PharmRL: pharmacophore elucidation with deep geometric reinforcement learning
Abstract Background Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are easiest to identify from co-crystal struct...
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2024-12-01
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| Online Access: | https://doi.org/10.1186/s12915-024-02096-5 |
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| author | Rishal Aggarwal David R. Koes |
| author_facet | Rishal Aggarwal David R. Koes |
| author_sort | Rishal Aggarwal |
| collection | DOAJ |
| description | Abstract Background Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are easiest to identify from co-crystal structures of a bound protein-ligand complex. However, designing a pharmacophore in the absence of a ligand is a much harder task. Results In this work, we develop a deep learning method that can identify pharmacophores in the absence of a ligand. Specifically, we train a CNN model to identify potential favorable interactions in the binding site, and develop a deep geometric Q-learning algorithm that attempts to select an optimal subset of these interaction points to form a pharmacophore. With this algorithm, we show better prospective virtual screening performance, in terms of F1 scores, on the DUD-E dataset than random selection of ligand-identified features from co-crystal structures. We also conduct experiments on the LIT-PCBA dataset and show that it provides efficient solutions for identifying active molecules. Finally, we test our method by screening the COVID moonshot dataset and show that it would be effective in identifying prospective lead molecules even in the absence of fragment screening experiments. Conclusions 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. |
| format | Article |
| id | doaj-art-960994299fce4615bee504bf76a0a412 |
| institution | Kabale University |
| issn | 1741-7007 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Biology |
| spelling | doaj-art-960994299fce4615bee504bf76a0a4122025-01-05T12:46:14ZengBMCBMC Biology1741-70072024-12-0122111510.1186/s12915-024-02096-5PharmRL: pharmacophore elucidation with deep geometric reinforcement learningRishal Aggarwal0David R. Koes1Joint PhD Program in Computational Biology, Carnegie Mellon University-University of PittsburghComputational & Systems Biology, University of PittsburghAbstract Background Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are easiest to identify from co-crystal structures of a bound protein-ligand complex. However, designing a pharmacophore in the absence of a ligand is a much harder task. Results In this work, we develop a deep learning method that can identify pharmacophores in the absence of a ligand. Specifically, we train a CNN model to identify potential favorable interactions in the binding site, and develop a deep geometric Q-learning algorithm that attempts to select an optimal subset of these interaction points to form a pharmacophore. With this algorithm, we show better prospective virtual screening performance, in terms of F1 scores, on the DUD-E dataset than random selection of ligand-identified features from co-crystal structures. We also conduct experiments on the LIT-PCBA dataset and show that it provides efficient solutions for identifying active molecules. Finally, we test our method by screening the COVID moonshot dataset and show that it would be effective in identifying prospective lead molecules even in the absence of fragment screening experiments. Conclusions 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.https://doi.org/10.1186/s12915-024-02096-5PharmacophoresVirtual screeningProtein-ligand interactionsMachine learning |
| spellingShingle | Rishal Aggarwal David R. Koes PharmRL: pharmacophore elucidation with deep geometric reinforcement learning BMC Biology Pharmacophores Virtual screening Protein-ligand interactions Machine learning |
| title | PharmRL: pharmacophore elucidation with deep geometric reinforcement learning |
| title_full | PharmRL: pharmacophore elucidation with deep geometric reinforcement learning |
| title_fullStr | PharmRL: pharmacophore elucidation with deep geometric reinforcement learning |
| title_full_unstemmed | PharmRL: pharmacophore elucidation with deep geometric reinforcement learning |
| title_short | PharmRL: pharmacophore elucidation with deep geometric reinforcement learning |
| title_sort | pharmrl pharmacophore elucidation with deep geometric reinforcement learning |
| topic | Pharmacophores Virtual screening Protein-ligand interactions Machine learning |
| url | https://doi.org/10.1186/s12915-024-02096-5 |
| work_keys_str_mv | AT rishalaggarwal pharmrlpharmacophoreelucidationwithdeepgeometricreinforcementlearning AT davidrkoes pharmrlpharmacophoreelucidationwithdeepgeometricreinforcementlearning |