Artificial Intelligence-Based Halogenated Chalcone Drug Discovery: An Integrated Framework for Rational Anticancer Lead Optimization

Abstract

Halogenated chalcones are emerging as an exciting class of potential anticancer scaffolds with respect to medicinal chemistry; they exhibit structural flexibility, multitarget pharmacology, and allow for rapid medicinal chemistry optimization. The biological activity associated with halogen substitutions on the scaffold is influenced by numerous non-linear SARs. As a result, traditional trial-and-error optimization approaches are typically very time-consuming and reduce the ability to find clinically relevant lead structures. Recent advancements in artificial intelligence (AI), however, now provide a means to optimize various aspects of drug discovery through the use of predictive models derived from large amounts of data, including the development of anticancer leads. This review will present a complete framework for designing optimized halogenated chalcone derivatives using AI, which assesses the role of molecular descriptor selection, QSAR modeling, molecular docking, molecular dynamics simulations, machine learning, deep learning, graph neural network technology, explainable AI, and generative AI to accelerate anticancer lead discovery. Furthermore, this review aims to integrate all these computational tools into one cohesive medicinal chemistry approach while providing an evaluation of their advantages and disadvantages and their translational relevance compared to previous reviews that assessed each individually. Finally, this review also addresses the potential of multimodal learning and next-generation generative AI methods along with some challenges currently facing researchers, i.e., the lack of integration among existing computational workflows, limited availability of quality datasets, and limited experimental validation.