AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

Particle swarm optimization

Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192

Adaptation in Natural and Artificial Systems

Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340

Optimization by Simulated Annealing

Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134

AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21256 · Cited by 26,294

A new optimizer using particle swarm theory

Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067

Numerical optimization

Braun, W. J., & Murdoch, D. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108993456.011 · Cited by 14,099

Numerical Optimization

Nocedal, J., & Wright, S. J. (1999) · Springer eBooks · doi:10.1007/b98874 · Cited by 9,563

ICM—A new method for protein modeling and design: Applications to docking and structure prediction from the distorted native conformation

Abagyan, R. A., Totrov, M. M., & Kuznetsov, D. (1994) · Journal of Computational Chemistry · doi:10.1002/jcc.540150503 · Cited by 1,718

The statistical-thermodynamic basis for computation of binding affinities: a critical review

Gilson, M. K., Given, J. A., Bush, B. L., & McCammon, J. A. (1997) · Biophysical Journal · doi:10.1016/s0006-3495(97)78756-3 · Cited by 1,222

Further development and validation of empirical scoring functions for structure-based binding affinity prediction

Wang, R., Lai, L., & Wang, S. (2002) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1016357811882 · Cited by 1,185

Accurate structure prediction of biomolecular interactions with AlphaFold 3

Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A. J., Bambrick, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07487-w · Cited by 15,861

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

Eberhardt, J., Santos‐Martins, D., Tillack, A. F., & Forli, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00203 · Cited by 8,292

UCSF ChimeraX: Meeting modern challenges in visualization and analysis

Goddard, T. D., Huang, C. C., Meng, E. C., Pettersen, E. F., Couch, G. S., Morris, J. H., & Ferrin, T. E. (2018) · Protein Science · doi:10.1002/pro.3235 · Cited by 5,267

Small-Molecule Library Screening by Docking with PyRx

Dallakyan, S., & Olson, A. J. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-2269-7_19 · Cited by 4,244

Molecular Docking: A Powerful Approach for Structure-Based Drug Discovery

Meng, X.-Y., Zhang, H.-X., Mezei, M., & Cui, M. (2011) · Current Computer Aided-Drug Design · doi:10.2174/157340911795677602 · Cited by 3,771

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025

Computational protein-ligand docking and virtual drug screening with the AutoDock suite

Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D., & Olson, A. (2016) · Nature Protocols · doi:10.1038/nprot.2016.051 · Cited by 2,536

Ligand docking and binding site analysis with PyMOL and Autodock/Vina

Seeliger, D., & de Groot, B. L. (2010) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-010-9352-6 · Cited by 2,648

Molecular Docking: Shifting Paradigms in Drug Discovery

Pinzi, L., & Rastelli, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20184331 · Cited by 2,537

CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting

Liu, Y., Yang, X., Gan, J., Chen, S., Xiao, Z. J., & Cao, Y. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac394 · Cited by 2,255

The rise of deep learning in drug discovery

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770

Software for molecular docking: a review

Pagadala, N. S., Syed, K., & Tuszynski, J. (2017) · Biophysical Reviews · doi:10.1007/s12551-016-0247-1 · Cited by 1,522

Computational approaches streamlining drug discovery

Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424

Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

Koes, D. R., Baumgartner, M. P., & Camacho, C. J. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300604z · Cited by 1,180

Cardiolipin externalization to the outer mitochondrial membrane acts as an elimination signal for mitophagy in neuronal cells

Chu, C. T., Ji, J., Dagda, R. K., Jiang, J. F., Tyurina, Y. Y., Kapralov, A. A., Tyurin, V. A., Yanamala, N., Shrivastava, I. H., Mohammadyani, D., et al. (2013) · Nature Cell Biology · doi:10.1038/ncb2837 · Cited by 1,083

Unravelling the Role of O-glycans in Influenza A Virus Infection

Mayr, J., Lau, K., Lai, J. C. C., Gagarinov, I. A., Shi, Y. Y., McAtamney, S., Chan, R. W. Y., Nicholls, J. M., von Itzstein, M., & Haselhorst, T. (2018) · Scientific Reports · doi:10.1038/s41598-018-34175-3 · Cited by 1,068

Generalized biomolecular modeling and design with RoseTTAFold All-Atom

Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., Morey-Burrows, F. S., Anishchenko, I., Humphreys, I. R., et al. (2024) · Science · doi:10.1126/science.adl2528 · Cited by 978

Comprehensive Evaluation of Ten Docking Programs on a Diverse Set of Protein-Ligand Complexes: The Prediction Accuracy of Sampling Power and Scoring Power

Wang, Z., Sun, H., Yao, X., Li, D., Xu, L., Li, Y., Tian, S., & Hou, T. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp01555g · Cited by 946

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

Ribavirin, Remdesivir, Sofosbuvir, Galidesivir, and Tenofovir against SARS-CoV-2 RNA dependent RNA polymerase (RdRp): A molecular docking study

Elfiky, A. A. (2020) · Life Sciences · doi:10.1016/j.lfs.2020.117592 · Cited by 929

CB-Dock: a web server for cavity detection-guided protein–ligand blind docking

Liu, Y., Grimm, M., Dai, W., Hou, M.-C., Xiao, Z. J., & Cao, Y. (2019) · Acta Pharmacologica Sinica · doi:10.1038/s41401-019-0228-6 · Cited by 927

Macromolecular modeling and design in Rosetta: recent methods and frameworks

Leman, J. K., Weitzner, B. D., Lewis, S. M., Adolf-Bryfogle, J., Alam, N., Alford, R. F., Aprahamian, M., Baker, D., Barlow, K. A., Barth, P., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0848-2 · Cited by 888

GNINA 1.0: molecular docking with deep learning

McNutt, A. T., Francoeur, P. G., Aggarwal, R., Masuda, T., Meli, R., Ragoza, M., Sunseri, J., & Koes, D. R. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00522-2 · Cited by 955

Comparative Assessment of Scoring Functions: The CASF-2016 Update

Su, M., Yang, Q., Du, Y., Feng, G., Liu, Z., Li, Y., & Wang, R. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00545 · Cited by 870

DOCK 6: Impact of new features and current docking performance

Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S. R., Moustakas, D. T., Lang, P. T., Case, D. A., Kuntz, I. D., & Rizzo, R. C. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23905 · Cited by 870

Protein–Ligand Scoring with Convolutional Neural Networks

Ragoza, M., Hochuli, J. E., Idrobo, E., Sunseri, J., & Koes, D. R. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00740 · Cited by 853

Deep learning for computational chemistry

Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809

Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model

Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.025 · Cited by 815

Structural insight into molecular mechanism of poly(ethylene terephthalate) degradation

Joo, S., Cho, I., Seo, H., Son, H. F., Sagong, H.-Y., Shin, T. J., Choi, S. Y., Lee, S. Y., & Kim, K. (2018) · Nature Communications · doi:10.1038/s41467-018-02881-1 · Cited by 816

Binding Affinity via Docking: Fact and Fiction

Pantsar, T., & Poso, A. (2018) · Molecules · doi:10.3390/molecules23081899 · Cited by 844

Broad-specificity efflux pumps and their role in multidrug resistance of Gram-negative bacteria

Nikaido, H., & Pagès, J. (2011) · FEMS Microbiology Reviews · doi:10.1111/j.1574-6976.2011.00290.x · Cited by 770

Role of computer-aided drug design in modern drug discovery

Macalino, S. J. Y., Gosu, V., Hong, S., & Choi, S. (2015) · Archives of Pharmacal Research · doi:10.1007/s12272-015-0640-5 · Cited by 762

Anti-HCV, nucleotide inhibitors, repurposing against COVID-19

Elfiky, A. A. (2020) · Life Sciences · doi:10.1016/j.lfs.2020.117477 · Cited by 719

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility

Ravindranath, P. A., Forli, S., Goodsell, D. S., Olson, A. J., & Sanner, M. F. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004586 · Cited by 700

Immune self-reactivity triggered by drug-modified HLA-peptide repertoire

Illing, P. T., Vivian, J. P., Dudek, N. L., Kostenko, L., Chen, Z., Bharadwaj, M., Miles, J. J., Kjer‐Nielsen, L., Gras, S., Williamson, N. A., et al. (2012) · Nature · doi:10.1038/nature11147 · Cited by 694

The kinase LYK5 is a major chitin receptor in Arabidopsis and forms a chitin-induced complex with related kinase CERK1

Cao, Y., Liang, Y., Tanaka, K., Nguyen, C. T., Jedrzejczak, R. P., Joachimiak, A., & Stacey, G. S. (2014) · eLife · doi:10.7554/elife.03766 · Cited by 707

Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences

Tsubaki, M., Tomii, K., & Sese, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty535 · Cited by 688

Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer’s Disease, Parkinson’s Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis

Nguyen, P. H., Ramamoorthy, A., Sahoo, B. R., Zheng, J., Faller, P., Straub, J. E., Domínguez, L., Shea, J., Dokholyan, N. V., Simone, A. D., et al. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01122 · Cited by 680

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

Sushko, I., Novotarskyi, S., Körner, R., Pandey, A. K., Rupp, M., Teetz, W., Brandmaier, S., Abdelaziz, A., Prokopenko, V. V., Tanchuk, V. Y., et al. (2011) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-011-9440-2 · Cited by 707

Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Salmaso, V., & Moro, S. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00923 · Cited by 719

An open-source drug discovery platform enables ultra-large virtual screens

Gorgulla, C., Boeszoermenyi, A., Wang, Z., Fischer, P. D., Coote, P. W., Das, K. M., Malets, Y. S., Radchenko, D. S., Moroz, Y. S., Scott, D. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2117-z · Cited by 677

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

Computer-aided drug design platform using PyMOL

Lill, M. A., & Danielson, M. L. (2010) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-010-9395-8 · Cited by 658

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

rDock: A Fast, Versatile and Open Source Program for Docking Ligands to Proteins and Nucleic Acids

Carmona, S. R., Álvarez-García, D., Foloppe, N., Garmendia-Doval, A. B., Juhos, S., Schmidtke, P., Barril, X., Hubbard, R. E., & Morley, S. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003571 · Cited by 660

Computational methods in drug discovery

Leelananda, S. P., & Lindert, S. (2016) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.12.267 · Cited by 675

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Sabe, V. T., Ntombela, T., Jhamba, L. A., Maguire, G. E. M., Govender, T., Naicker, T., & Kruger, H. G. (2021) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2021.113705 · Cited by 665

Nano-targeted induction of dual ferroptotic mechanisms eradicates high-risk neuroblastoma

Hassannia, B., Wiernicki, B., Ingold, I., Qu, F., Herck, S. V., Tyurina, Y. Y., Bayır, H., Abhari, B. A., Angeli, J. P. F., Choi, S. M., et al. (2018) · Journal of Clinical Investigation · doi:10.1172/jci99032 · Cited by 633

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

Hsin, K., Ghosh, S., & Kitano, H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083922 · Cited by 622

Progress in molecular docking

Fan, J., Fu, A., & Zhang, L. (2019) · Quantitative Biology · doi:10.1007/s40484-019-0172-y · Cited by 665

In silico methods and tools for drug discovery

Shaker, B., Ahmad, S., Lee, J., Jung, C., & Na, D. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104851 · Cited by 669

Class effects of SGLT2 inhibitors in mouse cardiomyocytes and hearts: inhibition of Na+/H+ exchanger, lowering of cytosolic Na+ and vasodilation

Uthman, L., Baartscheer, A., Bleijlevens, B., Schumacher, C. A., Fiolet, J. W. T., Koeman, A., Jancev, M., Hollmann, M. W., Weber, N. C., Coronel, R., et al. (2017) · Diabetologia · doi:10.1007/s00125-017-4509-7 · Cited by 633

Beware of Docking!

Chen, Y.-C. (2015) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2014.12.001 · Cited by 611

Computer-Aided Drug Design Methods

Yu, W., & MacKerell, A. D. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6634-9_5 · Cited by 599

Medicinal chemistry of indole derivatives: Current to future therapeutic prospectives

Kumari, A., & Singh, R. K. (2019) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2019.103021 · Cited by 610

Structure-Based Virtual Screening: From Classical to Artificial Intelligence

Maia, E. H. B., de Assis, L. C., de Oliveira, T. A., da Silva, A. M., & Taranto, A. G. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00343 · Cited by 623

SwissDock 2024: major enhancements for small-molecule docking with Attracting Cavities and AutoDock Vina

Bugnon, M., Röhrig, U. F., Goullieux, M., Perez, M. A. S., Daina, A., Michielin, O. A., & Zoete, V. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae300 · Cited by 639

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

Saikia, S., & Bordoloi, M. (2018) · Current Drug Targets · doi:10.2174/1389450119666181022153016 · Cited by 581

Cellular Red-Ox system in health and disease: The latest update

Jena, A. B., Samal, R. R., Bhol, N. K., & Duttaroy, A. K. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114606 · Cited by 587

A practical guide to large-scale docking

Bender, B. J., Gahbauer, S., Luttens, A., Lyu, J., Webb, C. M., Stein, R. M., Fink, E. A., Balius, T. E., Carlsson, J., Irwin, J. J., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00597-z · Cited by 546

Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides

Limón, A. M. V., Aguilar-Toalá, J. E., & Liceaga, A. M. (2022) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.1c06110 · Cited by 534

Glucose transporters in cancer – from tumor cells to the tumor microenvironment

Ancey, P., Contat, C., & Meylan, E. (2018) · FEBS Journal · doi:10.1111/febs.14577 · Cited by 531

AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

Wallach, I., Dzamba, M., & Heifets, A. (2015) · arXiv · doi:10.48550/arxiv.1510.02855

Fast docking using the CHARMM force field with EADock DSS

Grosdidier, A., Zoete, V., & Michielin, O. A. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21797 · Cited by 501

Targeting Antibiotic Resistance

Chellat, M. F., Raguž, L., & Riedl, R. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201506818 · Cited by 505

PotentialNet for Molecular Property Prediction

Feinberg, E. N., Sur, D., Wu, Z., Husic, B. E., Mai, H., Li, Y., Sun, S., Yang, J., Ramsundar, B., & Pande, V. S. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00507 · Cited by 490

The Intracellular B30.2 Domain of Butyrophilin 3A1 Binds Phosphoantigens to Mediate Activation of Human Vγ9Vδ2 T Cells

Sandstrom, A., Peigné, C.-M., Léger, A., Crooks, J. E., Gatto–Konczak, F. D., Gesnel, M.-C., Breathnach, R., Bonneville, M., Scotet, E., & Adams, E. J. (2014) · Immunity · doi:10.1016/j.immuni.2014.03.003 · Cited by 503

Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark

Gaillard, T. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00312 · Cited by 489

DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism

Bell, E. W., & Zhang, Y. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0362-7 · Cited by 521

The Art and Science of Molecular Docking

Paggi, J. M., Pandit, A., & Dror, R. O. (2024) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-030222-120000 · Cited by 546

Necrostatin-1 analogues: critical issues on the specificity, activity and in vivo use in experimental disease models

Takahashi, N., Duprez, L., Grootjans, S., Cauwels, A., Nerinckx, W., DuHadaway, J. B., Goossens, V, Roelandt, R., Van Hauwermeiren, F., Libert, C., et al. (2012) · Cell Death & Disease · doi:10.1038/cddis.2012.176 · Cited by 465

Uniform and stable immobilization of metal-organic frameworks into chitosan matrix for enhanced tetracycline removal from water

Zhao, R., Ma, T., Zhao, S., Rong, H., Tian, Y., & Zhu, G. (2019) · Chemical Engineering Journal · doi:10.1016/j.cej.2019.122893 · Cited by 460

Autodock Vina Adopts More Accurate Binding Poses but Autodock4 Forms Better Binding Affinity

Nguyen, N. T., Nguyen, T. H., Pham, T. N. H., Nguyen, H. T., Bay, M. V., Quân, P. M., Nam, P. C., Vu, V. V., & Ngô, S. T. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00778 · Cited by 461

Accelerating A uto D ock 4 with GPUs and Gradient-Based Local Search

Santos‐Martins, D., Solis-Vasquez, L., Tillack, A. F., Sanner, M. F., Koch, A., & Forli, S. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01006 · Cited by 480

Natural product-derived phytochemicals as potential agents against coronaviruses: A review

Mani, J. S., Johnson, J. B., Steel, J. C., Broszczak, D. A., Neilsen, P. M., Walsh, K. B., & Naiker, M. (2020) · Virus Research · doi:10.1016/j.virusres.2020.197989 · Cited by 451

MCC950 closes the active conformation of NLRP3 to an inactive state

Tapia‐Abellán, A., Angosto-Bazarra, D., Martínez‐Banaclocha, H., de Torre‐Minguela, C., Cerón‐Carrasco, J. P., Pérez‐Sánchez, H., Aróstegui, J. I., & Pelegrı́n, P. (2019) · Nature Chemical Biology · doi:10.1038/s41589-019-0278-6 · Cited by 448

Accessing non-natural reactivity by irradiating nicotinamide-dependent enzymes with light

Emmanuel, M. A., Greenberg, N. R., Oblinsky, D. G., & Hyster, T. K. (2016) · Nature · doi:10.1038/nature20569 · Cited by 449

Vitamin D-Mediated Hypercalcemia: Mechanisms, Diagnosis, and Treatment

Tebben, P. J., Singh, R. J., & Kumar, R. (2016) · Endocrine Reviews · doi:10.1210/er.2016-1070 · Cited by 443

Curcumin-loaded biodegradable polymeric micelles for colon cancer therapy in vitro and in vivo

Gou, M., Men, K., Shi, H., Xiang, M., Zhang, J., Song, J. T., Long, J., Wan, Y., Luo, F., Zhao, X., et al. (2011) · Nanoscale · doi:10.1039/c0nr00758g · Cited by 437

An Overview of Scoring Functions Used for Protein–Ligand Interactions in Molecular Docking

Li, J., Fu, A., & Zhang, L. (2019) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-019-00327-w · Cited by 468

Prediction of the SARS-CoV-2 (2019-nCoV) 3C-like protease (3CLpro) structure: virtual screening reveals velpatasvir, ledipasvir, and other drug repurposing candidates

Chen, Y. W., Yiu, C.-P. B., & Wong, K. (2020) · F1000Research · doi:10.12688/f1000research.22457.2 · Cited by 432

Lassomycin, a Ribosomally Synthesized Cyclic Peptide, Kills Mycobacterium tuberculosis by Targeting the ATP-Dependent Protease ClpC1P1P2

Gavrish, E., Sit, C. S., Cao, S., Kandror, O., Spoering, A. L., Peoples, A. J., Ling, L. L., Fetterman, A., Hughes, D. E., Bissell, A., et al. (2014) · Chemistry & Biology · doi:10.1016/j.chembiol.2014.01.014 · Cited by 418

Pharmit: interactive exploration of chemical space

Sunseri, J., & Koes, D. R. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw287 · Cited by 426

α-Glucosidase inhibition by flavonoids: an in vitro and in silico structure–activity relationship study

Proença, C., Freitas, M., Ribeiro, D., Oliveira, E. F. T., Sousa, J. L. C., Tomé, S. M., Ramos, M. J., Silva, A. M. S., Fernandes, P. A., & Fernandes, E. (2017) · Journal of Enzyme Inhibition and Medicinal Chemistry · doi:10.1080/14756366.2017.1368503 · Cited by 432

PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences

Buttenschoen, M., Morris, G. M., & Deane, C. M. (2023) · Chemical Science · doi:10.1039/d3sc04185a · Cited by 500

Differential α-amylase/α-glucosidase inhibitory activities of plant-derived phenolic compounds: a virtual screening perspective for the treatment of obesity and diabetes

Rasouli, H., Ghazvini, S. M. B. H., Adibi, H., & Khodarahmi‬, R. (2017) · Food & Function · doi:10.1039/c7fo00220c · Cited by 422

Vinardo: A Scoring Function Based on Autodock Vina Improves Scoring, Docking, and Virtual Screening

Quiroga, R., & Villarreal, M. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0155183 · Cited by 428

Predicting Drug–Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation

Lim, J., Ryu, S., Park, K., Choe, Y. J., Ham, J., & Kim, W. Y. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00387 · Cited by 411

The Enzymatic Oxidation of Graphene Oxide

Kotchey, G. P., Allen, B. L., Vedala, H., Yanamala, N. V. K., Kapralov, A. A., Tyurina, Y. Y., Klein‐Seetharaman, J., Kagan, V. E., & Star, A. (2011) · ACS Nano · doi:10.1021/nn103265h · Cited by 407

AMDock: a versatile graphical tool for assisting molecular docking with Autodock Vina and Autodock4

Tresanco, M. S. V., Valdés‐Tresanco, M. E., Valiente, P. A., & Moreno, E. (2020) · Biology Direct · doi:10.1186/s13062-020-00267-2 · Cited by 399

Quantum computational, spectroscopic investigations on N-(2-((2-chloro-4,5-dicyanophenyl)amino)ethyl)-4-methylbenzenesulfonamide by DFT/TD-DFT with different solvents, molecular docking and drug-likeness researches

Dege, N., Gökçe, H., Doĝan, O. E., Alpaslan, G., Ağar, T., Muthu, S., & Sert, Y. (2022) · Colloids and Surfaces A Physicochemical and Engineering Aspects · doi:10.1016/j.colsurfa.2022.128311 · Cited by 397

Boosting docking-based virtual screening with deep learning.

Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395

Rice cytochrome P450 MAX1 homologs catalyze distinct steps in strigolactone biosynthesis

Zhang, Y., van Dijk, A. D. J., Scaffidi, A., Flematti, G. R., Hofmann, M., Charnikhova, T. V., Verstappen, F. W. A., Hepworth, J., van der Krol, S., Leyser, O., et al. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1660 · Cited by 396

Machine-learning methods for ligand–protein molecular docking

Crampon, K., Giorkallos, A., Deldossi, M., Baud, S., & Steffenel, L. A. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.09.007 · Cited by 420

Roseburia intestinalis generated butyrate boosts anti-PD-1 efficacy in colorectal cancer by activating cytotoxic CD8 + T cells

Kang, X., Liu, C., Ding, Y., Ni, Y., Ji, F., Lau, H. C. H., Jiang, L., Sung, J. J., Wong, S. H., & Yu, J. (2023) · Gut · doi:10.1136/gutjnl-2023-330291 · Cited by 394

Deep learning in drug discovery: an integrative review and future challenges

Askr, H., Elgeldawi, E., Ella, H. A., Elshaier, Y. A. M. M., Gomaa, M. M., & Hassanien, A. E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10306-1 · Cited by 428

Computational approaches in target identification and drug discovery

Κάτσιλα, Θ., Spyroulias, G. A., Patrinos, G. P., & Matsoukas, M. (2016) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.04.004 · Cited by 408

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

Currin, A., Swainston, N., Day, P. J., & Kell, D. B. (2014) · Chemical Society Reviews · doi:10.1039/c4cs00351a · Cited by 393

Celastrol induces ferroptosis in activated HSCs to ameliorate hepatic fibrosis via targeting peroxiredoxins and HO-1

Luo, P., Liu, D., Zhang, Q., Yang, F., Wong, Y., Xia, F., Zhang, J., Chen, J., Tian, Y., Yang, C., et al. (2021) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2021.12.007 · Cited by 394

Ligand-binding properties of a juvenile hormone receptor, Methoprene-tolerant

Charles, J., Iwema, T., Epa, V. C., Takaki, K., Ryneš, J., & Jindra, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116123109 · Cited by 394

1 Structural bioinformatics Fast, Accurate, and Reliable Molecular Docking with QuickVina 2

Alhossary, A., Handoko, S. D., & Mu, Y. (2016)

Fast, accurate, and reliable molecular docking with QuickVina 2

Alhossary, A., Handoko, S. D., Mu, Y., & Kwoh, C. K. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv082 · Cited by 399

QuickVina: Accelerating AutoDock Vina Using Gradient-Based Heuristics for Global Optimization

Handoko, S. D., Ouyang, X., Su, C. T.-T., Kwoh, C. K., & Ong, Y.-S. (2012) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2012.82 · Cited by 69

Accelerating AutoDock Vina with GPUs

Tang, S., Chen, R., Mengru, L., Qingde, L., Zhu, Y., Ding, J., Wu, J., Haifeng, H., & Ming, L. (2022) · Molecules · doi:10.3390/molecules27093041 · Cited by 134

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings

Eberhardt, J., Santos‐Martins, D., Tillack, A. F., & Forli, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00203 · Cited by 8,292