Understanding how drugs interact with their molecular targets at the atomic level is central to modern drug discovery. Traditionally, drug development has focused primarily on binding affinity; however, it is now increasingly recognized that binding kinetics—how quickly a drug associates with and dissociates from its target—plays a decisive role in drug efficacy, selectivity, and safety. These investigations into molecular recognition and mechanism highlight the essential role of chemical biology in understanding and optimizing drug action. Recent advances in experimental biophysics, molecular simulations, and machine learning provide unprecedented opportunities to capture and predict the dynamic processes underlying drug–target recognition. These developments create a timely need to integrate diverse methodologies and perspectives to deepen our understanding of binding mechanisms at atomic resolution.

This Research Topic aims to bring together researchers working at the intersection of computational chemistry, structural biology, biophysics, medicinal chemistry, and chemical biology, as well as data-driven modeling to unravel drug–target binding kinetics and mechanisms. Despite significant progress, challenges remain in accurately characterizing energy landscapes, capturing rare binding events, and linking kinetic signatures to pharmacological outcomes. By integrating chemical biology principles with computational and experimental methods, we hope to accelerate the translation of molecular insights into clinically meaningful advances. Addressing these challenges requires synergy between atomistic simulations, free energy methods, kinetic modeling, high-resolution experimental approaches, and AI-driven prediction strategies. By uniting these approaches, we seek to establish a framework that goes beyond static affinity measures and embraces binding kinetics and mechanism as core pillars of rational drug design. Contributions are expected to illuminate how atomic-level insights can be translated into predictive models, guiding the design of more selective, potent, and clinically effective therapeutics.

• We welcome contributions across a wide range of themes, including but not limited to:
• Computational studies of drug–target binding kinetics and free energy landscapes
• Molecular dynamics and enhanced sampling methods for mechanistic insights
• Experimental approaches to characterize kinetic profiles and conformational dynamics
• Integration of AI/ML with physics-based methods for predicting binding mechanisms
• Case studies highlighting the role of binding kinetics in drug efficacy and selectivity
• Methodological advances for multiscale modeling of drug–target interactions

We encourage the submission of Original Research, Reviews, Mini-Reviews, Methods, and Perspectives. By fostering a multidisciplinary dialogue, this Research Topic will serve as a platform to accelerate the discovery of new principles, methodologies, and therapeutic opportunities grounded in the atomic-scale understanding of drug–target interactions.

Are you interested in contributing a manuscript to this Research Topic?

Drug–Target Binding Kinetics and Mechanisms: Toward Rational and Predictive Drug Discovery