PhenoModel: A multimodal phenotypic drug design foundation model for discovering novel potential inhibitors of multiple cancer cells
Year: 2025
Type: article
Abstract: Phenotypic drug discovery (PDD) focuses on the observable traits or phenotype of cells or organisms in response to drug treatment, rather than relying primarily on specific molecular targets. Drugs discovered through this approach may have better therapeutic relevance, as they are tested in conditions that closely mimic human disease. In this study, we present PhenoModel, a multimodal molecular foundation model developed using our unique dual-space contrastive learning framework. This model effectively connects molecular structures with phenotypic information. PhenoModel is applicable to a range of downstream drug discovery tasks, including molecular property prediction and active molecule screening based on targets, phenotypes, and ligands. Our results demonstrate that PhenoModel outperforms baseline methods in these areas. Building from this model, PhenoScreen is developed to successfully identify several phenotypically bioactive compounds against osteosarcoma and rhabdomyosarcoma cell lines. These findings highlight the versatility of PhenoModel and its potential to accelerate drug discovery by uncovering novel therapeutic pathways and expanding the diversity of viable drug candidates.
Source: Acta Pharmaceutica Sinica B
Authors: Shihang Wang, Qilei Han, Weichen Qin, Lin Wang, Junhong Yuan, Fengyu Cai, Yiqun Zhao, Pengxuan Ren, Yunze Zhang, Yilin Tang, Ruifeng Li, Zongquan Li, Wenchao Zhang, Shenghua Gao, Fang Bai
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,600
OpenAlex ID: W7083295155
