PharmRL: pharmacophore elucidation with deep geometric reinforcement learning
PharmRL addresses the need for automated methods in pharmacophore design, particularly in cases where a cognate ligand is unavailable. Experimental results demonstrate that PharmRL generates functional pharmacophores. Additionally, we provide a Google Colab notebook to facilitate the use of this method.
Authors
- David Ryan Koes (ORCID: https://orcid.org/0000-0002-6892-6614)
- Rishal Aggarwal
Institutions
- University of Pittsburgh (US)
- Carnegie Mellon University (US)
Publication Details
- Journal
- BMC Biology
- Published
- 2024-12-31
- DOI
- https://doi.org/10.1186/s12915-024-02096-5
- Citations
- 7
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 2.53
Funders
- National Institutes of Health
- National Institute of General Medical Sciences