ChemTS: An Efficient Python Library for de novo Molecular Generation

TL;DR

ChemTS is a Python library that combines Monte Carlo tree search and RNNs to efficiently generate novel molecules, outperforming traditional methods in optimizing chemical properties.

Contribution

Introduces ChemTS, a novel Python library that integrates MCTS and RNNs for efficient de novo molecular generation without predefined fragments.

Findings

ChemTS outperforms existing methods in optimizing molecular properties.

The library effectively explores vast chemical spaces.

ChemTS is publicly available for use and further development.

Abstract

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational auto encoders (VAEs) and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel python library ChemTS that explores the chemical space by combining Monte Carlo tree search (MCTS) and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.