Improving Compound Activity Classification via Deep Transfer and Representation Learning.

ABSTRACT: Recent advances in molecular machine learning, especially deep neural networks such as graph neural networks (GNNs), for predicting structure-activity relationships (SAR) have shown tremendous potential in computer-aided drug discovery. However, the applicability of such deep neural networks is limited by the requirement of large amounts of training data. In order to cope with limited training data for a target task, transfer learning for SAR modeling has been recently adopted to leverage information from data of related tasks. In this work, in contrast to the popular parameter-based transfer learning such as pretraining, we develop novel deep transfer learning methods TAc and TAc-fc to leverage source domain data and transfer useful information to the target domain. TAc</

SUBMITTER: Dey V 

PROVIDER: S-EPMC8945064 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature