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Composing Molecules with Multiple Property Constraints

02/08/2020
by   Wengong Jin, et al.
MIT
7

Drug discovery aims to find novel compounds with specified chemical property profiles. In terms of generative modeling, the goal is to learn to sample molecules in the intersection of multiple property constraints. This task becomes increasingly challenging when there are many property constraints. We propose to offset this complexity by composing molecules from a vocabulary of substructures that we call molecular rationales. These rationales are identified from molecules as substructures that are likely responsible for each property of interest. We then learn to expand rationales into a full molecule using graph generative models. Our final generative model composes molecules as mixtures of multiple rationale completions, and this mixture is fine-tuned to preserve the properties of interest. We evaluate our model on various drug design tasks and demonstrate significant improvements over state-of-the-art baselines in terms of accuracy, diversity, and novelty of generated compounds.

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Code Repositories

multiobj-rationale

Multi-Objective Molecule Generation using Interpretable Substructures (ICML 2020)


view repo

Rationales-extaction-from-active-molecules

rationales are molecule substructures that drive specific properties of interest


view repo

multiobj-rationale

multiobj-rationale


view repo