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A geometric deep learning approach to predict binding conformations of bioactive molecules

Nature Machine Intelligence volume 3, pages 1033–1039 (2021)Cite this article

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A preprint version of the article is available at ChemRxiv.

Abstract

Understanding the interactions formed between a ligand and its molecular target is key to guiding the optimization of molecules. Different experimental and computational methods have been applied to better understanding these intermolecular interactions. Here we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. The model learns a statistical potential based on the distance likelihood, which is tailor-made for each ligand–target pair. This potential can be coupled with global optimization algorithms to reproduce the experimental binding conformations of ligands. We show that the potential based on distance likelihood, described here, performs similarly or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.

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Fig. 1: Deep learning model used to learn a potential to predict binding conformations.
Fig. 2: Results for the distance likelihood potential in the CASF-2016 benchmark compared with other scoring functions.
Fig. 3: Use of distance likelihood potential to predict ligand-binding conformations.

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Data availability

The data that support the findings of this study are available in figshare with identifier https://doi.org/10.6084/m9.figshare.c.540732938. Source data are provided with this paper.

Code availability

The code used to generate the results shown in this study is available under an MIT Licence in the repository https://github.com/OptiMaL-PSE-Lab/DeepDock and https://doi.org/10.5281/zenodo.551020339.

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Acknowledgements

We thank D. Van Rompaey, J. Verhoeven and N. Dyubankova for supporting this project. We also appreciate comments from W. Heyndrickx that improved the manuscript.

Author information

Authors and Affiliations

  1. Generative AI Team, High Dimensional Biology and Discovery Data Sciences, Janssen Research & Development, Janssen Pharmaceutica NV, Beerse, Belgium

    Oscar Méndez-Lucio, Mazen Ahmad & Jörg Kurt Wegner

  2. Centre for Process Systems Engineering (CPSE), Department of Chemical Engineering, Imperial College London, London, UK

    Ehecatl Antonio del Rio-Chanona

Authors
  1. Oscar Méndez-Lucio
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  2. Mazen Ahmad
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  3. Ehecatl Antonio del Rio-Chanona
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  4. Jörg Kurt Wegner
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Contributions

O.M.-L. conceived the idea, wrote the code, performed the experiments and wrote the manuscript. M.A., E.A.d.R.-C. and J.K.W. helped with the preparation of the manuscript and with insightful discussions. E.A.d.R.-C helped to improve the code.

Corresponding authors

Correspondence to Oscar Méndez-Lucio or Jörg Kurt Wegner.

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Competing interests

O.M.L., M.A. and J.K.W. are employees of Janssen Pharmaceutica NV.

Additional information

Peer review information Nature Machine Intelligence thanks Matteo Aldeghi, Matteo Degiacomi and Hannah E. Bruce Macdonald for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Plot representing the Spearman correlation between RMSD and score for DeepDock and 34 frequently used scoring functions reported by Su et al30.

The x axis represents the ranges [0 to 2 Å], [0 to 3 Å], [0 to 4 Å], etc. Most scoring functions present a high correlation for conformations that are similar to the experimental pose (that is RMSD < 6 Å) but as the RMSD increases Spearman correlation decreases. DeepDock is the only scoring function that presents high Spearman correlation (0.83) taking into account all conformations with and RMSD between 0 and 10 Å.

Source data

Extended Data Fig. 2 DeepDock and 34 frequently used scoring functions reported by Su et al30.

Enhancement factor (EF) obtained for The EF measures the number of true binders among the top1% ranked conformations respect to the number of true binders for each of the 57 protein targets during the forward screening task. The red line indicates the mean EF for the scoring function and the bar represents the 90% confidence.

Source data

Extended Data Fig. 3 Comparison of real and predicted dihedral angles.

We show the distribution of the12 most common torsions (for example C-C-C-C) using all compounds in the training set predicted with an RMSD < = 1 Å. These plots compare the experimental and predicted dihedral angles for all rotatable bonds used during the optimization step.

Source data

Extended Data Fig. 4 Scatter plots summarizing the results of predicting the binding conformation for 1,367 compounds in the validation set.

a-b, show the correlation between the score of the predicted conformation vs the score of the real conformation. c-d, show that predicted conformations for compounds with less rotatable bonds present lower RMSD. e-f, show that compounds with less than 40 atoms usually result in a successful optimization using a differential evolution algorithm. g-h, show that there is no correlation between biological activity and the score obtained using the potential based on distance likelihood.

Source data

Extended Data Fig. 5 Scatter plots summarizing the results of predicting the binding conformation for 258 compounds in CASF-2016.

a-b, show the correlation between the score of the predicted conformation vs the score of the real conformation. c-d, show that predicted conformations for compounds with less rotatable bonds present lower RMSD. e-f, show that compounds with less than 40 atoms usually result in a successful optimization using a differential evolution algorithm.

Source data

Extended Data Fig. 6 Performance of binding conformation prediction per enzyme type.

Box plots represent the distributions of RMSD between predicted and experimental binding conformations for complexes in the validation set which optimization successfully finished and which target has a valid Enzyme Commission (EC) number.

Source data

Supplementary information

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Méndez-Lucio, O., Ahmad, M., del Rio-Chanona, E.A. et al. A geometric deep learning approach to predict binding conformations of bioactive molecules. Nat Mach Intell 3, 1033–1039 (2021). https://doi.org/10.1038/s42256-021-00409-9

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