Emerging Trends in Artificial Intelligence‐Driven Drug Discovery: Balancing Chemical Design and Pharmacological Properties for Cancer Therapy

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.