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DrugOrchestra: Jointly predicting drug response, targets, and side effects via deep multi-task learning
- Publication Year :
- 2020
- Publisher :
- Cold Spring Harbor Laboratory, 2020.
-
Abstract
- Massively accumulated pharmacogenomics, chemogenomics, and side effect datasets offer an unprecedented opportunity for drug response prediction, drug target identification and drug side effect prediction. Existing computational approaches limit their scope to only one of these three tasks, inevitably overlooking the rich connection among them. Here, we propose DrugOrchestra, a deep multi-task learning framework that jointly predicts drug response, targets and side effects. DrugOrchestra leverages pre-trained molecular structure-based drug representation to bridge these three tasks. Instead of directly fine-tuning on an individual task, DrugOrchestra uses deep multi-task learning to obtain a phenotype-based drug representation by simultaneously fine-tuning on drug response, target and side effect prediction. By coupling these three tasks together, DrugOrchestra is able to make predictions for unseen drugs by only knowing their molecular structures. We constructed a heterogeneous drug discovery dataset of over 21k drugs by integrating 8 datasets across three tasks. Our method obtained significant improvement in comparison to methods that were trained on a single task or a single dataset. We further revealed the transferability across 8 datasets and 3 tasks, providing novel insights for understanding drug mechanisms.Availabilityhttps://github.com/jiangdada1221/DrugOrchestra
- Subjects :
- Drug
Drug discovery
business.industry
Computer science
media_common.quotation_subject
Drug target
Multi-task learning
Machine learning
computer.software_genre
Task (project management)
Identification (information)
chemistry.chemical_compound
Side effect (computer science)
chemistry
Pharmacogenomics
Chemogenomics
Artificial intelligence
business
Representation (mathematics)
computer
media_common
Subjects
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi...........4c04b3a231b08084eb32f8a3388beced