Accelerating Cancer Drug Discovery: The Urgent Need for True Interdisciplinary Convergence of Computational Science and Organic Synthesis

Abstract

Cancer drug discovery remains a slow, costly, and resource-intensive process, limiting the timely development of effective anticancer therapies. The convergence of computational modeling, artificial intelligence (AI), and organic synthesis has the potential to transform the field from empirical screening to rational, mechanism-based, and increasingly personalized drug design. Advances in machine learning, quantitative structure–activity relationship (QSAR) modeling, structure-based drug design (SBDD), molecular generative models, and AI-assisted retrosynthetic planning now enable the rapid identification of cancer-specific molecular targets and the design of synthetically feasible compounds with optimized efficacy, selectivity, and safety profiles. Despite these advances, several critical challenges continue to impede clinical translation, including the limited synthetic accessibility of many computationally generated candidates, fragmented and non standardized datasets, insufficient experimental validation, and a persistent skills gap between computational scientists and synthetic chemists. Overcoming these barriers will require integrated drug discovery platforms that combine AI-driven molecular design, automated or robotic synthesis, high-throughput biological screening, and iterative experimental feedback. Continued investment in data infrastructure, interdisciplinary collaboration, and workforce training will be essential to fully realize the potential of this convergence. Ultimately, integrating computational intelligence with modern synthetic chemistry may accelerate the development of safer, more effective, and increasingly personalized anticancer therapeutics.