Deep learning in drug discovery: an integrative review and future challenges

Heba Askr; Enas Elgeldawi; Heba Aboul Ella; Yaseen A M M Elshaier; Mamdouh M Gomaa; Aboul Ella Hassanien

doi:10.1007/s10462-022-10306-1

Deep learning in drug discovery: an integrative review and future challenges

Artif Intell Rev. 2023;56(7):5975-6037. doi: 10.1007/s10462-022-10306-1. Epub 2022 Nov 17.

Authors

Heba Askr¹, Enas Elgeldawi², Heba Aboul Ella³, Yaseen A M M Elshaier⁴, Mamdouh M Gomaa², Aboul Ella Hassanien⁵

Affiliations

¹ Faculty of Computers and Artificial Intelligence, University of Sadat City, Sadat City, Egypt.
² Computer Science Department, Faculty of Science, Minia University, Minia, Egypt.
³ Faculty of Pharmacy and Drug Technology, Chinese University in Egypt (CUE), Cairo, Egypt.
⁴ Faculty of Pharmacy, University of Sadat City, Sadat City, Menoufia Egypt.
⁵ Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt.

Abstract

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

Keywords: Artificial intelligence; Deep learning; Digital twining; Drug discovery; Drug dosing optimization; Drug sensitivity and response; Drug side-effects; Drug–drug similarity; Drug–target interactions; Explainable artificial intelligence.