Abstract
•We propose transfer learning approaches for predicting drug candidates.•We compare the proposed approaches against existing baseline approaches.•We conduct experimental results for several prediction tasks of drug candidates.•Our approaches achieve high and significant performance results over baselines.
The prediction of drug candidates for given tissues of organisms based on expression data is a critical biological problem. By correctly predicting drug candidates for given tissues, biologists can (1) avoid an experimental process of high-throughput screening that requires excessive time and costly equipment and (2) accelerate the drug discovery process by automatically assigning drug candidates. Although high throughput screening for therapeutic compounds lead to the generation of expression data, the process of correctly assigning candidate drugs based on such data remains a rigorous task. Hence, the design of high-performance machine learning (ML) algorithms is crucial for data analysts who work with clinicians. Clinicians incorporate advanced ML tools into expert and intelligent systems to improve the drug discovery process by accurately identifying drug candidates. The transfer learning approaches that are necessary to improve the prediction performance of several tasks that are involved in identifying drug candidates are presented in this paper. The performances of machine learning algorithms are compared in the transfer learning setting by employing several evaluation measures on real data that are obtained from experiments conducted on rats to identify drug candidates. The experimental results show that the proposed transfer learning approaches outperform baseline approaches in terms of prediction performance and statistical significance.