ArticleJournal of cheminformatics2023
Probabilistic generative transformer language models for generative design of molecules.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Data-driven design and screening of novelRSC advances · 2026Article
- Disentangled autoencoding equivariant diffusion model for controlled generation of 3D molecules.Nature communications · 2026Article
- VeGA: A Versatile Generative Architecture for Bioactive Molecules across Multiple Therapeutic Targets.Journal of chemical information and modeling · 2025Article
- An open-source family of large encoder-decoder foundation models for chemistry.Communications chemistry · 2025Article
- Activity cliff-aware reinforcement learning for de novo drug design.Journal of cheminformatics · 2025Article
- CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.Journal of cheminformatics · 2025Article
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- Crystal Composition Transformer: Self-Learning Neural Language Model for Generative and Tinkering Design of Materials.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Application of Transformers in Cheminformatics.Journal of chemical information and modeling · 2024Review
- ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.ArXiv · 2023Article
- Transformer technology in molecular science.Wiley interdisciplinary reviews. Computational molecular scienceArticle
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Authors and funding
5 authors.
Funding
Abstract
Self-supervised neural language models have recently found wide applications in the generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose the Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the "molecules grammars" with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering with molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformer.
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