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A multilevel generative framework with hierarchical self-contrasting for bias control and transparency in structure-based ligand design

A preprint version of the article is available at arXiv.

Abstract

Generative models for structure-based molecular design hold considerable promise for drug discovery, with the potential to speed up the hit-to-lead development cycle while improving the quality of drug candidates and reducing costs. Data sparsity and bias are, however, the two main roadblocks to the development of three-dimensionally aware models. Here we propose a training protocol based on multilevel self-contrastive learning for improved bias control and data efficiency. The framework leverages the large data resources available for two-dimensional generative modelling with datasets of ligand–protein complexes, resulting in hierarchical generative models that are topologically unbiased, explainable and customizable. We show how, by deconvolving the generative posterior into chemical, topological and structural context factors, we not only avoid common pitfalls in the design and evaluation of generative models, but also gain detailed insight into the generative process itself. This improved transparency considerably aids method development and allows fine-grained control over novelty versus familiarity.

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Fig. 1: Generative framework and PQR learning approach.
Fig. 2: Vocabulary shift across molecular libraries.
Fig. 3: Baseline-corrected enrichment in the PQR model.
Fig. 4: Visualization of the posterior.
Fig. 5: Analysis of experimental SARs.

Data availability

The datasets used in this study are publicly available, for pointers see https://github.com/capoe/libpqr/tree/master/data (repository https://doi.org/10.5281/ZENODO.6827338; ref. 58).

Code availability

The source code and pre-trained models can be accessed at https://github.com/capoe/libpqr (ref. 58).

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Acknowledgements

L.C. acknowledges funding from Astex through the Sustaining Innovation Postdoctoral Program. We thank C. Murray and D. Branduardi for thoughtful comments on the manuscript, and L. Colwell for fruitful discussions.

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C.P. and M.V. conceived the project. C.P. developed the PQR formalism. L.C. and C.P. developed the code, ran the experiments, performed the data analysis and wrote the paper. R.K. contributed to data preprocessing and visualization. All authors contributed to discussions and to the preparation of the manuscript.

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Correspondence to Carl Poelking.

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Nature Machine Intelligence thanks Jannis Born and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Discussion, Figs. 1–4 and Tables 1–6.

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Chan, L., Kumar, R., Verdonk, M. et al. A multilevel generative framework with hierarchical self-contrasting for bias control and transparency in structure-based ligand design. Nat Mach Intell 4, 1130–1142 (2022). https://doi.org/10.1038/s42256-022-00564-7

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