Modeling Boltzmann-weighted structural ensembles of proteins using artificial intelligence–based methods
Akashnathan Aranganathan, Xinyu Gu, Dedi Wang, Bodhi P. Vani, Pratyush Tiwary
Abstract
This review highlights recent advances in AI-driven methods for generating Boltzmann-weighted structural ensembles, which are crucial for understanding biomolecular dynamics and drug discovery. With the rise of deep learning models such as AlphaFold2, there has been a shift toward more accurate and efficient sampling of structural ensembles. The review discusses the integration of AI with traditional molecular dynamics techniques as well as experiments, the challenges of conformational sampling, and future directions for AI-driven research in structural biology, particularly in drug discovery and protein dynamics.
Topics & Concepts
Artificial intelligenceBoltzmann machineComputer scienceArtificial neural networkComputational biologyPattern recognition (psychology)Statistical physicsMachine learningBiological systemPhysicsBiologyProtein Structure and DynamicsComputational Drug Discovery MethodsBioinformatics and Genomic Networks