ArticleFrontiers in molecular biosciences2021
DESP: Deep Enhanced Sampling of Proteins' Conformation Spaces Using AI-Inspired Biasing Forces.
Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- From Atoms to Cells: AI-Based Structure Prediction Fueling Molecular Dynamics Simulations in Computational Structural Biology.Advances in experimental medicine and biology · 2026Review
- Machine Learning Generation of Dynamic Protein Conformational Ensembles.Molecules (Basel, Switzerland) · 2023Review
- Direct generation of protein conformational ensembles via machine learning.Nature communications · 2023Article
- Protein Function Analysis through Machine Learning.Biomolecules · 2022Review
- Advanced Sampling Methods for Multiscale Simulation of Disordered Proteins and Dynamic Interactions.Biomolecules · 2021Review
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Abstract
The molecular structures (i.e., conformation spaces, CS) of bio-macromolecules and the dynamics that molecules exhibit are crucial to the understanding of the basis of many diseases and in the continuous attempts to retarget known drugs/medications, improve the efficacy of existing drugs, or develop novel drugs. These make a better understanding and the exploration of the CS of molecules a research hotspot. While it is generally easy to computationally explore the CS of small molecules (such as peptides and ligands), the exploration of the CS of a larger biomolecule beyond the local energy well and beyond the initial equilibrium structure of the molecule is generally nontrivial and can often be computationally prohibitive for molecules of considerable size. Therefore, research efforts in this area focus on the development of ways that systematically favor the sampling of new conformations while penalizing the resampling of previously sampled conformations. In this work, we present
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