# ChemTS: An Efficient Python Library for de novo Molecular Generation

arXiv: https://arxiv.org/abs/1710.00616 · HTML: https://mlsanity.com/paper/1710.00616/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2017.

## Built on by (most cited first, 30 of 32)

- [MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design](https://mlsanity.com/paper/2203.14500/) (2022)
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- [Reinforced Genetic Algorithm for Structure-based Drug Design](https://mlsanity.com/paper/2211.16508/) (2022)
- [Learning to design drug-like molecules in three-dimensional space using deep generative models](https://mlsanity.com/paper/2104.08474/) (2021)
- [Dynamic Search for Inference-Time Alignment in Diffusion Models](https://mlsanity.com/paper/2503.02039/) (2025)
- [JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design](https://mlsanity.com/paper/2106.04011/) (2021)
- [Data-Efficient Graph Grammar Learning for Molecular Generation](https://mlsanity.com/paper/2203.08031/) (2022)
- [Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention](https://mlsanity.com/paper/2103.00213/) (2021)
- [Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications](https://mlsanity.com/paper/2303.08060/) (2023)
- [Probabilistic Generative Transformer Language models for Generative Design of Molecules](https://mlsanity.com/paper/2209.09406/) (2022)
- [Preference Optimization for Molecular Language Models](https://mlsanity.com/paper/2310.12304/) (2023)
- [Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design](https://mlsanity.com/paper/2105.02637/) (2021)
- [Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search](https://mlsanity.com/paper/2108.10266/) (2021)
- [Entropy-Reinforced Planning with Large Language Models for Drug Discovery](https://mlsanity.com/paper/2406.07025/) (2024)
- [A Bayesian Flow Network Framework for Chemistry Tasks](https://mlsanity.com/paper/2407.20294/) (2024)
- [Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery](https://mlsanity.com/paper/2106.02190/) (2021)
- [A Method for Inferring Polymers Based on Linear Regression and Integer Programming](https://mlsanity.com/paper/2109.02628/) (2021)
- [Graph Energy-based Model for Substructure Preserving Molecular Design](https://mlsanity.com/paper/2102.04600/) (2021)
- [Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design](https://mlsanity.com/paper/2502.09860/) (2025)
- [An Inverse QSAR Method Based on Linear Regression and Integer Programming](https://mlsanity.com/paper/2107.02381/) (2021)
- [Materials Discovery using Max K-Armed Bandit](https://mlsanity.com/paper/2212.08225/) (2022)
- [Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2502.17456/) (2025)
- [A molecular generative model with genetic algorithm and tree search for cancer samples](https://mlsanity.com/paper/2112.08959/) (2021)
- [Molecular Graph Generation by Decomposition and Reassembling](https://mlsanity.com/paper/2302.00587/) (2022)
- [A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"](https://mlsanity.com/paper/2102.00700/) (2021)
- [Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model](https://mlsanity.com/paper/2209.13527/) (2022)
- [Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes](https://mlsanity.com/paper/2305.00801/) (2023)
- [Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networks](https://mlsanity.com/paper/2204.01847/) (2022)
- [SHA-256 Infused Embedding-Driven Generative Modeling of High-Energy Molecules in Low-Data Regimes](https://mlsanity.com/paper/2510.25788/) (2025)

## Abstract (arXiv)

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.