Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space

TL;DR

This paper introduces a hybrid genetic algorithm enhanced with a neural network discriminator to improve molecule generation diversity and steer the search in chemical space, outperforming other models in optimization tasks.

Contribution

The study presents a novel integration of deep neural networks with genetic algorithms for chemical space exploration, enhancing diversity and interpretability.

Findings

Outperforms other generative models in optimization tasks

Increases diversity of generated molecules

Provides insights into design principles

Abstract

Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space. We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA. We show that our algorithm outperforms other generative models in optimization tasks. We furthermore present a way to increase interpretability of genetic algorithms, which helped us to derive design principles.