mlsanityData to 7 Oct 2026

ChemTS: An Efficient Python Library for de novo Molecular Generation

arXiv 1710.00616 ↗First posted 29 Sep 2017physics.chem-ph cs.CEcited by 32

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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.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2017.

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Citing papers per year: 2021: 11; 2022: 9; 2023: 3; 2024: 3; 2025: 5; 2026: 1.

32 papers.

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  1. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  2. 2024Efficient Evolutionary Search Over Chemical Space with Large Language Modelscited by 59
  3. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
  4. 2022Reinforced Genetic Algorithm for Structure-based Drug Designcited by 46
  5. 2021Learning to design drug-like molecules in three-dimensional space using deep generative modelscited by 37
  6. 2025Dynamic Search for Inference-Time Alignment in Diffusion Modelscited by 36
  7. 2021JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Designcited by 31
  8. 2022Data-Efficient Graph Grammar Learning for Molecular Generationcited by 26
  9. 2021Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attentioncited by 20
  10. 2023Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applicationscited by 16
  11. 2022Probabilistic Generative Transformer Language models for Generative Design of Moleculescited by 8
  12. 2021Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Designcited by 6
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  1. 2021Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Searchcited by 6
  2. 2023Preference Optimization for Molecular Language Modelscited by 6
  3. 2024Entropy-Reinforced Planning with Large Language Models for Drug Discoverycited by 6
  4. 2024A Bayesian Flow Network Framework for Chemistry Taskscited by 6
  5. 2021Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discoverycited by 5
  6. 2021A Method for Inferring Polymers Based on Linear Regression and Integer Programmingcited by 5
  7. 2021Graph Energy-based Model for Substructure Preserving Molecular Designcited by 4
  8. 2025Gradient GA: Gradient Genetic Algorithm for Drug Molecular Designcited by 4
  9. 2021An Inverse QSAR Method Based on Linear Regression and Integer Programmingcited by 3
  10. 2022Materials Discovery using Max K-Armed Banditcited by 3
  11. 2025Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunitiescited by 3
  12. 2021A molecular generative model with genetic algorithm and tree search for cancer samplescited by 2
  13. 2022Molecular Graph Generation by Decomposition and Reassemblingcited by 2
  14. 2021A reproducibility study of "Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space"cited by 1
  15. 2022Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Modelcited by 1
  16. 2023Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanescited by 1
  17. 2022Bayesian Sequential Stacking Algorithm for Concurrently Designing Molecules and Synthetic Reaction Networkscited by 0
  18. 2025SHA-256 Infused Embedding-Driven Generative Modeling of High-Energy Molecules in Low-Data Regimescited by 0
  19. 2025FRAGMENTA: End-to-end Fragmentation-based Generative Model with Agentic Tuning for Drug Lead Optimizationcited by 0
  20. 2026From Tokens to Blocks: A Block-Diffusion Perspective on Molecular Generationcited by 0