Is predicting Drug-Target Interaction Possible When A Limited Amount of Bioactivity Data is Available?

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2022-10

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Orta Doğu Teknik Üniversitesi, Enformatik Enstitüsü

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Motivation Lately. deep learning-based models for drug-target interaction (DTI) prediction have yielded highly promising results. However. the majority of proposed deep learning models developed so far require large-scale training data. For many proteins. there is very little bioactivity data recorded in the databases or none at all; therefore. no prediction models are available for these. For example. in ChEMBL_29. only 8% of proteins have more than 1.000 bioactive compounds. while 56% of the proteins have less than 50 bioactive compounds. Deep transfer learning can be used to learn from such small amounts of data. Transfer learning is a machine learning approach where a model is trained for a source task. and this trained source model is then reused as an initial configuration to build (train) a target model for a different but related target task. Transfer learning has not been extensively exploited so far in the area of drug-target interaction prediction. In this study. we exploit the use of transfer learning techniques for the prediction of interactions between drugs/compounds and understudied target proteins that have scarce training data.

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https://hibit2022.ims.metu.edu.tr
https://hdl.handle.net/11511/102011

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The International Symposium on Health Informatics and Bioinformatics

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Graduate School of Informatics, Conference / Seminar

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Citation Formats

E. E. Orta Doğu Teknik Üniversitesi, “Is predicting Drug-Target Interaction Possible When A Limited Amount of Bioactivity Data is Available?,” Erdemli, Mersin, TÜRKİYE, 2022, p. 3063, Accessed: 00, 2023. [Online]. Available: https://hibit2022.ims.metu.edu.tr.