Authors
Mohsin Ali, Muhammad Ali Tajwar, Farid Ahmed, Muhammad Muzammal Hussain, Wai-Yeung Wong, Hai Xiong
Published in
Medicinal research reviews. Jul 27, 2026. Epub Jul 27, 2026.
Abstract
Traditional cancer drug discovery encounters challenges, including lengthy synthesis durations, high costs, and a 90% failure rate in clinical trials, primarily due to inadequate chemical design and drug properties. Artificial intelligence (AI) provides powerful computational tools to overcome these issues by speeding up target identification, predicting properties, and optimizing leads. This review emphasizes the influence of new AI platforms like AlphaFold3, molecular interactions are structurally optimized (MISATO), and ZairaChem on the discovery of oncology drugs. We specifically examine how AI reconciles chemical design with pharmacological feasibility. In addition to evaluating these advancements, we meticulously evaluate methodological challenges, including dataset bias, overfitting, insufficient external validation, and reproducibility issues. Furthermore, the development of complex and targeted modalities, such as antibody-drug conjugates (ADCs), aptamer-drug conjugates (Ap‑DCs), and proteolysis-targeting chimeras (PROTACs), is being explored for cancer treatment using AI. Following a detailed review of regulatory and clinical translation issues, this review presents practical tips for improving model validation, data sharing, and incorporation into medicinal chemistry workflows. By examining successes and persistent limitations, this review article offers a strategic roadmap for leveraging AI to provide clinically translatable cancer therapies with enhanced chemical and pharmacological balance.
PMID:
42504405
Bibliographic data and abstract were imported from PubMed on 27 Jul 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 7
- Comments 0