ChemAP: predicting drug approval with chemical structures before clinical trial phase by leveraging multi-modal embedding space and knowledge distillation.

ABSTRACT: Recent studies showed that the likelihood of drug approval can be predicted with clinical data and structure information of drug using computational approaches. Predicting the likelihood of drug approval can be innovative and of high impact. However, models that leverage clinical data are applicable only in clinical stages, which is not very practical. Prioritizing drug candidates and early-stage decision-making in the de novo drug development process is crucial in pharmaceutical research to optimize resource allocation. For early-stage decision-making, we need a computational model that uses only chemical structures. This seemingly impossible task may utilize the predictive power with multi-modal features including clinical data. In this work, we introduce ChemAP (Chemical structure-based

SUBMITTER: Cho C 

PROVIDER: S-EPMC11449903 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature