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A deep generative model for molecule optimization via one fragment modification

Nature Machine Intelligence volume 3, pages 1040–1049 (2021)Cite this article

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A preprint version of the article is available at arXiv.

Abstract

Molecule optimization is a critical step in drug development to improve the desired properties of drug candidates through chemical modification. We have developed a novel deep generative model, Modof, over molecular graphs for molecule optimization. Modof modifies a given molecule through the prediction of a single site of disconnection at the molecule and the removal and/or addition of fragments at that site. A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites. Here we show that Modof-pipe is able to retain major molecular scaffolds, allow controls over intermediate optimization steps and better constrain molecule similarities. Modof-pipe outperforms the state-of-the-art methods on benchmark datasets. Without molecular similarity constraints, Modof-pipe achieves 81.2% improvement in the octanol–water partition coefficient, penalized by synthetic accessibility and ring size, and 51.2%, 25.6% and 9.2% improvement if the optimized molecules are at least 0.2, 0.4 and 0.6 similar to those before optimization, respectively. Modof-pipe is further enhanced into Modof-pipem to allow modification of one molecule to multiple optimized ones. Modof-pipem achieves additional performance improvement, at least 17.8% better than Modof-pipe.

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Fig. 1: Modof model overview.
Fig. 2: Modof-pipe examples for plogP optimization.
Fig. 3: Modof-pipe examples for DRD2, QED and multi-property optimization.

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Data availability

The data used in this manuscript are available publicly from Chen et al.52 and https://github.com/ziqi92/Modof. Source data are provided with this paper.

Code availability

The code for Modof, Modof-pipe and Modof-pipem is publicly available from Chen et al.52 and https://github.com/ziqi92/Modof.

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Acknowledgements

This project was made possible, in part, by support from the National Science Foundation grant nos. IIS-1855501 (X.N.), IIS-1827472 (X.N.), IIS-2133650 (X.N. and S.P.) and OAC-2018627 (S.P.), the National Library of Medicine grant nos. 1R01LM012605-01A1 (X.N.) and 1R21LM013678-01 (X.N.), an AWS Machine Learning Research Award (X.N.) and The Ohio State University President’s Research Excellence programme (X.N.). Any opinions, findings and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funding agencies. We thank X. Wang and X. Cheng for their constructive comments.

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Authors and Affiliations

  1. Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

    Ziqi Chen, Srinivasan Parthasarathy & Xia Ning

  2. Machine Learning Department, NEC Labs America, Princeton, NJ, USA

    Martin Renqiang Min

  3. Translational Data Analytics Institute, The Ohio State University, Columbus, OH, USA

    Srinivasan Parthasarathy & Xia Ning

  4. Biomedical Informatics, The Ohio State University, Columbus, OH, USA

    Xia Ning

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  1. Ziqi Chen
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Contributions

X.N. conceived the research. X.N. and S.P. obtained funding for the research and co-supervised Z.C. Z.C., M.R.M., S.P. and X.N. designed the research. Z.C. and X.N. conducted the research, including data curation, formal analysis, methodology design and implementation, result analysis and visualization. Z.C. drafted the original manuscript. M.R.M. provided comments on the original manuscript. Z.C., X.N. and S.P. conducted the manuscript editing and revision. All authors reviewed the final manuscript.

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Correspondence to Xia Ning.

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Competing interests

M.R.M. was employed by NEC Labs America. The remaining authors declare no competing interests.

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Peer review information Nature Machine Intelligence thanks Michael Withnall and Benjamin Sanchez-Lengeling for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Sections 1–14, Discussion, Tables 1–11, Figs. 1–9, Results and Algorithms 1–5.

Source data

Source Data Fig. 1

SMILES strings of molecules in Fig. 1.

Source Data Fig. 2

SMILES strings of molecules in Fig. 2.

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Chen, Z., Min, M.R., Parthasarathy, S. et al. A deep generative model for molecule optimization via one fragment modification. Nat Mach Intell 3, 1040–1049 (2021). https://doi.org/10.1038/s42256-021-00410-2

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