2020

Multi-Objective Molecule Generation using Interpretable Substructures

Jin, Wengong, Barzilay, Regina, Jaakkola, Tommi

Understand

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.

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