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...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.展开更多
基金supported by Shanghai Science and Technology Development Funds(Grant IDs:24JS2850200,24JS2850100,and 24YF2729200)ShanghaiTech AI4S Initiative SHTAI4S202404,National Key R&D Program of China(Grant IDs:2022YFC3400501 and 2022YFC3400500)start-up package from ShanghaiTech University,and Shanghai Frontiers Science Center for Biomacromolecules and Precision Medicine at ShanghaiTech University.
文摘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.
