Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking

Francesco Gentile, Jean Charle Yaacoub, James Gleave, Michael Fernandez, Anh-Tien Ton, Fuqiang Ban, Abraham Stern, Artem Cherkasov

Published: 2022-02-03 DOI: 10.1038/s41596-021-00659-2

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2, Supplementary Figs. 1 and 2 and Supplementary References.

evaluation.csv file obtained from evaluating different training sizes in one DD iteration, screening the ZINC20 library against the AR dimerization site (PDB ID: 1R4I ; ref. 40) using Glide SP for docking and a recall of 0.90. Validation and test sets comprised 700,000 molecules each.

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Lyu, J. et al. Ultra-large library docking for discovering new chemotypes. Nature 566, 224–229 (2019).
Gorgulla, C. et al. An open-source drug discovery platform enables ultra-large virtual screens. Nature 580, 663–668 (2020).
Stein, R. M. et al. Virtual discovery of melatonin receptor ligands to modulate circadian rhythms. Nature 579, 609–614 (2020).
Grygorenko, O. O. et al. Generating multibillion chemical space of readily accessible screening compounds. iScience 23, 101681 (2020).
Acharya, A. et al. Supercomputer-based ensemble docking drug discovery pipeline with application to Covid-19. J. Chem. Inf. Model. 60, 5832–5852 (2020).
Shoichet, B. K. Virtual screening of chemical libraries. Nature 432, 862–865 (2004).
Cherkasov, A., Ban, F., Li, Y., Fallahi, M. & Hammond, G. L. Progressive docking: a hybrid QSAR/docking approach for accelerating in silico high throughput screening. J. Med. Chem. 49, 7466–7478 (2006).
Svensson, F., Norinder, U. & Bender, A. Improving screening efficiency through iterative screening using docking and conformal prediction. J. Chem. Inf. Model. 57, 439–444 (2017).
Ahmed, L. et al. Efficient iterative virtual screening with Apache Spark and conformal prediction. J. Cheminform. 10, 8 (2018).
Gentile, F. et al. Deep Docking: a deep learning platform for augmentation of structure based drug discovery. ACS Cent. Sci. 6, 939–949 (2020).
Sterling, T. & Irwin, J. J. ZINC 15—ligand discovery for everyone. J. Chem. Inf. Model. 55, 2324–2337 (2015).
McGann, M. FRED pose prediction and virtual screening accuracy. J. Chem. Inf. Model. 51, 578–596 (2011).
Friesner, R. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem. 47, 1739–1749 (2004).
Ton, A.-T., Gentile, F., Hsing, M., Ban, F. & Cherkasov, A. Rapid identification of potential inhibitors of SARS-CoV-2 main protease by deep docking of 1.3 billion compounds. Mol. Inf. 39, e2000028 (2020).
Muratov, E. N. et al. A critical overview of computational approaches employed for COVID-19 drug discovery. Chem. Soc. Rev. 50, 9121–9151 (2021).
Gentile, F., Ton, A.-T., Mslati, H., Ban, F. & Cherkasov, A. Discovery of SARS-CoV-2 main protease inhibitors through Deep Docking of 1.36 billion compounds. in 26th Congress of the European Society of Biomechanics (European Society of Biomechanics, 2021).
Rossetti, G. G. et al. Identification of low micromolar SARS-CoV-2 Mpro inhibitors from hits identified by in silico screens. Preprint at bioRxiv https://doi.org/10.1101/2020.12.03.409441(2020).
Jastrzębski, S. et al. Emulating docking results using a deep neural network: a new perspective for virtual screening. J. Chem. Inf. Model. 60, 4246–4262 (2020).
Al Saadi, A. et al. IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads. in ACM International Conference Proceeding Series (Association for Computing Machinery, 2021); https://doi.org/10.1145/3472456.3473524
Berenger, F., Kumar, A., Zhang, K. Y. J. & Yamanishi, Y. Lean-docking: exploiting ligands’ predicted docking scores to accelerate molecular docking. J. Chem. Inf. Model. 61, 2341––2352 (2021).
Graff, D. E., Shakhnovich, E. I. & Coley, C. W. Accelerating high-throughput virtual screening through molecular pool-based active learning. Chem. Sci. 12, 7866–7881 (2021).
Yang, Y. et al. Efficient exploration of chemical space with docking and deep-learning. Preprint at https://chemrxiv.org/engage/chemrxiv/article-details/60c755bf842e65adc6db4393 (2021).
Sessions, Z. et al. Recent progress on cheminformatics approaches to epigenetic drug discovery. Drug Discov. Today 25, 2268–2276 (2020).
Coley, C. W. Defining and exploring chemical spaces. Trends Chem. 3, 133–145 (2021).
Irwin, J. J. et al. ZINC20—a free ultralarge-scale chemical database for ligand discovery. J. Chem. Inf. Model. 60, 6065–6073 (2020).
Enamine. REAL Database https://enamine.net/library-synthesis/real-compounds/real-database# (2021).
Enamine. REAL Space https://enamine.net/compound-collections/real-compounds/real-space-navigator (2021).
Hawkins, P. C. D., Skillman, A. G., Warren, G. L., Ellingson, B. A. & Stahl, M. T. Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J. Chem. Inf. Model. 50, 572–584 (2010).
The RDKit Documentation—The RDKit 2020.03.1 Documentation. https://www.rdkit.org/docs/ (2020).
QUACPAC 2.0.2.2. (OpenEye Scientific Software, 2019).
O’Boyle, N. M. et al. Open Babel: an open chemical toolbox. J. Cheminform. 3, 33 (2011).
Kochev, N. T., Paskaleva, V. H. & Jeliazkova, N. Ambit-Tautomer: an open source tool for tautomer generation. Mol. Inf. 32, 481–504 (2013).
Morgan, H. L. The generation of a unique machine description for chemical structures—a technique developed at Chemical Abstracts Service. J. Chem. Doc. 5, 107–113 (1965).
Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742–754 (2010).
Extended Connectivity Fingerprint ECFP https://docs.chemaxon.com/display/docs/extended-connectivity-fingerprint-ecfp.md (ChemAxon, 2021).
Maestro v9.3. (Schrödinger, 2019).
Molecular Operating Environment 2019 (Chemical Computing Group, 2019).
Moustakas, D. T. et al. Development and validation of a modular, extensible docking program: DOCK 5. J. Comput. Aided Mol. Des. 20, 601–619 (2006).
Shaffer, P. L., Jivan, A., Dollins, D. E., Claessens, F. & Gewirth, D. T. Structural basis of androgen receptor binding to selective androgen response elements. Proc. Natl Acad. Sci. USA. 101, 4758–4763 (2004).
Santos-Martins, D. et al. Accelerating AutoDock4 with GPUs and gradient-based local search. J. Chem. Theory Comput. 17, 1060–1073 (2021).
Alhossary, A., Handoko, S. D., Mu, Y. & Kwoh, C.-K. Fast, accurate, and reliable molecular docking with QuickVina 2. Bioinformatics 31, 2214–2216 (2015).
Abagyan, R., Totrov, M. & Kuznetsov, D. ICM—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J. Comput. Chem. 15, 488–506 (1994).
Neves, M. A. C., Totrov, M. & Abagyan, R. Docking and scoring with ICM: the benchmarking results and strategies for improvement. J. Comput. Aided Mol. Des. 26, 675–686 (2012).
Giga Docking Guide—Orion Programming Guide. 1.0 documentation https://docs.eyesopen.com/orion-developer/2020-2-1/modules/large-scale-floes/docs/source/giga_docking_guide.html (OpenEye Software, 2020).
LeGrand, S. et al. GPU-accelerated drug discovery with docking on the Summit supercomputer: porting, optimization, and application to COVID-19 research. Preprint at https://arxiv.org/abs/2007.03678 (2020).
Bender, B. J. et al. A practical guide to large-scale docking. Nat. Protoc. 16, 4799–4832 (2021).
Jorgensen, W. L. The many roles of computation in drug discovery. Science 303, 1813–1818 (2004).
OEDOCKING v3.3.0.3 (OpenEye Scientific Software, 2021).
Abadi, M. et al. TensorFlow: a system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016 265–283 (The USENIX Association, 2016).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2012).
Berman, H. M. The Protein Data Bank. Nucleic Acids Res 28, 235–242 (2000).
Morris, G. M. et al. Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19, 1639–1662 (1998).
Melo, F. Area under the ROC curve. in Encyclopedia of Systems Biology (eds. Dubitzky, W. et al.) 38–39 (Springer, 2013).
Hur, E. et al. Recognition and accommodation at the androgen receptor coactivator binding interface. PLoS Biol. 2, E274 (2004).
Melo, F. Receiver operating characteristic (ROC) curve. in Encyclopedia of Systems Biology (eds. Dubitzky, W. et al.) 1818–1823 (Springer, 2013).
Shen, Z. et al. Design of SARS-CoV-2 PLpro inhibitors for COVID-19 antiviral therapy leveraging binding cooperativity. J. Med. Chem. https://doi.org/10.1021/acs.jmedchem.1c01307 (2021).

Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking

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