Preferential Multi-Objective Bayesian Optimization for Drug Discovery
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910888330526720 |
|---|---|
| author | Dang, Tai Pham, Long-Hung Truong, Sang T. Glenn, Ari Nguyen, Wendy Pham, Edward A. Glenn, Jeffrey S. Koyejo, Sanmi Luong, Thang |
| author_facet | Dang, Tai Pham, Long-Hung Truong, Sang T. Glenn, Ari Nguyen, Wendy Pham, Edward A. Glenn, Jeffrey S. Koyejo, Sanmi Luong, Thang |
| contents | Despite decades of advancements in automated ligand screening, large-scale drug discovery remains resource-intensive and requires post-processing hit selection, a step where chemists manually select a few promising molecules based on their chemical intuition. This creates a major bottleneck in the virtual screening process for drug discovery, demanding experts to repeatedly balance complex trade-offs among drug properties across a vast pool of candidates. To improve the efficiency and reliability of this process, we propose a novel human-centered framework named CheapVS that allows chemists to guide the ligand selection process by providing preferences regarding the trade-offs between drug properties via pairwise comparison. Our framework combines preferential multi-objective Bayesian optimization with a docking model for measuring binding affinity to capture human chemical intuition for improving hit identification. Specifically, on a library of 100K chemical candidates targeting EGFR and DRD2, CheapVS outperforms state-of-the-art screening methods in identifying drugs within a limited computational budget. Notably, our method can recover up to 16/37 EGFR and 37/58 DRD2 known drugs while screening only 6% of the library, showcasing its potential to significantly advance drug discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16841 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Preferential Multi-Objective Bayesian Optimization for Drug Discovery Dang, Tai Pham, Long-Hung Truong, Sang T. Glenn, Ari Nguyen, Wendy Pham, Edward A. Glenn, Jeffrey S. Koyejo, Sanmi Luong, Thang Machine Learning Human-Computer Interaction Biomolecules Despite decades of advancements in automated ligand screening, large-scale drug discovery remains resource-intensive and requires post-processing hit selection, a step where chemists manually select a few promising molecules based on their chemical intuition. This creates a major bottleneck in the virtual screening process for drug discovery, demanding experts to repeatedly balance complex trade-offs among drug properties across a vast pool of candidates. To improve the efficiency and reliability of this process, we propose a novel human-centered framework named CheapVS that allows chemists to guide the ligand selection process by providing preferences regarding the trade-offs between drug properties via pairwise comparison. Our framework combines preferential multi-objective Bayesian optimization with a docking model for measuring binding affinity to capture human chemical intuition for improving hit identification. Specifically, on a library of 100K chemical candidates targeting EGFR and DRD2, CheapVS outperforms state-of-the-art screening methods in identifying drugs within a limited computational budget. Notably, our method can recover up to 16/37 EGFR and 37/58 DRD2 known drugs while screening only 6% of the library, showcasing its potential to significantly advance drug discovery. |
| title | Preferential Multi-Objective Bayesian Optimization for Drug Discovery |
| topic | Machine Learning Human-Computer Interaction Biomolecules |
| url | https://arxiv.org/abs/2503.16841 |