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Retrieval-based Controllable Molecule Generation

Wang, Zichao and Nie, Weili and Qiao, Zhuoran and Xiao, Chaowei and Baraniuk, Richard and Anandkumar, Anima (2022) Retrieval-based Controllable Molecule Generation. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20221221-004642358

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Abstract

Generating new molecules with specified chemical and biological properties via generative models has emerged as a promising direction for drug discovery. However, existing methods require extensive training/fine-tuning with a large dataset, often unavailable in real-world generation tasks. In this work, we propose a new retrieval-based framework for controllable molecule generation. We use a small set of exemplar molecules, i.e., those that (partially) satisfy the design criteria, to steer the pre-trained generative model towards synthesizing molecules that satisfy the given design criteria. We design a retrieval mechanism that retrieves and fuses the exemplar molecules with the input molecule, which is trained by a new self-supervised objective that predicts the nearest neighbor of the input molecule. We also propose an iterative refinement process to dynamically update the generated molecules and retrieval database for better generalization. Our approach is agnostic to the choice of generative models and requires no task-specific fine-tuning. On various tasks ranging from simple design criteria to a challenging real-world scenario for designing lead compounds that bind to the SARS-CoV-2 main protease, we demonstrate our approach extrapolates well beyond the retrieval database, and achieves better performance and wider applicability than previous methods.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2208.11126arXivDiscussion Paper
ORCID:
AuthorORCID
Qiao, Zhuoran0000-0002-5704-7331
Xiao, Chaowei0000-0002-7043-4926
Baraniuk, Richard0000-0002-0721-8999
Anandkumar, Anima0000-0002-6974-6797
Additional Information:ETHICS STATEMENT. Applications that involve molecule generation such as drug discovery are high-stake in nature. These applications are highly regulated to prevent potential misuse (Hill and Richards, 2022). RetMol as a technology to improve controllable molecule generation has the potential to be subjected to malicious use. For example, one could change the retrieval database and the design criteria into harmful ones, such as increased drug toxicity. However, we note that RetMol is a computational tool useful for in silico experiments. As a result, although RetMol can suggest new molecules according to arbitrary design criteria, the properties of the generated molecules are estimations of the real chemical and biological properties and need to be further validated in lab experiments. Thus, while RetMol’s real-world impact is limited to in silico experiments, it is also prevented from directly generating real drugs that can be readily used. In addition, controllable molecule generation is an active area of research; we hope that our work contribute to this ongoing line of research and make ML methods safe and reliable for molecule generation applications in the real world. REPRODUCIBILITY STATEMENT. To ensure the reproducibility of the empirical results, we provide the implementation details of each task (i.e., experimental setups, hyperparameters, dataset specifications, etc.) in Appendix B. The source code will be released in the future.
Group:COVID-19
DOI:10.48550/arXiv.2208.11126
Record Number:CaltechAUTHORS:20221221-004642358
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20221221-004642358
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:118541
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:22 Dec 2022 18:51
Last Modified:02 Jun 2023 01:29

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