Artificial Intelligence and Protein Dynamics: Transforming Disease Modeling and Therapeutic Developments, Drug Discovery and Lead Optimization

Artificial intelligence (AI) is revolutionising the field of drug discovery and disease modelling, with a significant impact on protein dynamics and the study of protein ensembles. AI methodologies such as machine learning, deep learning, and computational modelling are pivotal in understanding the complex mechanisms of protein interactions and their implications in diseases. These AI-driven techniques enable researchers to simulate protein behaviours, predict conformational changes, and explore the dynamics of protein ensembles in ways that traditional methods cannot match.This Research Topic aims to explore the cutting-edge AI applications that facilitate the comprehensive analysis of protein dynamics, enhancing the prediction of protein structures, interactions, and functions. By integrating AI with protein science, this collection seeks to push the boundaries of how we model disease processes at the molecular level and develop targeted therapies. Emphasising protein ensembles, the topic will highlight how AI contributes to capturing the diversity of protein states involved in health and disease, aiding in the identification of novel therapeutic targets.The scope of this themed article collection includes, but is not limited to:• AI algorithms for modelling and predicting protein dynamics.• Machine learning approaches for analysing protein ensemble data.• Deep learning frameworks for understanding protein-protein interactions.• AI-driven methods for simulating the physicochemical properties of proteins.• Computational techniques for mapping protein conformational landscapes.• ML approaches in computational toxicology• AI methods in predicting peptide or protein aggregation and amyloidosis in disease• Integrative models combining genomics, proteomics, and AI to elucidate protein functions in disease.• AI applications in the design and optimisation of modulators targeting specific protein states.• Case studies and applications of AI in understanding the role of protein dynamics in drug discovery.• Computational challenges in the simulation and prediction of protein behaviours.Topic Editors declare the following potential competing interests: Dr. Michael Rock Goldsmith (Chief Innovation Scientist and co-founder, Congruence Therapeutics); Dr. Ross Charles Walker (founder and sole owner, RCW Computing and RCW Consulting; Advisory Board member, ProbiusDX Inc. and PhareBio Inc.); Sina Sarparast (employee, Congruence Therapeutics); Dr. Maximilian CCJC Ebert (employee and Executive Director of Computational Molecular Sciences, Congruence Therapeutics); Prof. G. Andres Cisneros (former PhD students: Hedieh Torabifard and Alice Walker); Dr. Rebecca J. Swett (Director of Computational Chemistry Innovation, X-Chem; multiple patents filed with Relay Therapeutics and Vertex Pharmaceuticals). Topic editors: Michael Rock Goldsmith, Gerardo Andres Cisneros, R.J. Swett, Ross Walker Source: https://www.frontiersin.org/research-topics/70134/artificial-intelligence-and-protein-dynamics-transforming-disease-modeling-and-therapeutic-developments-drug-discovery-and-lead-optimization