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

AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alan Aspuru-Guzik

Keywords: Generative model, Chemical Space, Inverse Molecular Design

TL;DR: Tackling inverse design via genetic algorithms augmented with deep neural networks.

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

Code: https://github.com/aspuru-guzik-group/GA

Community Implementations: [ 3 code implementations](https://www.catalyzex.com/paper/arxiv:1909.11655/code)