ArticleNature communications2024
Exhaustive local chemical space exploration using a transformer model.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Diversity Beats Size Scaling for Chemical Language Models.npj drug discovery · 2026Article
- Modular Framework for 3D Molecular Generation in Computational Chemistry Applications.Journal of the American Chemical Society · 2026Article
- A Multi-Step Computational Workflow for Screening and Prioritizing SHP2-Binding Molecules.Pharmaceuticals (Basel, Switzerland) · 2026Article
- ANNalog: generation of MedChem-similar molecules.Journal of cheminformatics · 2026Article
- Large Language Model Agent for Modular Task Execution in Drug Discovery.Journal of chemical information and modeling · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- PURE: policy-guided unbiased REpresentations for structure-constrained molecular generation.Journal of cheminformatics · 2025Article
- RSGPT: a generative transformer model for retrosynthesis planning pre-trained on ten billion datapoints.Nature communications · 2025Article
- PepINVENT: generative peptide design beyond natural amino acids.Chemical science · 2025Article
- Unveiling the power of language models in chemical research question answering.Communications chemistry · 2025Article
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Alternative weighting schemes for fine-tuned extended similarity indices.Journal of chemometrics · 2024Article
- Exhaustive local chemical space exploration using a transformer model.Nature communications · 2024Article
- Evaluation of reinforcement learning in transformer-based molecular design.Journal of cheminformatics · 2024Article
- Reinvent 4: Modern AI-driven generative molecule design.Journal of cheminformatics · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
How many near-neighbors does a molecule have? This fundamental question in chemistry is crucial for molecular optimization problems under the similarity principle assumption. Generative models can sample molecules from a vast chemical space but lack explicit knowledge about molecular similarity. Therefore, these models need guidance from reinforcement learning to sample a relevant similar chemical space. However, they still miss a mechanism to measure the coverage of a specific region of the chemical space. To overcome these limitations, a source-target molecular transformer model, regularized via a similarity kernel function, is proposed. Trained on a largest dataset of ≥200 billion molecular pairs, the model enforces a direct relationship between generating a target molecule and its similarity to a source molecule. Results indicate that the regularization term significantly improves the correlation between generation probability and molecular similarity, enabling exhaustive exploration of molecule near-neighborhoods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.