Cryo-EM Structure-Function Studies for Rational Drug Discovery

Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling high-resolution visualization of biomolecules in native conformational states without crystallization. This technique provides unprecedented views of protein-drug interactions, allosteric mechanisms, and conformational changes crucial for rational drug discovery. Recent technological advances have pushed resolution limits below 2 Å, while artificial intelligence and machine learning further accelerate the field through enhanced image processing, automated particle picking, and improved 3D reconstruction. AI-driven approaches like AlphaFold complement experimental data, and machine learning models predict drug-target interactions, optimizing compound libraries. This synergy between cryo-EM and AI/ML transforms drug discovery by reducing timeframes and increasing therapeutic development success rates.

The primary challenge in modern drug discovery lies in bridging the gap between static structural data and dynamic biological function, leading to high failure rates and prolonged development timelines. This research topic collection addresses how cryo-EM structure-function studies can revolutionize rational drug design by capturing native protein conformations and their functional states. Recent advances in single-particle analysis, time-resolved cryo-EM, and AI-enhanced image processing enable unprecedented visualization of drug-target interactions and allosteric mechanisms. Integration with machine learning algorithms for virtual screening and structural prediction accelerates lead optimization. By combining high-resolution structural insights with computational approaches, we can develop more effective therapeutics with reduced off-target effects and improved clinical success rates.

This Research Topic advances cryo-EM structural studies integrated with rational drug discovery, seeking manuscripts that bridge structural biology with pharmaceutical sciences. We welcome submissions on topics including, but not limited to:

• Studies on high-resolution cryo-EM structures of drug targets presenting target alone, target-drug complexes and allosteric mechanisms;

• Method articles on AI/ML-enhanced workflows including deep learning for image processing and 3D reconstruction;

• Research demonstrating structure-based drug design applications, lead optimization, and structure-activity relationships;

• Time-resolved structural dynamics studies exploring conformational changes and binding kinetics;

• Integrated computational-experimental strategies combining cryo-EM with molecular dynamics, virtual screening, and predictive modeling for enhanced drug design.