Synergizing Experimental and Computational Methods for Structural Insights and Drug Discovery

Chemical biology increasingly benefits from the close integration of experimental structural techniques and computational approaches to elucidate biomolecular mechanisms and accelerate drug discovery. Recent advances in structural biology, AI-based structure prediction and data-driven modeling have greatly expanded the range of systems in which this integration can be effectively applied, making synergistic workflows accessible across diverse biological targets and disease contexts.This Research Topic aims to showcase studies that combine high-resolution structural and biophysical techniques, such as X-ray crystallography, cryo-EM, NMR, and binding assays, with computational tools that include docking, molecular simulations, virtual screening, big-data resources and AI or deep learning.By emphasizing iterative pipelines that unite experimental evidence with predictive modeling, the Topic seeks to show how integrated strategies can support structural and mechanistic insight discoveries, target validation and accelerate ligand or probe development. Aligned with the mission of Frontiers in Chemical Biology, this Research Topic focuses on work at the chemistry, biology and bioinformatics interface that dissects the mechanisms of enzymes, receptors and transport proteins, and applies structure-guided strategies to enable drug design and optimization in infectious, oncologic and neurodegenerative disease contexts.Understanding and manipulating molecular recognition, catalysis and signaling in chemical biology requires detailed structural and mechanistic information, often enabled by advanced chemical tools and probes. However, key challenges persist: allosteric and transient pockets can be hard to detect; dynamic conformational ensembles complicate static structures; and the vastness of chemical space makes exhaustive experimental screening unfeasible.In parallel, computational modeling, big-data resources and AI methods now support testable hypotheses on target druggability, binding modes and functional vulnerabilities, provided they are anchored in robust structural and biophysical data. There is a growing need to connect these experimental and in silico components into iterative workflows—for example, using fragment-based crystal structures to seed virtual screening, or feeding MD-refined binding models into medicinal chemistry cycles.This Research Topic highlights integrated chemical biology strategies in which structural data, mechanistic insight and computational predictions are combined to drive target validation and to discover and optimize ligands in a rational, data-driven manner.This Research Topic welcomes contributions that sit firmly within chemical biology and emphasize synergistic experimental–computational approaches for structural insight and drug discovery. Areas of interest include, but are not limited to:• Structural and biophysical characterization of enzymes, receptors, and transporters in health and disease;• Fragment-based and structure-guided ligand discovery, including optimization of inhibitors and chemical probes;• Biophysical and structural analysis of target–ligand interactions (e.g., crystallography, cryo-EM, NMR, MS, SPR, calorimetry);• Computational modeling, docking, molecular simulations , virtual screening and AI or ML workflows informed by experimental data;• Database resources, web servers and computational platforms supporting target assessment, structural analysis or ligand discovery;• Integrative pipelines that couple high-throughput or fragment screening with in silico design;• Chemical biology tools and nanoplatforms that enable structural or mechanistic interrogation of biological systems. Topic editors: Annarita Fiorillo, Patrizio Di Micco, Vikash Kumar Dubey Source: https://www.frontiersin.org/research-topics/76683/synergizing-experimental-and-computational-methods-for-structural-insights-and-drug-discovery