Multi-Objective Molecule Generation using Interpretable Substructures

TL;DR

This paper introduces a novel molecule generation method that constructs compounds from interpretable substructures called rationales, enabling effective multi-property optimization in drug discovery.

Contribution

It proposes a graph-based generative model that composes molecules from property-associated rationales, improving multi-objective molecule generation.

Findings

Significant improvements over baselines in accuracy, diversity, and novelty.

Effective handling of multiple property constraints in molecule generation.

Enhanced interpretability through rationale-based molecule construction.

Abstract

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.