Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey-Reference-Cited by-同舟云学术

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Published:2024 Issue: Volume:12 Page:35796-35812
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Alizadehsani Roohallah¹^ORCID,Oyelere Solomon Sunday²^ORCID,Hussain Sadiq³^ORCID,Jagatheesaperumal Senthil Kumar⁴^ORCID,Calixto Rene Ripardo⁵,Rahouti Mohamed⁶^ORCID,Roshanzamir Mohamad⁷,De Albuquerque Victor Hugo C.⁵^ORCID

Affiliation:

1. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia

2. Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Skellefteå, Sweden

3. Examination Branch, Dibrugarh University, Dibrugarh, Assam, India

4. Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India

5. Department of Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Brazil

6. Department of Computer and Information Science, Fordham University, Bronx, NY, USA

7. Department of Computer Engineering, Faculty of Engineering, Fasa University, Fasa, Iran

Funder

PRECISE-Luleå University of Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10459028.pdf?arnumber=10459028

Reference94 articles.

1. Exploiting machine learning for end-to-end drug discovery and development

2. Deep learning in drug discovery: opportunities, challenges and future prospects

3. ADMET modeling approaches in drug discovery

4. Structure-Based Virtual Screening: From Classical to Artificial Intelligence

5. Rethinking drug design in the artificial intelligence era

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