JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design

TL;DR

JANUS is a novel parallel tempered genetic algorithm guided by deep neural networks that efficiently explores chemical space for inverse molecular design, reducing evaluation costs and achieving state-of-the-art results.

Contribution

The paper introduces JANUS, combining parallel tempering, active learning, and neural network property prediction to improve inverse molecular design efficiency.

Findings

Outperforms existing models in inverse design tasks

Reduces the number of expensive property evaluations

Achieves state-of-the-art performance in molecular generation

Abstract

Inverse molecular design, i.e., designing molecules with specific target properties, can be posed as an optimization problem. High-dimensional optimization tasks in the natural sciences are commonly tackled via population-based metaheuristic optimization algorithms such as evolutionary algorithms. However, expensive property evaluation, which is often required, can limit the widespread use of such approaches as the associated cost can become prohibitive. Herein, we present JANUS, a genetic algorithm that is inspired by parallel tempering. It propagates two populations, one for exploration and another for exploitation, improving optimization by reducing expensive property evaluations. Additionally, JANUS is augmented by a deep neural network that approximates molecular properties via active learning for enhanced sampling of the chemical space. Our method uses the SELFIES molecular representation and the STONED algorithm for the efficient generation of structures, and outperforms other generative models in common inverse molecular design tasks achieving state-of-the-art performance.