ChemTS: An Efficient Python Library for de novo Molecular Generation

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.

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
• Automatic chemical design using a data-driven continuous representation of molecules
• Pixel Recurrent Neural Networks
• Grammar Variational Autoencoder
• Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Cited by in corpus (31)

• Automatic chemical design using a data-driven continuous representation of molecules
• Deep learning for molecular design - a review of the state of the art
• Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
• Learning to design drug-like molecules in three-dimensional space using deep generative models
• Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery
• Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
• Population-based de novo molecule generation, using grammatical evolution
• Overcoming data scarcity with transfer learning
• Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention
• Constrained Bayesian Optimization for Automatic Chemical Design
• In silico generation of novel, drug-like chemical matter using the LSTM neural network
• Guiding Deep Molecular Optimization with Genetic Exploration
• Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures
• CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
• JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
• A generative model for molecule generation based on chemical reaction trees
• Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications
• Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design
• Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
• A novel method for inference of chemical compounds with prescribed topological substructures based on integer programming
• A Bayesian Flow Network Framework for Chemistry Tasks
• Controlled Molecule Generator for Optimizing Multiple Chemical Properties
• Goal directed molecule generation using Monte Carlo Tree Search
• A Novel Method for Inference of Acyclic Chemical Compounds with Bounded Branch-height Based on Artificial Neural Networks and Integer Programming
• Prediction of Atomization Energies of Au13+ Clusters through the Machine Learning Approach
• Graph Energy-based Model for Substructure Preserving Molecular Design
• Accelerating Copolymer Inverse Design using AI Gaming algorithm
• Probabilistic hypergraph grammars for efficient molecular optimization
• Generate Novel Molecules With Target Properties Using Conditional Generative Models
• A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"
• Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search