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An explainable deep learning platform for molecular discovery

Nature Protocols (2024)Cite this article

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Abstract

Deep learning approaches have been increasingly applied to the discovery of novel chemical compounds. These predictive approaches can accurately model compounds and increase true discovery rates, but they are typically black box in nature and do not generate specific chemical insights. Explainable deep learning aims to ‘open up’ the black box by providing generalizable and human-understandable reasoning for model predictions. These explanations can augment molecular discovery by identifying structural classes of compounds with desired activity in lieu of lone compounds. Additionally, these explanations can guide hypothesis generation and make searching large chemical spaces more efficient. Here we present an explainable deep learning platform that enables vast chemical spaces to be mined and the chemical substructures underlying predicted activity to be identified. The platform relies on Chemprop, a software package implementing graph neural networks as a deep learning model architecture. In contrast to similar approaches, graph neural networks have been shown to be state of the art for molecular property prediction. Focusing on discovering structural classes of antibiotics, this protocol provides guidelines for experimental data generation, model implementation and model explainability and evaluation. This protocol does not require coding proficiency or specialized hardware, and it can be executed in as little as 1–2 weeks, starting from data generation and ending in the testing of model predictions. The platform can be broadly applied to discover structural classes of other small molecules, including anticancer, antiviral and senolytic drugs, as well as to discover structural classes of inorganic molecules with desired physical and chemical properties.

Key points

  • This protocol enables the computational discovery of chemical compounds using a deep learning architecture called graph neural networks, which, given the chemical structure of any compound, can predict whether the compound has a property of interest.

  • The platform leverages explainable deep learning to facilitate the identification of structural classes of novel compounds. This approach guides hypothesis generation and makes searching large chemical spaces more efficient compared with previous approaches, which are typically black box in nature and do not generate specific chemical insights.

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Fig. 1: Overview of the protocol.
Fig. 2: Examples of training data.
Fig. 3: The process of modeling and expected inputs and outputs at each modeling step.
Fig. 4: Example results.
Fig. 5: Illustration of the MCTS.

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Data availability

Example datasets are available as Supplementary Information. The main datasets used in this protocol are subsets of data from a previously published study (ref. 7) identifying a structural class of antibiotics using explainable DL.

Code availability

Chemprop is available at https://github.com/chemprop/chemprop. A working example of the files provided as inputs and created as outputs of this protocol is available at https://github.com/felixjwong/protocol. Additional code from a previously published study, which includes Chemprop checkpoints for models trained on larger datasets, are available at https://github.com/felixjwong/antibioticsai and https://zenodo.org/records/10095879 (ref. 78). The code in this protocol has been peer reviewed.

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Acknowledgements

F.W. was supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number K25AI168451. A.K. was supported by the Swiss National Science Foundation under grant number SNSF_ 203071. J.J.C. was supported by the Defense Threat Reduction Agency (grant numbers HDTRA12210032 and HDTRA12210010), the National Institutes of Health (grant number R01-AI146194) and the Broad Institute of MIT and Harvard. This work is part of the Antibiotics-AI Project, which is directed by J.J.C. and supported by the Audacious Project, Flu Lab, LLC, the Sea Grape Foundation, Rosamund Zander and Hansjorg Wyss for the Wyss Foundation and an anonymous donor.

Author information

Authors and Affiliations

  1. Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Felix Wong, Satotaka Omori, Aarti Krishnan & James J. Collins

  2. Institute for Medical Engineering and Science and Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Felix Wong, Aarti Krishnan & James J. Collins

  3. Integrated Biosciences, Inc., Redwood City, CA, USA

    Felix Wong, Satotaka Omori, Alicia Li, Ryan S. Lach & Maxwell Z. Wilson

  4. Center for BioEngineering, University of California Santa Barbara, Santa Barbara, CA, USA

    Joseph Rufo & Maxwell Z. Wilson

  5. Biomolecular Science and Engineering Program, University of California Santa Barbara, Santa Barbara, CA, USA

    Joseph Rufo & Maxwell Z. Wilson

  6. Department of Molecular, Cellular, and Developmental Biology, University of California Santa Barbara, Santa Barbara, CA, USA

    Joseph Rufo & Maxwell Z. Wilson

  7. Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA, USA

    Joseph Rufo & Maxwell Z. Wilson

  8. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    James J. Collins

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Contributions

F.W. prepared the manuscript and supervised research. S.O., A.L., A.K., R.S.L., J.R. and M.Z.W. contributed to writing and validating the protocol steps. J.J.C. supervised research. All authors assisted with manuscript editing.

Corresponding author

Correspondence to James J. Collins.

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Competing interests

J.J.C. is an academic cofounder and Scientific Advisory Board chair of EnBiotix, an antibiotic drug discovery company and Phare Bio, a nonprofit venture focused on antibiotic drug development. J.J.C. is also an academic cofounder and board member of Cellarity and the founding Scientific Advisory Board chair of Integrated Biosciences. F.W. and M.Z.W. are cofounders of Integrated Biosciences. S.O., A.L. and R.S.L. contributed to this work as employees of Integrated Biosciences, and S.O. and R.S.L. may have equity interest in Integrated Biosciences. The remaining authors declare no competing interests.

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Nature Protocols thanks Octavio Franco and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references

Wong, F. et al. Nature 626, 177–185 (2024): https://doi.org/10.1038/s41586-023-06887-8

Wong, F. et al. Nat. Aging 3, 734–750 (2023): https://doi.org/10.1038/s43587-023-00415-z

Liu, G. et al. Nat. Chem. Biol. 19, 1342–1350 (2023): https://doi.org/10.1038/s41589-023-01349-8

Stokes, J. M. et al. Cell 180, 688–702.e13 (2020): https://doi.org/10.1016/j.cell.2020.01.021

Supplementary information

Supplementary Data 1

Sample training data for antibacterial activity against S. aureus RN4220.

Supplementary Data 2

Sample training data for cytotoxicity against HepG2 cells.

Supplementary Data 3

Sample training data for cytotoxicity against HSkM cells.

Supplementary Data 4

Sample training data for cytotoxicity against IMR-90 cells.

Supplementary Data 5

Sample test data for 100,000 compounds from a Broad Institute database.

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Wong, F., Omori, S., Li, A. et al. An explainable deep learning platform for molecular discovery. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-01084-x

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