Fine-tuning of BERT Model to Accurately Predict Drug–Target Interactions

Fine-tuning of BERT Model to Accurately Predict Drug–Target Interactions

The identification of optimal drug candidates is very important in drug discovery. Researchers in biology and computational sciences have sought to use machine learning (ML) to efficiently predict drug–target interactions (DTIs). In recent years, according to the emerging usefulness of pretrained mo...

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Bibliographic Details
Main Authors: Hyeunseok Kang, Sungwoo Goo, Hyunjung Lee, Jung-woo Chae, Hwi-yeol Yun, Sangkeun Jung
Format: Article
Language:English
Published: MDPI AG 2022-08-01
Series:Pharmaceutics
Subjects:
drug–target interaction
bidirectional encoder representations from transformers (BERT)
ChemBERTa
ProBert
pretrained model
self-supervised learning
Online Access:https://www.mdpi.com/1999-4923/14/8/1710

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https://www.mdpi.com/1999-4923/14/8/1710

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