TransDTI: Transformer-Based Language Models for Estimating DTIs and Building a Drug Recommendation Workflow
Yogesh Kalakoti, Shashank Yadav, Durai Sundar
Abstract
2 over baseline methods. The results showed that models based on transformer-based language models effectively predict novel drug-target interactions from sequence data. The proposed models significantly outperformed existing methods like DeepConvDTI, DeepDTA, and DeepDTI on a test data set. Further, the validity of novel interactions predicted by TransDTI was found to be backed by molecular docking and simulation analysis, where the model prediction had similar or better interaction potential for MAP2k and transforming growth factor-β (TGFβ) and their known inhibitors. Proposed approaches can have a significant impact on the development of personalized therapy and clinical decision making.
Topics & Concepts
Computer scienceWorkflowTransformerData miningMachine learningArtificial intelligenceRegression testingLanguage modelEngineeringDatabaseProgramming languageVoltageElectrical engineeringSoftwareSoftware constructionSoftware systemComputational Drug Discovery MethodsBioinformatics and Genomic NetworksMachine Learning in Materials Science