Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

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

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Extra Precision Glide: Docking and Scoring Incorporating a Model of Hydrophobic Enclosure for Protein−Ligand Complexes

Friesner, R. A., Murphy, R. B., Repasky, M. P., Frye, L. L., Greenwood, J. R., Halgren, T. A., Sanschagrin, P. C., & Mainz, D. T. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051256o · Cited by 7,012

Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen

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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

HADDOCK: A Protein−Protein Docking Approach Based on Biochemical or Biophysical Information

Dominguez, C., Boelens, R., & Bonvin, A. M. J. J. (2003) · Journal of the American Chemical Society · doi:10.1021/ja026939x · Cited by 3,187

Improved protein–ligand docking using GOLD

Verdonk, M. L., Cole, J. C., Hartshorn, M. J., Murray, C. W., & Taylor, R. D. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10465 · Cited by 3,139

PatchDock and SymmDock: servers for rigid and symmetric docking

Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki481 · Cited by 3,018

A Fast Flexible Docking Method using an Incremental Construction Algorithm

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MolDock: A New Technique for High-Accuracy Molecular Docking

Thomsen, R., & Christensen, M. H. (2006) · Journal of Medicinal Chemistry · doi:10.1021/jm051197e · Cited by 2,381

Novel Procedure for Modeling Ligand/Receptor Induced Fit Effects

Sherman, W., Day, T., Jacobson, M. P., Friesner, R. A., & Farid, R. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050540c · Cited by 2,089

Scoring Functions in Docking Experiments

Ambure, P. S., & Roy, K. (2016) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-5225-0115-2.ch003 · Cited by 2

Approaching Pharmacological Space: Events and Components

Vistoli, G., Pedretti, A., Mazzolari, A., & Testa, B. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7899-1_12 · Cited by 2

Drug Discovery

Prabu, S. L. (2018) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-5225-7326-5.ch001 · Cited by 2

Towards an Enrichment Optimization Algorithm (EOA)‐based Target Specific Docking Functions for Virtual Screening

Spiegel, J. D., & Senderowitz, H. (2022) · Molecular Informatics · doi:10.1002/minf.202200034 · Cited by 2

Evaluation of Geometric Complementarity between Molecular Surfaces Using Compactly Supported Radial Basis Functions

Diago, L., & Moreno, E. (2009) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2009.31 · Cited by 2

Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock

Lu, Q., Qi, L., & Liu, J. (2019) · Journal of Theoretical and Computational Chemistry · doi:10.1142/s0219633619500275 · Cited by 2

Chemoinformatic and Chemogenomic Approach to ADMET

Martiny, V. Y., Pajeva, I. K., Wiese, M., Davis, A. M., & Miteva, M. A. (2014) · Wiley eBooks · doi:10.1002/9781118783344.ch7 · Cited by 2

DrugScreener-G: Towards an Integrated Environment for Grid-Enabled Large-Scale Virtual Screening and Drug Discovery

Kim, J. B., Dang, N. N., Lee, S.-H., Hwang, S., & Breton, V. (2008) · IEEE International Conference on eScience · doi:10.1109/escience.2008.26 · Cited by 2

Integration of Computational and Experimental Techniques for the Discovery of SARS-CoV-2 PLpro Covalent InhibitorsPreprint

Huang, H. Y., Pinus, S., Zhang, X. C., Wang, G., Rueda, A. M., Yaouba, S., Huck, S., Huot, M. J., Vlaho, D., Pottel, J., et al. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-r0v7t · Cited by 1

N-arylnaphthylamines as inhibitors of human immunodeficiency virus integrase - lens epithelium-derived growth factor interactions: theoretical studies

Капуста, К., Sizochenko, N., Anderson, M. L., Kołodziejczyk, W., Voronkov, E. O., Saloni, J., Leszczyński, J., & Hill, G. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1719203 · Cited by 1

Analysis of human aldehyde dehydrogenases (ALDH) gene expression pattern in breast cancer tissue samples: rutin-copper complex inhibit the breast cancer cell proliferation

Pattar, S. V., Mirjankar, M. R., Kulkarni, S. S., Gai, P. B., Pujar, N. K., Premakshi, H. G., Mulla, S. I., Babu, R. L., & Kamanavalli, C. M. (2021) · Egyptian Journal of Medical Human Genetics · doi:10.1186/s43042-021-00143-2 · Cited by 1

In silico study of the xanthine oxidase enzyme inhibition by the essential oil compounds of Myrtus communis plant in the treatment of gout disease

Hicham, A., Amal, B., Berrichi, A., & Ghalem, S. (2024) · Reaction Kinetics Mechanisms and Catalysis · doi:10.1007/s11144-024-02731-w · Cited by 1

In Silico Evaluation of Quercetin and CAPE as Potential Anti-Inflammatory Agents Targeting COX-2

Helmi, V. N., Anggraeni, F., Budiastuti, Widjiastuti, I., Ismiyatin, K., Irmawati, A., Surboyo, M. D. C., & Shariff, K. A. (2025) · Tropical Journal of Natural Product Research · doi:10.26538/tjnpr/v9i10.4 · Cited by 1

Computational evaluation of synergistic inhibitors for human tyrosinase: multiple-ligand docking and molecular dynamics study

Ayuhastuti, A., Megantara, S., Syah, I. S. K., Fakih, T. M., & Chaerunisaa, A. Y. (2026) · Green Chemistry Letters and Reviews · doi:10.1080/17518253.2025.2607804 · Cited by 1

Ant Colony Optimisation for Ligand Docking

Korb, O., & Cole, J. C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15461-4_7 · Cited by 1

Complementarity of Hydrophobic/Hydrophilic Properties In Protein—Ligand Complexes: A New Tool to Improve Docking Results

Pyrkov, T. V., Chugunov, A. O., Krylov, N. A., Nolde, D. E., & Efremov, R. G. (2009) · NATO science for peace and security series. B, Physics and biophysics · doi:10.1007/978-90-481-2368-1_2 · Cited by 1

Algorithmic challenges in structure-based drug design and NMR structural biology

Wang, L., Zou, S., & Wang, Y. (2012) · Frontiers of Electrical and Electronic Engineering in China · doi:10.1007/s11460-012-0193-z · Cited by 1

MTD-PLS and docking study for a series of substituted 2-phenylindole derivatives with oestrogenic activity

Șeclăman, E. P., Bora, A., Avram, S. I., Simon, Z., & Kurunczi, L. (2011) · Chemical Papers · doi:10.2478/s11696-011-0040-3 · Cited by 2

Docking features for predicting binding loss due to protein mutation

Goodacre, N., Edwards, N. J., Danielsen, M., Uetz, P., & Wu, C. H. (2014) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2649387.2649397 · Cited by 1

Structure-Based Virtual Screening Using Index Technology

Schlosser, J. (2011) · Cited by 1

SCDL Applications to Drug Discovery

Hecht, D., Sheu, P. C. ‐., & Tsai, J. J. P. (2009) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2009.82 · Cited by 1

Predicting nsSNPs that Disrupt Protein-Protein Interactions Using Docking

Goodacre, N., Edwards, N. J., Danielsen, M., Uetz, P., & Wu, C. H. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2520931 · Cited by 1

: The Pitfalls and Challenges of Predicting GPCR-Ligand Interactions.

Roumen, L., Mpa, S., Vroling, B., de Esch, I. J. P., de Vlieg, J., Leurs, R., Jpg, K., Nabuurs, S., & de Graaf, C. (2011) · Pharmaceuticals Policy and Law · Cited by 1

Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors

Thai, K., Dong, Q.-H., Nguyen, T.-T.-L., Le, D.-P., Le, M.-T., & Tran, T. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-0549-5.ch017 · Cited by 1

Role of Molecular Docking in Computer-Aided Drug Design and Development

Agarwal, R., Singh, A., & Sen, S. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-1762-7.ch026 · Cited by 1

Importance and Application of Computational Studies in Finding New Active Quinazoline Derivatives

Al-Madhagi, W. M. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.1002314 · Cited by 1

PARADOCKS – a framework for molecular docking

Pippel, M., Meier, R., & Sippl, W. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-s1-p35 · Cited by 1

Supercomputers in Modeling of Biological Systems

Maples, R., Ramasahayam, S., & Dumancas, G. G. (2015) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/978-1-4666-7461-5.ch008 · Cited by 1

Repurposing Potential of Diminazene Aceturate as an Inhibitor of the E. coli DNA Gyrase B

Dwivedi, V., Ayyagari, A., Chandran, R., Diwan, P., Gupta, S. M., & Gupta, V. (2020) · Journal of Biomedical Research & Environmental Sciences · doi:10.37871/jbres1153 · Cited by 1

Water mapping: Analysis of binding site spaces to enhance binding

Silakari, O., & Singh, P. K. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820546-4.00008-8 · Cited by 1

Computational Approaches for Structure Based Drug Design and Protein Structure-Function Prediction

Vankayala, S. L. (2013) · Digital Commons - University of South Florida (University of South Florida) · Cited by 1

The Comparison of Docking Search Algorithms and Scoring Functions

Novič, M., Tibaut, T., Anderluh, M., Borišek, J., & Tomašič, T. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-1762-7.ch032 · Cited by 1

Molecular docking and antibacterial activity of some natural products against cariogenic Staphylococcus aureus

Vidya, M., Bano, T., Velamakanni, R. P., Merugu, R., & Jayasree, D. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.05.588 · Cited by 1

A Pre-Docking Filter Based on Image RecognitionPreprint

Kiszka, E. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.8072 · Cited by 1

Target‐Based Virtual Screening to Address Protein–Protein Interfaces

Spérandio, O., Miteva, M. A., & Villoutreix, B. O. (2011) · Methods and principles in medicinal chemistry · doi:10.1002/9783527633326.ch16 · Cited by 1

Universal Optimizations of Scoring Functions for Virtual Screening

Onodera, K., & Kamijo, S. (2010) · Chem-Bio Informatics Journal · doi:10.1273/cbij.10.85 · Cited by 1

Protein‐Ligand Docking

Slynko, I., Rognan, D., & Kellenberger, E. (2017) · doi:10.1002/9781119161110.ch22 · Cited by 1

Prospects of Covalent Approaches in Drug Discovery: An Overview

Ramachandran, B., Saravanan, M., & Jeyakanthan, J. (2021) · Wiley eBooks · doi:10.1002/9781119654803.ch15 · Cited by 1

In Silico Protein Design and Virtual Screening

Chavda, V. P., Patel, Z., Parmar, Y., & Chavda, D. (2021) · doi:10.1002/9781119654803.ch5 · Cited by 1

In Silico Design and Selection of CD44 Antagonists

Ruiz-Moreno, A. J. (2021) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.193912062 · Cited by 1

Virtual Screening

Muegge, I., & Oloff, S. (2010) · Wiley eBooks · doi:10.1002/0471266949.bmc006.pub2 · Cited by 1

FLEXIBLE TOPOLOGY: A NEW METHOD FOR DYNAMIC DRUG DESIGNPreprint

Donyapour, N., Roussey, N. M., Bose, S., & Dickson, A. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-g7src · Cited by 1

A Novel Atom Pair Attention Methodology for MolecularRepresentation LearningPreprint

Sun, H., Jiang, Y., Wang, M., Xiao, X., Wan, X., Tang, M., Jin, X., Niu, Z., & Chen, H. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-3b0js · Cited by 1

Community Benchmarking Exercises for Docking and Scoring

Devi, B., Baidya, A. T. K., & Kumar, R. (2024) · Wiley eBooks · doi:10.1002/9783527840748.ch20 · Cited by 1

Antigenicity of envelop and non-structural proteins of dengue serotypes and their potentiality to elicit specific antibody

Venkatachalam, R., & Vijayakumar, S. (2015) · Asian Pacific Journal of Tropical Disease · doi:10.1016/s2222-1808(15)60855-6

Influence of genetic variability and external regulating factors on amyloid-beta peptide aggregation

Hubin, E. (2014) · doi:10.3990/1.9789036537643

Receptores de hormônios da tireóide: estudos computacionais, ressonância plasmônica de superfície e ensaios celulares

Valadares, N. F. (2008) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.76.2008.tde-18122008-154730

Molecular Modeling in Nucleic Acid‐Targeted Drug Design

Sun, L., Jin, H., Zhang, L., & Zhang, L. (2011) · doi:10.1002/9781118092804.ch6

Planejamento de inibidores da enzima gliceraldeído-3-fosfato desidrogenase de Trypanosoma cruzi e avaliação bioquímica por calorimetria de titulação isotérmica

Prokopczyk, I. M. (2012) · doi:10.11606/d.75.2012.tde-17042012-170802

Planejamento racional de novos agentes quimioterápicos: Identificação e estudos cinéticos de novos inibidores da gliceraldeído-3-fosfato desidrogenase glicossomal de Trypanosoma cruzi

Zottis, A. (2009) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.76.2009.tde-23042009-145454

Protein Kinase Inhibitors Highlight the Complexities of Drug-Target Non-Covalent Interactions

Engh, R. A. (2008) · Biotechnology & Biotechnological Equipment · doi:10.1080/13102818.2008.10817551

In silico target identification of biologically active compounds using an inverse docking simulation

Choi, Y. (2013) · TANG [HUMANITAS MEDICINE] · doi:10.5667/tang.2013.0008

Knowledge-based search and multi-objective filters

Hashmi, I., Veltri, D., Kabbani, N., & Shehu, A. (2014) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2649387.2649391

Multivariate design of molecular docking experiments : An investigation of protein-ligand interactions

Andersson, D. (2010) · KTH Publication Database DiVA (KTH Royal Institute of Technology)

Acceleration and Verification of Virtual High-throughput Multiconformer Docking

Ellingson, S. R. (2014)

CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION

Ouyang, X., Handoko, S. D., & Kwoh, C. K. (2011)

Molecular Docking Improvement: Coefficient Adaptive Genetic Algorithms for Multiple Scoring Functions

Kang, L., Gu, J., Li, K., Wang, X., & Li, Z. (2014)

Integrative Systems Approaches Towards Brain Pharmacology and Polypharmacology

Bonn, R. F.-W.-U. (2013)

Determination of in silico rules for predicting small molecule binding behavior to nucleic acids in vitro.

Holt, P. (2010) · doi:10.18297/etd/627

Marine invertebrate and bacterial extract library screens for novel modulators of the glucagon-like peptide-1 and glucose-dependent insulinotropic polypeptide receptors

Merchant, C. E. (2011) · cIRcle (University of British Columbia) · doi:10.14288/1.0105089

Virtual compound screening and SAR analysis: method development and practical applications in the design of new serine and cysteine protease inhibitors

Sisay, M. T. (2010) · bonndoc (University of Bonn)

Studies of an ortho- Formylation Reaction and its Application for the Syntheses of Anti-Cancer Agents

Akselsen, Ø. W. (2011) · NORA - Norwegian Open Research Archives

Computational Approaches for the Discovery of Novel Hepatitis C Virus NS3/4A and NS5B Inhibitors

Thai, K., Dong, Q.-H., Nguyen, T.-T.-L., Le, D.-P., Le, M.-T., & Tran, T. (2015) · Advances in chemical and materials engineering book series · doi:10.4018/978-1-4666-8136-1.ch009

UNIVERSIDADE ESTADUAL DE FEIRA DE SANTANA DEPARTAMENTO DE CIÊNCIAS BIOLÓGICAS PROGRAMA DE PÓS-GRADUAÇÃO EM RECURSOS GENÉTICOS VEGETAIS - PPGRGV

dos Santos, E. R. (2014)

Discovering Ligands for TRP Ion Channels Using Formal Concept Analysis

Sugiyama, M., Imajo, K., Otaki, K., & Yamamoto, A. (2014) · IMPERIAL COLLEGE PRESS eBooks · doi:10.1142/9781783265091_0007

Increasing the virtual screening quality to identify adrenergic β2 receptor ligands using classification trees

Istyastono, E. P. (2016) · AIP conference proceedings · doi:10.1063/1.4958512

Understanding structure, function, and evolution of protein-protein interactions by computational modeling and analysis

Zhao, N. (2013) · doi:10.32469/10355/37826

Bioquímica e biofísica computacionais: simulações da reatividade e da estrutura de biomoléculas

Arantes, G. M. (2016) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.46.2016.tde-03082016-145527

Robust Search Methods for Rational Drug Design Applications

Sadjad, B. S. (2009) · UWSpace (University of Waterloo)

A Quantitative Structure-Activity Relationship Study, Compound Development, Pharmacophore Feature, and Molecular Docking of Pyrazolo-(3,4-d)-Pyrimidine Derivatives as Mer Tyrosine Kinase Inhibitor

Hamzah, N., & Gowa, S. (2016)

NMR, FT-IR spectroscopic studies and molecular docking study of 4-acetoxyphenethyl acrylate

Dıkmen, G., & Alver, Ö. (2017) · Journal of Theoretical and Computational Chemistry · doi:10.1142/s0219633617500250

Insights into the Development of Chemotherapeutics Targeting PFKFB Enzymes

Crochet, R. B. (2015) · Louisiana State University · doi:10.31390/gradschool_dissertations.311

Simulações computacionais de desenovelamento de proteína e complexação de ligantes com amostragem aumentada

Nunes‐Alves, A. (2018) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.46.2018.tde-14122017-135400

Pranlukast; An Alternative Potential Leptin Stimulator: Structure-Based Virtual Screening Study

Alsafi, M. Y. B., Manalla, D. E. T., Abdulrahman, I. A., Ahmed, O. H., & El-Hadiyah, T. M. (2018) · Immunochemistry & Immunopathology · doi:10.4172/2469-9756.1000128

Structure-Based Drug Design with a Special Emphasis on Herbal Extracts

Velmurugan, D., Kutumbarao, N. H. V., Viswanathan, V., & Bhattacharjee, A. (2019) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-030-05282-9_9

Computer-Aided Design of Ligands at Multiple Protein Targets for Multifactorial Diseases

Kalash, L. N. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.38293

Compostos carbonílicos α,β-insaturados e hidrazonas sintéticas como antiparasitários

Gatti, F. D. M. (2020) · doi:10.11606/t.9.2020.tde-20022020-114648

Virtual Screening for Chemists

Saha, I., & Harran, P. G. (2021) · ACS in focus · doi:10.1021/acsinfocus.7e5001

From desktop to benchtop - Developing computational tools for organic and medicinal chemistry

Patrascu, M. B. (2020) · doi:10.82308/17726

Drug Repurposing for COVID-19 Using Molecular Docking Tools

Öztaş, D. Y. (2021) · Chapman University Digital Commons (Chapman University) · doi:10.36837/chapman.000286

Computational evaluation of binding property of riboswitches and optimization of self-docking of RNA-ligand complexes for drug discovery

Luo, J. (2019) · doi:10.82308/26390

In silico analysis of Trisindoline 1 compound against Mpro SARS-CoV-2 as novel potential drugs candidate

Lianingsih, F. (2021) · Sasambo Journal of Pharmacy · doi:10.29303/sjp.v2i2.87

Computer-Aided Drug Design for Membrane Channel Proteins

Wacker, S. (2012) · doi:10.53846/goediss-2927

Current Incorporation and Impact of Computational Biology in Alzheimer's Disease

Naaz, S., Rani, S. G., & Pati, P. (2022) · International Journal of Health Sciences and Research · doi:10.52403/ijhsr.20220345

On the energy minimization of large molecules

Davey, J. (2011) · doi:10.22215/etd/2011-06738

Determining Stable ligand Orientation Using Particle Swarm Optimization

Ghosh, A., Talukdar, M., & Roy, U. K. (2015) · International Journal of Artificial Intelligence & Applications · doi:10.5121/ijaia.2015.6508

Computer Science & Information Technology ( CS & IT )

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Drug Discovery

Ramakrishnan, G. (2017) · Translational medicine research · doi:10.1007/978-94-024-1045-7_1

Faculty Opinions recommendation of Diazepam-bound GABAA receptor models identify new benzodiazepine binding-site ligands.

Bajorath, J. (2012) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.14267089.15779222

Docking is not enough: 17-trifluoromethylphenyl trinor PGF2α is only a very weak ligand of neurokinin-1 receptor

Matalińska, J., & Lipiński, P. F. J. (2022) · Experimental and Molecular Pathology · doi:10.1016/j.yexmp.2022.104849

A Novel Atom Pair Attention Methodology for Molecular Representation LearningPreprint

Sun, H., Jiang, Y., Wang, M., Xiao, X., Wan, X., Tang, M., Jin, X., Niu, Z., & Chen, H. (2023) · Research Square · doi:10.21203/rs.3.rs-2418652/v1

Molecular modelling and machine learning for the investigation of 2-oxazolidinone ribosomal antibacterials

Buckley, M. E. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.242134

Insights into In Silico Methods to Explore Plant Bioactive Substances in Combating SARS-CoV-2

Pramanik, S., Seth, D., & Debnath, S. (2023) · Springer eBooks · doi:10.1007/978-981-99-3664-9_9

Comparative Analysis of Balanced Code Smell Detection Using Machine Learning

Sabharwal, M., Gupta, A., Gandhi, R., & Khan, I. U. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-5997-6_32

Structure-based Design for Medicinal Chemists

Blaney, J., & Davis, A. M. (2023) · Royal Society of Chemistry eBooks · doi:10.1039/9781788018982-00137

DoRIAT: A Bayesian framework for interpreting and annotating docking runsPreprint

Maniatis, C., Ouaray, Z., Xiao, C., Dixon, T. P. E., Naylor, C., Snowden, J., Teng, M. S., & Hurst, J. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.02.626325

In Silico Study of Bajakah Compounds (Spatholobus suberectus) to ProteaseSARS-CoV-2 Inhibitor

Norhayati, N., Andika, A., & Purwanto, A. (2025) · Jurnal Sains dan Kesehatan · doi:10.30872/jsk.v5i2.p78-89

Enzymes as Targets for Disease Control: From Discovery to Application

Brambila, B., Martelli, A. C. F. S., Rosa, A. E. M., Barcelos, M. P., Antão, S. C., Alexandrino, A. V., da Silva, C. H. T. D. P., & Novo‐Mansur, M. T. M. (2025) · Series in bioengineering · doi:10.1007/978-3-032-07366-2_8

Targeting the alpha-synuclein protein to treat Parkinson's disease using novel inhibitors identified using an integrated computational drug development approaches

Siddiqui, A. J., Jahan, S., Saxena, J., Sachidanandan, M., Badraoui, R., & Adnan, M. (2025) · In Silico Pharmacology · doi:10.1007/s40203-025-00477-4

Exploring promising pharmaceutical candidates for gastrointestinal disorders using In silico approaches

Singh, R. D., Vaishali, Deshwal, R. K., Jain, K., & Basant, N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-36372-6.00015-x

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

Best Practices for Docking-Based Virtual Screening

Neves, B. J., Mottin, M., Moreira‐Filho, J. T., Sousa, B. K. D. P., Mendonça, S. S., & Andrade, C. H. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822312-3.00001-1 · Cited by 24

Docking on trial

Kirkpatrick, P. N. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1862 · Cited by 4

Overcoming the accuracy–generalization tradeoff in docking and scoring for prospective virtual screeningPreprint

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