AI- and Data-Driven Molecular Recognition: From Prediction to Design

Molecular recognition, a central tenet of molecular biosciences, underlies critical biological functions and a wide range of applications from drug development to materials science. Traditionally, understanding and optimizing how molecules interact—whether through protein-ligand binding or supramolecular host-guest systems—has demanded labor-intensive experimentation and computational modeling. Despite progress, challenges persist in predicting how flexible and structurally complex molecules interact within the immense landscape of chemical possibilities, often slowing scientific discovery. Recent advances in computational intelligence, especially machine learning and deep learning, now enable the high-throughput analysis and prediction of molecular interactions with unprecedented accuracy. Emerging studies show these methods can effectively learn from massive, multidimensional datasets, expediting the identification of new recognition motifs and deepening our understanding of the fundamental forces dictating molecular assembly. Nevertheless, a gap remains in seamlessly connecting these digital breakthroughs with real-world, experimentally validated outcomes—particularly in systems characterized by disorder or dynamic flexibility.This Research Topic aims to highlight and accelerate the convergence of computational intelligence and experimental approaches in molecular recognition. We seek to illuminate strategies that leverage machine learning, artificial intelligence, or advanced simulation techniques to predict and design noncovalent molecular interactions, especially within flexible or complex systems such as soft materials, dynamic proteins, or intricate glycan architectures. By inviting contributions that establish robust connections between in silico predictions and experimental validation, this topic aspires to facilitate a genuinely “closed-loop” discovery paradigm. Central goals include addressing the enduring challenge of binding affinity prediction in disordered environments, showcasing next-generation generative and geometric modeling techniques, and exploring platforms that couple autonomous experimentation with algorithmic design.This Research Topic covers strategies for integrating computational models with experimental feedback in the study and design of molecular recognition phenomena. While the primary focus is on the interplay between machine learning-driven methodologies and experimental testing, submissions may also explore broader ramifications in therapeutics, materials design, and catalysis. To gather further insights in this area, we welcome articles addressing, but not limited to, the following themes:- Systematic prediction of structure-activity and structure-affinity relationships in flexible, complex systems- The integration of in silico design workflows with high-throughput or autonomous experimental platforms- Exploration of large-scale datasets and data-driven identification of novel recognition motifs- Real-world validation of computational models, with iterative feedback between digital prediction and physical testing- Development and benchmarking of generative models or geometric deep learning for receptor and ligand design- Applications of molecular recognition principles toward biosensors, targeted therapeutics, and advanced catalystsWe encourage the submission of Original Research, Review, and Perspective articles. Methodological, conceptual, or application-oriented manuscripts will be considered, provided that the central focus remains on fundamental advances in molecular recognition. Topic editors: Mohammad Fouad Bayan, Karina S. Machado, MOHAMMAD H. ALYAMI Source: https://www.frontiersin.org/research-topics/80029/ai--and-data-driven-molecular-recognition-from-prediction-to-design