Artificial Intelligence for Polypharmacology and Resistance-Aware Cancer Drug Discovery

Cancer is increasingly recognized as a dynamic and adaptive network disease characterized by pathway redundancy, tumor heterogeneity, clonal evolution, and rapid acquisition of therapeutic resistance. Although artificial intelligence (AI) has significantly advanced biomarker discovery, tumor classification, and single-target drug design, durable clinical responses often require multi-target modulation, rational drug combinations, or repurposed agents capable of perturbing interconnected oncogenic signaling networks.Traditional “one target-one drug” paradigms frequently fail due to compensatory pathway activation and mutation-driven escape mechanisms. Emerging computational strategies -including network pharmacology, polypharmacology modeling, molecular docking and dynamics simulations, mutation landscape analysis, and nanoinformatics- offer powerful tools to design therapeutics that anticipate and overcome resistance.Despite growing interest in AI-driven oncology, comparatively limited attention has been devoted to resistance-aware, multi-target, and systems-level computational medicinal chemistry approaches. Addressing this gap is critical for the development of next-generation cancer therapeutics.This Research Topic aims to advance the application of artificial intelligence and computational medicinal chemistry toward:- Multi-target drug discovery- Resistance-aware therapeutic design- Network-guided intervention strategies- Rational drug repurposing and combination therapy modelingBy shifting the focus from single-target precision strategies to systems-level and adaptive therapeutic design, this Topic seeks to highlight innovative methodologies that improve the robustness, durability, and translational potential of cancer therapeutics.We welcome original research articles, systematic reviews, methods papers, brief reports, and perspective articles addressing AI-driven and computational approaches in polypharmacology and resistance-aware cancer therapeutics.Topics of interest include, but are not limited to:- AI-driven polypharmacology and multi-target drug design- Network pharmacology modeling of oncogenic signaling pathways- Computational prediction and mapping of resistance mutations- Molecular docking and molecular dynamics simulations for multi-protein targeting- AI-guided drug repurposing strategies in oncology- Prediction of drug–drug synergy and rational combination therapies- Modeling compensatory signaling and adaptive tumor responses- Systems biology approaches integrating mutation data with drug response profiles- Nanoinformatics and AI-guided design of dual- or multi-drug nanoformulations- Structure-based design targeting resistant or previously “undruggable” oncogenic proteins- Integration of computational predictions with experimental validation in preclinical cancer modelsSubmissions that combine computational modeling with experimental or translational validation are particularly encouraged. Interdisciplinary contributions bridging computational medicinal chemistry, structural biology, systems oncology, nanotechnology, and translational research are highly welcome. Topic editors: Shaban Ahmad, Khalid Raza, NAGMI BANO Source: https://www.frontiersin.org/research-topics/77977/artificial-intelligence-for-polypharmacology-and-resistance-aware-cancer-drug-discovery