Cited by 20Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and EducationScience, Research, and Medicine

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An in-depth review of AI-powered advancements in cancer drug discovery

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Le, Minh Huu Nhat, Nguyen, Phat Ky, Nguyen, Thi Phuong Trang, Nguyen, Hien Quang, Tam, Dao Ngoc Hien, Huynh, Han Hong, Huynh, Phat Kim, Le, Nguyen Quoc Khanh·2025·Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease

DOI 10.1016/j.bbadis.2025.167680

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Abstract

The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep learning and advanced data analytics, in accelerating key stages of the drug discovery process: target identification, drug design, clinical trial optimization, and drug response prediction. Cutting-edge tools such as DrugnomeAI and PandaOmics have made substantial contributions to therapeutic target identification, while AI's predictive capabilities are driving personalized treatment strategies. Additionally, advancements like AlphaFold highlight AI's capacity to address intricate challenges in drug development. However, the field faces significant challenges, including the management of large-scale genomic datasets and ethical concerns surrounding AI deployment in healthcare. This review underscores the promise of data-centric AI approaches and emphasizes the necessity of continued innovation and interdisciplinary collaboration. Together, AI and genomics are charting a path toward more precise, efficient, and transformative cancer therapeutics.

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Authors: Le, M. H. N., Nguyen, P. K., Nguyen, T. P. T., Nguyen, H. Q., Tam, D. N. H., Huynh, H. H., Huynh, P. K., & Le, N. Q. K.
Affiliations: Taipei Medical University (TW)
HUTECH University (VN)
Methodist Hospital (US)
North Carolina Agricultural and Technical State University (US)
Taipei Medical University Hospital (TW)
Corresponding author: Le, Nguyen Quoc Khanh
ORCID: Le, Minh Huu Nhat
Nguyen, Phat Ky
Nguyen, Hien Quang
Tam, Dao Ngoc Hien
Le, Nguyen Quoc Khanh
Venue: Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease
Publisher: Elsevier BV
Published: 2025-03
Volume: 1871
Issue: 3
Pages: 167680
ISSN: 0925-4439 (ISSN-L)
DOI: 10.1016/j.bbadis.2025.167680
OpenAlex: W4406596099
Cited by: 15
References: 261
Citations by year: 2026: 8 · 2025: 7
FWCI: 0.93
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 365 d)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Science, Research, and Medicine
Keywords: artificial intelligence in drug discovery, anticancer drug discovery, drug target identification, drug design, clinical trial optimization, drug response prediction, precision oncology, AlphaFold, genomic data management, ethics of AI in healthcare
MeSH terms: Deep Learning, Antineoplastic Agents, Artificial Intelligence, Humans, Neoplasms, Genomics, Drug Discovery, Precision Medicine

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An in-depth review of AI-powered advancements in cancer drug discovery

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