CSM-lig: a web server for assessing and comparing protein-small molecule affinities

Development and testing of a general amber force field

Wang, J.-M., Wolf, R., Caldwell, J., Kollman, P., & Case, D. (2004) · Journal of Computational Chemistry · doi:10.1002/jcc.20035 · Cited by 18,318

Gaussian Processes for Machine Learning

Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705

Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy

Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057

CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields

Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S. C., Zhong, S., Shim, J. Y., Darian, E., Guvench, O., Lopes, P. E. M., Vorobyov, I. V., et al. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21367 · Cited by 7,637

pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2015) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b00104 · Cited by 6,126

A semiempirical free energy force field with charge‐based desolvation

Huey, R., Morris, G. M., Olson, A. J., & Goodsell, D. S. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20634 · Cited by 2,246

A Critical Assessment of Docking Programs and Scoring Functions

Warren, G. L., Andrews, C. W., Capelli, A., Clarke, B., LaLonde, J. M., Lambert, M. H., Lindvall, M., Nevins, N., Semus, S. F., Senger, S., et al. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050362n · Cited by 1,889

mCSM: Predicting the effects of mutations in proteins using graph-based signatures

Pires, D. E. V., Ascher, D. B., & Blundell, T. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt691 · Cited by 1,066

PDB-wide collection of binding data: current status of the PDBbind database

Liu, Z., Li, Y., Han, L., Li, J., Liu, J., Zhao, Z., Nie, W., Liu, Y., & Wang, R. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu626 · Cited by 663

Comparative Assessment of Scoring Functions on an Updated Benchmark: 2. Evaluation Methods and General Results

Li, Y., Han, L., Liu, Z., & Wang, R. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci500081m · Cited by 388

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473

DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability

Rodrigues, C. H., Pires, D. E., & Ascher, D. B. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky300 · Cited by 1,086

K DEEP : Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks

Jiménez-Luna, J., Škalič, M., Martínez-Rosell, G., & Fabritiis, G. D. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00650 · Cited by 946

DynaMut2 : Assessing changes in stability and flexibility upon single and multiple point missense mutations

Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2020) · Protein Science · doi:10.1002/pro.3942 · Cited by 697

Key Topics in Molecular Docking for Drug Design

Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva, F. P. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184574 · Cited by 703

Performance of machine-learning scoring functions in structure-based virtual screening.

Wójcikowski, M., Ballester, P. J., & Siedlecki, P. (2017) · Scientific Reports · doi:10.1038/srep46710 · Cited by 364

mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

Rodrigues, C. H. M., Myung, Y., Pires, D. E. V., & Ascher, D. B. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz383 · Cited by 369

Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges

Guedes, I. A., Pereira, F. S. S., & Dardenne, L. E. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01089 · Cited by 331

Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation

Decherchi, S., & Cavalli, A. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00534 · Cited by 308

Large-scale prediction of binding affinity in protein–small ligand complexes: the PRODIGY-LIG web server

Vangone, A., Schaarschmidt, J., Koukos, P. I., Geng, C., Citro, N., Trellet, M. E., Xue, L. C., & Bonvin, A. M. J. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty816 · Cited by 263

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Shen, C., Ding, J., Wang, Z., Cao, D., Ding, X., & Hou, T. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1429 · Cited by 255

The METLIN small molecule dataset for machine learning-based retention time prediction

Domingo-Almenara, X., Guijas, C., Billings, E., Montenegro-Burke, J. R., Uritboonthai, W., Aisporna, A., Chen, E., Benton, H. P., & Siuzdak, G. E. (2019) · Nature Communications · doi:10.1038/s41467-019-13680-7 · Cited by 244

Machine learning for synergistic network pharmacology: a comprehensive overview

Noor, F., Asif, M., Ashfaq, U. A., Qasim, M., & Qamar, M. T. U. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad120 · Cited by 226

Predictions of Ligand Selectivity from Absolute Binding Free Energy Calculations

Aldeghi, M., Heifetz, A., Bodkin, M. J., Knapp, S., & Biggin, P. C. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.6b11467 · Cited by 177

Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand

Pires, D. E. V., Kaminskas, L. M., & Ascher, D. B. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7756-7_14 · Cited by 156

Machine‐learning scoring functions for structure‐based drug lead optimization

Li, H., Sze, K., Lü, G., & Ballester, P. J. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1465 · Cited by 150

mCSM-lig: quantifying the effects of mutations on protein-small molecule affinity in genetic disease and emergence of drug resistance

Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2016) · Scientific Reports · doi:10.1038/srep29575 · Cited by 143

mCSM-NA: predicting the effects of mutations on protein-nucleic acids interactions

Pires, D. E. V., & Ascher, D. B. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx236 · Cited by 127

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

Singh, N., Chaput, L., & Villoutreix, B. O. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa034 · Cited by 125

Accurate Estimation of Ligand Binding Affinity Changes upon Protein Mutation

Aldeghi, M., Gapsys, V., & de Groot, B. L. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00717 · Cited by 116

mCSM-membrane: predicting the effects of mutations on transmembrane proteins

Pires, D. E. V., Rodrigues, C. H. M., & Ascher, D. B. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa416 · Cited by 101

NRT1.1 Dual-Affinity Nitrate Transport/Signalling and its Roles in Plant Abiotic Stress Resistance

Fang, X. Z., Fang, S. Q., Ye, Z. Q., Liu, D., Zhao, K. L., & Jin, C. W. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.715694 · Cited by 105

Novel scaffold hopping of potent benzothiazole and isatin analogues linked to 1,2,3-triazole fragment that mimic quinazoline epidermal growth factor receptor inhibitors: Synthesis, antitumor and mechanistic analyses

Rezki, N., Almehmadi, M. A., Ihmaid, S. K., Shehata, A. M., Omar, A. M., Ahmed, H. E. A., & Aouad, M. R. (2020) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2020.104133 · Cited by 104

Artificial intelligence in virtual screening: Models versus experiments

Murugan, N. A., Priya, G. R., Sastry, G. N., & Markidis, S. (2022) · Drug Discovery Today · doi:10.1016/j.drudis.2022.05.013 · Cited by 99

Evolution of carbapenem resistance in Acinetobacter baumannii during a prolonged infection

Hawkey, J., Ascher, D. B., Judd, L. M., Wick, R. R., Kostoulias, X., Cleland, H. J., Spelman, D. W., Padiglione, A. A., Peleg, A. Y., & Holt, K. E. (2018) · Microbial Genomics · doi:10.1099/mgen.0.000165 · Cited by 92

mCSM-AB2: guiding rational antibody design using graph-based signatures

Myung, Y., Rodrigues, C. H. M., Ascher, D. B., & Pires, D. E. V. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz779 · Cited by 82

mmCSM-PPI: predicting the effects of multiple point mutations on protein–protein interactions

Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab273 · Cited by 83

epitope3D: a machine learning method for conformational B-cell epitope prediction

Silva¹, B. F. D., Myung, Y., Ascher, D. B., & Pires, D. E. V. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab423 · Cited by 84

CSM-AB: graph-based antibody–antigen binding affinity prediction and docking scoring function

Myung, Y., Pires, D. E. V., & Ascher, D. B. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab762 · Cited by 72

Structure guided prediction of Pyrazinamide resistance mutations in pncA

Karmakar, M., Rodrigues, C. H. M., Horan, K., Denholm, J. T., & Ascher, D. B. (2020) · Scientific Reports · doi:10.1038/s41598-020-58635-x · Cited by 77

Familial STAG2 germline mutation defines a new human cohesinopathy

Soardi, F. C., Machado-Silva, A., Linhares, N. D., Zheng, G., Qu, Q., Pena, H. B., Martins, T. M. M., Vieira, H. G. S., Pereira, N. B., de Melo-Minardi, R. C., et al. (2017) · npj Genomic Medicine · doi:10.1038/s41525-017-0009-4 · Cited by 71

Empirical ways to identify novel Bedaquiline resistance mutations in AtpE

Karmakar, M., Rodrigues, C. H. M., Holt, K. E., Dunstan, S. J., Denholm, J. T., & Ascher, D. B. (2019) · PLoS ONE · doi:10.1371/journal.pone.0217169 · Cited by 72

Mechanism of Dayuanyin in the treatment of coronavirus disease 2019 based on network pharmacology and molecular docking

Ruan, X., Du, P. F., Zhao, K. F., Huang, J., Xia, H., Dai, D., Huang, S., Cui, X., Liu, L., & Zhang, J. (2020) · Chinese Medicine · doi:10.1186/s13020-020-00346-6 · Cited by 64

Fragment-Based Approach to Targeting Inosine-5′-monophosphate Dehydrogenase (IMPDH) from Mycobacterium tuberculosis

Trapero, A., Pacitto, A., Singh, V., Sabbah, M., Coyne, A. G., Mizrahi, V., Blundell, T. L., Ascher, D. B., & Abell, C. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b01622 · Cited by 63

toxCSM: comprehensive prediction of small molecule toxicity profiles

de Sá, A. G. C., Long, Y., Portelli, S., Pires, D. E. V., & Ascher, D. B. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac337 · Cited by 61

Synthesis, characterization, molecular modeling and preliminary biochemical evaluation of new copper (II) mixed-ligand complexes

Ismael, M., Abdel‐Mawgoud, A. M., Rabia, M. K. M., & Abdou, A. (2020) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2020.129695 · Cited by 60

The Impact of Protein Structure and Sequence Similarity on the Accuracy of Machine-Learning Scoring Functions for Binding Affinity Prediction

Li, H., Peng, J., Leung, Y., Leung, K., Wong, M., Lü, G., & Ballester, P. J. (2018) · Biomolecules · doi:10.3390/biom8010012 · Cited by 55

cardioToxCSM : A Web Server for Predicting Cardiotoxicity of Small Molecules

Iftkhar, S., de Sá, A. G. C., Velloso, J. P. L., Al‐Jarf, R., Pires, D. E. V., & Ascher, D. B. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00822 · Cited by 56

Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity

Wójcikowski, M., Siedlecki, P. S., & Ballester, P. J. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9752-7_1 · Cited by 55

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties

Al‐Jarf, R., de Sá, A. G. C., Pires, D. E. V., & Ascher, D. B. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00168 · Cited by 60

mmCSM-AB: guiding rational antibody engineering through multiple point mutations

Myung, Y., Pires, D. E. V., & Ascher, D. B. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa389 · Cited by 51

SDHA related tumorigenesis: a new case series and literature review for variant interpretation and pathogenicity

Casey, R. T., Ascher, D. B., Rattenberry, E., Izatt, L., Andrews, K. A., Simpson, H., Challis, B. G., Park, S., Bulusu, V. R., Lalloo, F. I., et al. (2017) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.279 · Cited by 52

The rational design, synthesis, and antimicrobial investigation of 2-Amino-4-Methylthiazole analogues inhibitors of GlcN-6-P synthase

Omar, A. M., Ihmaid, S. K., Habib, E. E., Althagfan, S. S., Ahmed, S., Abulkhair, H. S., & Ahmed, H. E. A. (2020) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2020.103781 · Cited by 50

Novel molecular discovery of promising amidine-based thiazole analogues as potent dual Matrix Metalloproteinase-2 and 9 inhibitors: Anticancer activity data with prominent cell cycle arrest and DNA fragmentation analysis effects

Omar, A. M., Bajorath, J., Ihmaid, S. K., Mohamed, H. M., El-Agrody, A. M., Mora, A., El‐Araby, M. E., & Ahmed, H. E. A. (2020) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2020.103992 · Cited by 49

mycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits against Mycobacteria

Pires, D. E. V., & Ascher, D. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00362 · Cited by 48

dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction

Kaminskas, L. M., Pires, D. E. V., & Ascher, D. B. (2019) · Scientific Reports · doi:10.1038/s41598-019-51789-3 · Cited by 46

Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures

Pan, Q., Nguyen, T., Ascher, D. B., & Pires, D. E. V. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac025 · Cited by 50

Genomes, structural biology and drug discovery: combating the impacts of mutations in genetic disease and antibiotic resistance

Pandurangan, A. P., Ascher, D. B., Thomas, S. E., & Blundell, T. L. (2017) · Biochemical Society Transactions · doi:10.1042/bst20160422 · Cited by 40

Synthesis and Structure–Activity relationship of 1-(5-isoquinolinesulfonyl)piperazine analogues as inhibitors of Mycobacterium tuberculosis IMPDH

Singh, V., Pacitto, A., Donini, S., Ferraris, D. M., Boros, S., Illyés, E., Szokol, B., Rizzi, M., Blundell, T. L., Ascher, D. B., et al. (2019) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2019.04.027 · Cited by 36

Bisphenol A Inhibits the Transporter Function of the Blood-Brain Barrier by Directly Interacting with the ABC Transporter Breast Cancer Resistance Protein (BCRP)

Engdahl, E., van Schijndel, M. D. M., Voulgaris, D., Criscio, M. D., Ramsbottom, K. A., Rigden, D. J., Herland, A., & Rüegg, J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22115534 · Cited by 36

The Presence, Persistence and Functional Properties of Plasmodium vivax Duffy Binding Protein II Antibodies Are Influenced by HLA Class II Allelic Variants

Kano, F. S., Souza-Silva, F. A., Torres-Martínez, L. M., Lima, B. A. S., de Sousa, T. N., Alves, J. R. S., Rocha, R. S., Fontes, C. J. F., Sanchez, B. A. M., Adams, J. H., et al. (2016) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0005177 · Cited by 34

cropCSM: designing safe and potent herbicides with graph-based signatures

Pires, D. E. V., Stubbs, K. A., Mylne, J. S., & Ascher, D. B. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac042 · Cited by 40

Combating mutations in genetic disease and drug resistance: understanding molecular mechanisms to guide drug design

Albanaz, A. T. S., Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2017) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2017.1322579 · Cited by 33

Kinact: a computational approach for predicting activating missense mutations in protein kinases

Rodrigues, C. H., Ascher, D. B., & Pires, D. E. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky375 · Cited by 31

EasyVS: a user-friendly web-based tool for molecule library selection and structure-based virtual screening

Pires, D. E. V., Veloso, W. N. D. P., Myung, Y., Rodrigues, C. H. M., Silk, M. A., Rezende, P. M., Silva, F., Xavier, J. S., Velloso, J. P. L., da Silveira, C. H., et al. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa480 · Cited by 30

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase

Zhou, Y., Portelli, S., Pat, M., Rodrigues, C. H. M., Nguyen, T., Pires, D. E. V., & Ascher, D. B. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.09.016 · Cited by 31

AMMOS2: a web server for protein–ligand–water complexes refinement via molecular mechanics

Labbé, C. M., Pencheva, T., Jereva, D., Desvillechabrol, D., Becot, J., Villoutreix, B. O., Pajeva, I. K., & Miteva, M. A. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx397 · Cited by 30

SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space

de Azevedo, W. F., Quiroga, R., Villarreal, M. A., da Silveira, N. J. F., Bitencourt‐Ferreira, G., da Silva, A. D., Veit‐Acosta, M., Oliveira, P. R., Tutone, M., Biziukova, N. Y., et al. (2024) · Journal of Computational Chemistry · doi:10.1002/jcc.27449 · Cited by 28

Improving the binding affinity estimations of protein–ligand complexes using machine-learning facilitated force field method

Soni, A., Bhat, R., & Jayaram, B. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00305-1 · Cited by 26

Adaptive Regulation of Nitrate Transceptor NRT1.1 in Fluctuating Soil Nitrate Conditions

Rashid, M., Bera, S., Medvinsky, A. B., Sun, G., Li, B.-L., & Chakraborty, A. (2018) · iScience · doi:10.1016/j.isci.2018.03.007 · Cited by 26

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity

Ammar, A., Cavill, R., Evelo, C. T., & Willighagen, E. (2023) · Journal of Cheminformatics · doi:10.1186/s13321-023-00701-3 · Cited by 25

A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments

Lagarde, N., Goldwaser, E., Pencheva, T., Jereva, D., Pajeva, I. K., Rey, J., Tufféry, P., Villoutreix, B. O., & Miteva, M. A. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184648 · Cited by 23

Cryo-EM structure of a thermostable bacterial nanocompartment

Wiryaman, T., & Toor, N. (2021) · IUCrJ · doi:10.1107/s2052252521001949 · Cited by 22

Evaluating hierarchical machine learning approaches to classify biological databases

Rezende, P. M., Xavier, J. S., Ascher, D. B., Fernandes, G. D. R., & Pires, D. E. V. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac216 · Cited by 26

Machine Learning Meets Physics-based Modeling: A Mass-spring System to Predict Protein-ligand Binding Affinity

de Azevedo, W. F. (2024) · Current Medicinal Chemistry · doi:10.2174/0109298673307315240730042209 · Cited by 22

Computational toolbox for the analysis of protein–glycan interactions

Nieto‐Fabregat, F., Lenza, M. P., Marseglia, A., Carluccio, C. D., Molinaro, A., Silipo, A., & Marchetti, R. (2024) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.20.180 · Cited by 23

pdCSM-GPCR: predicting potent GPCR ligands with graph-based signatures

Velloso, J. P. L., Ascher, D. B., & Pires, D. E. V. (2021) · Bioinformatics Advances · doi:10.1093/bioadv/vbab031 · Cited by 22

Repurposed Molecules: A New Hope in Tackling Neglected Infectious Diseases

Fernandez‐Prada, C., Douanne, N., Minguez-Menendez, A., Pena, J., Tunes, L. G., Pires, D. E. V., & do Monte‐Neto, R. L. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816125-8.00005-5 · Cited by 24

Critical assessment of approaches for molecular docking to elucidate associations of HLA alleles with adverse drug reactions

Ramsbottom, K. A., Carr, D. F., Jones, A. R., & Rigden, D. J. (2018) · Molecular Immunology · doi:10.1016/j.molimm.2018.08.003 · Cited by 21

embryoTox: Using Graph-Based Signatures to Predict the Teratogenicity of Small Molecules

Al‐Jarf, R., Tang, S., Pires, D. E. V., & Ascher, D. B. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00824 · Cited by 18

Computational representations of protein–ligand interfaces for structure-based virtual screening

Tong, Q., Zhu, Z., Wang, X. S., Xia, J., & Wu, S. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1929921 · Cited by 18

Comparative study of the interaction of ivermectin with proteins of interest associated with SARS-CoV-2: A computational and biophysical approach

González-Paz, L. A., Hurtado-León, M. L., Lossada, C. A., Fernández-Materán, F. V., Vera-Villalobos, J., Loroño, M., Paz, J. L., Jeffreys, L. N., & Alvarado, Y. J. (2021) · Biophysical Chemistry · doi:10.1016/j.bpc.2021.106677 · Cited by 16

kinCSM : Using graph‐based signatures to predict small molecule CDK2 inhibitors

Zhou, Y., Al‐Jarf, R., Alavi, A., Nguyen, T., Rodrigues, C. H. M., Pires, D. E. V., & Ascher, D. B. (2022) · Protein Science · doi:10.1002/pro.4453 · Cited by 17

Quinacrine inhibits GSTA1 activity and induces apoptosis through G1/S arrest and generation of ROS in human non-small cell lung cancer cell lines

Kumar, M., Martin, A., Nirgude, S., Chaudhary, B., Mondal, S., & Sarkar, A. (2020) · Oncotarget · doi:10.18632/oncotarget.27558 · Cited by 16

Development of a New Scoring Function for Virtual Screening: APBScore

Bao, J., He, X. D., & Zhang, J. Z. H. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00474 · Cited by 15

Novel Benzimidazole‐1,2,3‐Triazole Hybrids: Synthesis, Dual Antimicrobial, Anticancer Activity, Mechanistic Insights, and Computational Studies

Soliman, M. A., Eltamany, E. H. M., Boraei, A. T. A., Aouad, M. R., Aljuhani, A., Almohaywi, B., Awaji, A. A., Alghamdi, R., Aljohani, A. K. B., & Ahmed, H. E. A. (2025) · ChemistrySelect · doi:10.1002/slct.202500813 · Cited by 15

Protein–ligand docking using FFT based sampling: D3R case study

Padhorny, D., Hall, D. R., Mirzaei, H., Mamonov, A. B., Moghadasi, M., Alekseenko, A. E., Beglov, D., & Kozakov, D. (2017) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0069-7 · Cited by 14

Unveiling six potent and highly selective antileishmanial agents via the open source compound collection ‘Pathogen Box’ against antimony-sensitive and -resistant Leishmania braziliensis

Silva, J. A. S., Tunes, L. G., Coimbra, R. S., Ascher, D. B., Pires, D. E. V., & do Monte‐Neto, R. L. (2020) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2020.111049 · Cited by 14

The Role of Structural Bioinformatics in Understanding Tumor Necrosis Factor α-Interacting Protein Mechanisms in Chronic Inflammatory Diseases: A Review

Bastos, L. L., Mariano, D., Lemos, R. P., Bialves, T. S., Oliveira, C. J. F., & de Melo-Minardi, R. C. (2024) · Immuno · doi:10.3390/immuno4010002 · Cited by 16

CSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function

Nguyen, T., Pires, D. E. V., & Ascher, D. B. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab512 · Cited by 12

AI-Driven Enhancements in Drug Screening and Optimization

Serghini, A., Portelli, S., & Ascher, D. B. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3441-7_15 · Cited by 13

ET‐score: Improving Protein‐ligand Binding Affinity Prediction Based on Distance‐weighted Interatomic Contact Features Using Extremely Randomized Trees Algorithm

Rayka, M., Karimi‐Jafari, M. H., & Firouzi, R. (2021) · Molecular Informatics · doi:10.1002/minf.202060084 · Cited by 13

Exploring Protein Supersecondary Structure Through Changes in Protein Folding, Stability, and Flexibility

Pires, D. E. V., Rodrigues, C. H. M., Albanaz, A. T. S., Karmakar, M., Myung, Y., Xavier, J. S., Michanetzi, E.-M., Portelli, S., & Ascher, D. B. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9161-7_9 · Cited by 11

AI-driven GPCR analysis, engineering, and targeting

Velloso, J. P. L., Kovacs, A. S., Pires, D. E. V., & Ascher, D. B. (2024) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2023.102427 · Cited by 10

CSM‐Potential2 : A comprehensive deep learning platform for the analysis of protein interacting interfaces

Rodrigues, C. H. M., & Ascher, D. B. (2023) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.26615 · Cited by 9

piscesCSM: prediction of anticancer synergistic drug combinations

Al‐Jarf, R., Rodrigues, C. H. M., Myung, Y., Pires, D. E. V., & Ascher, D. B. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00859-4 · Cited by 11

The Route to ‘Chemobrain’ - Computational probing of neuronal LTP pathway

Fahim, A., Rehman, Z., Bhatti, M. F., Virk, N., Ali, A., Rashid, A., & Paracha, R. Z. (2019) · Scientific Reports · doi:10.1038/s41598-019-45883-9 · Cited by 9

Enhancing lipid identification in LC-HRMS data through machine learning-based retention time prediction

Noreldeen, H. A. A. (2025) · Journal of Chromatography A · doi:10.1016/j.chroma.2024.465650 · Cited by 7

Application of Docking for Lead Optimization

Patra, J., Singh, D., Jain, S., & Mahindroo, N. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822312-3.00012-6 · Cited by 8

Designing safe and potent herbicides with the cropCSM online resourcePreprint

Pires, D. E. V., Stubbs, K. A., Mylne, J. S., & Ascher, D. B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.01.364240 · Cited by 7

A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods

Pires, D. E. V., Portelli, S., Rezende, P. M., Veloso, W. N. D. P., Xavier, J. S., Karmakar, M., Myung, Y., Velloso, J. P. L., Rodrigues, C. H. M., Silk, M. A., et al. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0270-6_7 · Cited by 6

Docking, Scoring, and Virtual Screening in Drug Discovery

Spyrakis, F., Sarkar, A., & Kellogg, G. E. (2021) · Wiley eBooks · doi:10.1002/0471266949.bmc301 · Cited by 6

Informatics investigations into anti-thyroid drug induced agranulocytosis associated with multiple HLA-B alleles

Ramsbottom, K. A., Carr, D. F., Rigden, D. J., & Jones, A. R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0220754 · Cited by 5

Identifying Genotype–Phenotype Correlations via Integrative Mutation Analysis

Airey, E., Portelli, S., Xavier, J. S., Myung, Y., Silk, M. A., Karmakar, M., Velloso, J. P. L., Rodrigues, C. H. M., Parate, H. H., Garg, A., et al. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0826-5_1 · Cited by 5

Distinct N6-Substituted Adenosine Derivatives Exhibit Receptor-Specific Anticytokinin Action by Unknown Non-competitive Mechanism

Zenchenko, A. A., Savelieva, E. M., Semenova, J. D., Drenichev, M. S., Solyev, P. N., Arkhipov, D. V., Romanov, G. A., & Oslovsky, V. E. (2025) · Journal of Plant Growth Regulation · doi:10.1007/s00344-025-11714-7 · Cited by 4

CLIMBS: Assessing Carbohydrate–Protein Interactions through a Graph Neural Network Classifier Using Synthetic Negative Data

Luo, Y., & Parmeggiani, F. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6c00126 · Cited by 4

Antenna effect and phosphorescence spectra to find the location of drug tetracycline in bovine β-lactoglobulin A

Mukherjee, M., Sardar, P. S., Roy, P., Dasgupta, S., Roy, M. B., & Ghosh, S. (2018) · JBIC Journal of Biological Inorganic Chemistry · doi:10.1007/s00775-018-1591-3 · Cited by 4

Binding order of substrate and cofactor in sulfonamide monooxygenase during sulfa drug degradation: in silico studies

Desai, A., Mahajan, V., Ramabhadran, R. O., & Mukherjee, R. (2024) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2024.2306495 · Cited by 3

Deep learning in drug discovery

Bhati, M., Virmani, T., Kumar, G., Sharma, A., & Chitranshi, N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00013-8 · Cited by 2

Bioinformatics Databases and Tools Available for the Development of Antiviral Drugs

Singh, A., Dhaka, P., Kumar, P., Tomar, S., & Singla, J. (2024) · Livestock diseases and management · doi:10.1007/978-981-99-9195-2_2 · Cited by 2

Large-scale prediction of binding affinity in protein–small ligand complexes: the PRODIGY-LIG web server

Vangone, A., Schaarschmidt, J., Koukos, P. I., Geng, C., Citro, N., Trellet, M. E., Xue, L. C., & Bonvin, A. M. J. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty816 · Cited by 263

mCSM-lig: quantifying the effects of mutations on protein-small molecule affinity in genetic disease and emergence of drug resistance

Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2016) · Scientific Reports · doi:10.1038/srep29575 · Cited by 143

CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION FOR PROTEIN–LIGAND BINDING AFFINITY PREDICTION USING MODIFIED CMAC LEARNING ARCHITECTURE

Ouyang, X., Handoko, S. D., & Kwoh, C. K. (2011) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s021972001100577x · Cited by 51

SG ‐ ML ‐ PLAP : A structure‐guided machine learning–based scoring function for protein–ligand binding affinity prediction

Pal, S., Pal, A., & Mohanty, D. (2024) · Protein Science · doi:10.1002/pro.5257 · Cited by 11

A novel method for protein‐ligand binding affinity prediction and the related descriptors exploration

Li, S., Xi, L., Wang, C. C. Q., Li, J., Lei, B., Liu, H., & Yao, X. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.21078 · Cited by 28