Conformational changes essential for protein function involve transitions through multiple short-lived, high-energy states within the complex free energy landscape. While existing methods, such as Markov State Models and non-Markovian approaches buil...
Journal of chemical information and modeling
Nov 17, 2025
Data-driven modeling based on machine learning (ML) is becoming a central component of protein engineering workflows. This perspective presents the elements necessary to develop effective, reliable, and reproducible ML models, and a set of guidelines...
Journal of chemical information and modeling
Nov 17, 2025
Predicting protein function from its primary sequence is a fundamental challenge in computational biology. While deep learning has excelled, the optimal representation of sequence data remains an open question. This study explores protein sonificatio...
Journal of chemical information and modeling
Nov 14, 2025
Topological data analysis (TDA) has emerged as a powerful framework for extracting robust, multiscale, and interpretable features from complex molecular data for artificial intelligence (AI) modeling and topological deep learning (TDL). This review p...
Rapid and label-free evaluation of induced pluripotent stem cell (iPSC) pluripotency is critical for advancing regenerative medicine and clinical applications. Although traditional genomics- and proteomics-based pluripotency assessment methods are re...
Journal of chemical information and modeling
Nov 13, 2025
Accurate identification of druggable pockets and their features is essential for structure-based drug design and effective downstream docking. Here, we present RAPID-Net, a deep learning-based algorithm designed for accurate prediction of binding poc...
Recent advances in Artificial Intelligence have enabled multi-modal systems to model and translate diverse information spaces. Extending beyond text and vision, we introduce OneProt, a multi-modal Deep Learning model for proteins that integrates stru...
In this work we introduce TorchANI-Amber, an interface for routine molecular dynamics simulations of biomolecular systems using ANI-style machine learning potentials. TochANI-Amber incorporates the ANI neural network potentials into the Amber softwar...
Journal of chemical information and modeling
Nov 4, 2025
Protein and protein-protein complex conformations play a critical role in biological functions, while exploring these via traditional molecular dynamics (MD) simulation is computationally expensive. Enhanced sampling methods offer improvements but re...
Proceedings of the National Academy of Sciences of the United States of America
Nov 4, 2025
Modeling the conformational heterogeneity of protein-small molecule interactions is important for understanding natural systems and evaluating designed systems but remains an outstanding challenge. We reasoned that while residue-level descriptions of...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.