and idock

Masters, L., Eagon, S., & Heying, M. (2020) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2020.107532 · Cited by 90

Identifying ligands at orphan GPCRs: current status using structure‐based approaches

Ngo, T., Kufareva, I., Coleman, J. L. J., Graham, R. M., Abagyan, R. A., & Smith, N. J. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13452 · Cited by 89

Identification of Estrogen Receptor α Antagonists from Natural Products via In Vitro and In Silico Approaches

Pang, X., Fu, W., Wang, J., Kang, D., Xu, L., Zhao, Y., Liu, A., & Du, G. (2018) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2018/6040149 · Cited by 86

Rational design of allosteric modulators: Challenges and successes

Chatzigoulas, A., & Cournia, Z. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1529 · Cited by 90

Benchmarking data sets for the evaluation of virtual ligand screening methods: review and perspectives.

Lagarde, N., Zagury, J.-F., & Montes, M. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00090 · Cited by 85

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2

de Ávila, M. B., Xavier, M. M., Pintro, V. O., & de Azevedo, W. F. (2017) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2017.10.035 · Cited by 85

Effective drug–target interaction prediction with mutual interaction neural network

Li, F., Zhang, Z., Guan, J., & Zhou, S. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac377 · Cited by 88

The Roles of the NLRP3 Inflammasome in Neurodegenerative and Metabolic Diseases and in Relevant Advanced Therapeutic Interventions

Pirzada, R. H., Javaid, N., & Choi, S. (2020) · Genes · doi:10.3390/genes11020131 · Cited by 84

Beware of the generic machine learning-based scoring functions in structure-based virtual screening

Shen, C., Hu, Y., Wang, Z., Zhang, X., Pang, J., Wang, G., Zhong, H., Xu, L., Cao, D., & Hou, T. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa070 · Cited by 80

An up-to-date overview of computational polypharmacology in modern drug discovery

Chaudhari, R. R., Fong, L. W. R., Tan, Z., Huang, B., & Zhang, S. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1767063 · Cited by 84

Representation learning applications in biological sequence analysis

Iuchi, H., Matsutani, T., Yamada, K., Iwano, N., Sumi, S., Hosoda, S., Zhao, S., Fukunaga, T., & Hamada, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.039 · Cited by 88

Pharmacophore-Based Virtual Screening, Quantum Mechanics Calculations, and Molecular Dynamics Simulation Approaches Identified Potential Natural Antiviral Drug Candidates against MERS-CoV S1-NTD

Bouback, T. A., Pokhrel, S., Albeshri, A. O., Aljohani, A. M., Samad, A., Alam, R., Hossen, M. S., AlGhamdi, K. M., Talukder, M. E. K., Ahammad, F., et al. (2021) · Molecules · doi:10.3390/molecules26164961 · Cited by 85

Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking

Ellis, C. R., Kruhlak, N. L., Kim, M. T., Hawkins, E. G., & Stavitskaya, L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197734 · Cited by 85

Sequence-based drug-target affinity prediction using weighted graph neural networks

Jiang, M., Wang, S., Zhang, S., Zhou, W. G., Zhang, Y., & Li, Z. (2022) · BMC Genomics · doi:10.1186/s12864-022-08648-9 · Cited by 86

Improving virtual screening results with MM/GBSA and MM/PBSA rescoring

Sahakyan, H. (2021) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-021-00389-3 · Cited by 80

A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers

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