NeuralDock: Rapid and Conformation-Agnostic Docking of Small Molecules
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Abstract
Virtual screening is a cost- and time-effective alternative to traditional high-throughput screening in the drug discovery process. Both virtual screening approaches, structure-based molecular docking and ligand-based cheminformatics, suffer from computational cost, low accuracy, and/or reliance on prior knowledge of a ligand that binds to a given target. Here, we propose a neural network framework, NeuralDock, which accelerates the process of high-quality computational docking by a factor of 10 6 , and does not require prior knowledge of a ligand that binds to a given target. By approximating both protein-small molecule conformational sampling and energy-based scoring, NeuralDock accurately predicts the binding energy, and affinity of a protein-small molecule pair, based on protein pocket 3D structure and small molecule topology. We use NeuralDock and 25 GPUs to dock 937 million molecules from the ZINC database against superoxide dismutase-1 in 21 h, which we validate with physical docking using MedusaDock. Due to its speed and accuracy, NeuralDock may be useful in brute-force virtual screening of massive chemical libraries and training of generative drug models.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412 · see citing works
(2014) · Cited by 33,419 · see citing works
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(00)00129-0 · Cited by 18,139 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460 · see citing works
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 · see citing works
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 · see citing works
Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2012) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2012.09.019 · Cited by 3,720 · see citing works
Spectral Normalization for Generative Adversarial Networks
Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018) · arXiv · doi:10.48550/arxiv.1802.05957 · Cited by 3,438 · see citing works
Innovation in the pharmaceutical industry: New estimates of R&D costs
DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016) · Journal of Health Economics · doi:10.1016/j.jhealeco.2016.01.012 · Cited by 3,359 · see citing works
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. (2020) · UTS ePRESS (University of Technology Sydney) · Cited by 3,174 · see citing works
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 · see citing works
Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Mureşan, S., & Hopkins, A. L. (2012) · Nature Chemistry · doi:10.1038/nchem.1243 · Cited by 2,301 · see citing works
Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python
Contributors, S. 1. 0., Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T. J., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0772-5 · Cited by 1,848 · see citing works
Pingouin: statistics in Python
Vallat, R. (2018) · Journal of Open Source Software · doi:10.21105/joss.01026 · Cited by 1,780 · see citing works
Reconciling modern machine-learning practice and the classical bias–variance trade-off
Belkin, M., Hsu, D., Ma, S., & Mandal, S. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1903070116 · Cited by 1,595 · see citing works
An analysis of the attrition of drug candidates from four major pharmaceutical companies
Waring, M. J., Arrowsmith, J., Leach, A. R., Leeson, P. D., Mandrell, S., Owen, R. M., Pairaudeau, G., Pennie, W. D., Pickett, S. D., Wang, J., et al. (2015) · Nature Reviews Drug Discovery · doi:10.1038/nrd4609 · Cited by 1,450 · see citing works
Self-citationWang, R., Fang, X., Lu, Y., & Wang, S. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm030580l · Cited by 1,137 · see citing works
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 923 · see citing works
DOCK 6: Impact of New Features and Current Docking Performance
Allen, W. J., Balius, T. E., Mukherjee, S., Brozell, S., Moustakas, D., Lang, P. T., Case, D., Kuntz, I., & Rizzo, R. C. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23905 · Cited by 731 · see citing works
DNA-encoded chemistry: enabling the deeper sampling of chemical space
Goodnow, R. A., Dumelin, C. E., & Keefe, A. D. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.213 · Cited by 621 · see citing works
Deep double descent: where bigger models and more data hurt*
Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., & Sutskever, I. (2021) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/ac3a74 · Cited by 565 · see citing works
High drug attrition rates—where are we going wrong?
Hutchinson, L., & Kirk, R. (2011) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2011.34 · Cited by 517 · see citing works
Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery
Gentile, F., Agrawal, V., Hsing, M., Ton, A., Ban, F., Norinder, U., Gleave, M. E., & Cherkasov, A. R. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00229 · Cited by 455 · see citing works
Francoeur, P. G., Masuda, T., Sunseri, J., Jia, A., Iovanisci, R. B., Snyder, I. M., & Koes, D. R. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00411 · Cited by 353 · see citing works
Cang, Z., Mu, L., & Wei, G.-W. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005929 · Cited by 257 · see citing works
Goodsell, D. S., Sanner, M. F., Olson, A. J., & Forli, S. (2020) · Protein Science · doi:10.1002/pro.3934 · Cited by 239 · see citing works
Use of Artificial Neural Network in Pattern Recognition
April, A. A., Basu, J. K., Bhattacharyya, D., & Kim, T.-H. (2010) · International Journal of Software Engineering and Its Applications · Cited by 237 · see citing works
Fragment Quantum Mechanical Calculation of Proteins and Its Applications
He, X. D., Zhu, T., Wang, X., Liu, J., & Zhang, J. Z. H. (2014) · Accounts of Chemical Research · doi:10.1021/ar500077t · Cited by 209 · see citing works
MedusaScore: An Accurate Force Field-Based Scoring Function for Virtual Drug Screening
Yin, S., Biedermannová, L., Vondrášek, J., & Dokholyan, N. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci8001167 · Cited by 204 · see citing works
Self-citationFinzi, M., Stanton, S., Izmailov, P., & Wilson, A. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.12880 · Cited by 123 · see citing works
Rapid Flexible Docking Using a Stochastic Rotamer Library of Ligands
Ding, F., Yin, S., & Dokholyan, N. V. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100218t · Cited by 90 · see citing works
Self-citationMedusaDock 2.0: Efficient and Accurate Protein–Ligand Docking With Constraints
Wang, J., & Dokholyan, N. V. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00905 · Cited by 73 · see citing works
Self-citationTrends in Modern Drug Discovery
Eder, J., & Herrling, P. L. (2015) · Handbook of experimental pharmacology · doi:10.1007/164_2015_20 · Cited by 53 · see citing works
Ensemble-based docking: From hit discovery to metabolism and toxicity predictions
Evangelista, W., Weir, R. L., Ellingson, S. R., Harris, J. B., Kapoor, K., Smith, J. C., & Baudry, J. Y. (2016) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2016.07.064 · Cited by 48 · see citing works
GPU-Accelerated Flexible Molecular Docking
Fan, M., Wang, J., Jiang, H., Feng, Y., Mahdavi, M., Madduri, K., Kandemir, M., & Dokholyan, N. V. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c09051 · Cited by 32 · see citing works
Self-citationExploring End-to-End Differentiable Natural Logic Modeling
Feng, Y., Zheng, Z., Liu, Q., Greenspan, M., & Zhu, X. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.101 · Cited by 12 · see citing works
Yin, S., Biedermannová, L., Vondrášek, J., & Dokholyan, N. V. (2008) · ChemInform · doi:10.1002/chin.200848214 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
2 of the 48 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR
Tropsha, A., Isayev, O., Varnek, A. A., Schneider, G., & Cherkasov, A. R. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00832-0 · Cited by 423 · see citing works
Doostmohammadi, A., Jooya, H., Ghorbanian, K., Gohari, S., & Dadashpour, M. (2024) · Cell Communication and Signaling · doi:10.1186/s12964-024-01607-9 · Cited by 207 · see citing works
Seal, S., Mahale, M., García-Ortegón, M., Joshi, C. K., Hosseini-Gerami, L., Beatson, A., Greenig, M., Shekhar, M., Patra, A., Weis, C., et al. (2025) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.5c00033 · Cited by 159 · see citing works
Deep Learning Model for Efficient Protein–Ligand Docking with Implicit Side-Chain Flexibility
Masters, M. R., Mahmoud, A. H., Yao, W., & Lill, M. A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01436 · Cited by 59 · see citing works
de Azevedo, W. F. (2024) · Current Medicinal Chemistry · doi:10.2174/0109298673307315240730042209 · Cited by 22 · see citing works
Amorós, M. A., Choi, E. S., Cofré, A. H. R., Dokholyan, N. V., & Duzzioni, M. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.962881 · Cited by 11 · see citing works
Self-citationVoitsitskyi, T., Koleiev, I., Stratiichuk, R., Kot, O. I., Kyrylenko, R., Savchenko, I., Husak, V., Yesylevskyy, S. O., Starosyla, S. A., & Нафієв, А. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02777 · Cited by 8 · see citing works
Brezinova, M., Brotzakis, Z. F., Horne, R. I., Chowdhury, V. R., Gregory, R. C., Bian, Y., Díaz, A. G., Gentile, F., & Vendruscolo, M. (2025) · Molecular Systems Biology · doi:10.1038/s44320-025-00159-5 · Cited by 2 · see citing works
Lin, H., Vogel, H., & Zhang, H. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27073142 · Cited by 5 · see citing works
- Authors
- Sha, C. M., Wang, J., & Dokholyan, N. V.
- Affiliations
- Pennsylvania State University (US)
- Corresponding author
- Dokholyan, Nikolay V.
- Venue
- Frontiers in Molecular Biosciences
- Publisher
- Frontiers Media SA
- Published
- 2022-03-22
- Volume
- 9
- Pages
- 867241-867241
- ISSN
- 2296-889X (ISSN-L)
- PMC
- PMC8980736
- OpenAlex
- W4220695049
- Cited by
- 9
- References
- 48
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 2 · 2023: 2 · 2022: 1
- FWCI
- 0.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Molecular Biosciences (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- virtual screening, superoxide dismutase 1, generative drug design, GPU acceleration
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