ArticleJournal of chemical information and modeling2024
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Optimizing drug design by merging generative AI with a physics-based active learning framework.Communications chemistry · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design.Journal of cheminformatics · 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 review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- Generative AI in drug discovery and development: the next revolution of drug discovery and development would be directed by generative AI.Annals of medicine and surgery (2012) · 2024Article
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4 authors.
Funding
Abstract
The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit desired characteristics. In this work, we present a computationally efficient active learning methodology and demonstrate its applicability to targeted molecular generation. When applied to c-Abl kinase, a protein with FDA-approved small-molecule inhibitors, the model learns to generate molecules similar to the inhibitors without prior knowledge of their existence and even reproduces two of them exactly. We also show that the methodology is effective for a protein without any commercially available small-molecule inhibitors, the HNH domain of the CRISPR-associated protein 9 (Cas9) enzyme. To facilitate implementation and reproducibility, we made all of our software available through the open-source ChemSpaceAL Python package.
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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.