Machine learning for protein folding and dynamics
Year: 2019
Type: review
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: bronze
Funders: National Science Foundation, Welch Foundation, European Commission, Deutsche Forschungsgemeinschaft, Ministerio de Economía y Competitividad, Einstein Stiftung Berlin, Berlin Mathematics Research Center MATH+, European Research Council, Horizon 2020, European Regional Development Fund, Division of Chemistry
Awards: G1156025916, G1211480464, G1312815840, G188828862, G3479333676, G3784915899, G4067678199, G4304686666, G477274039, A Centre of Excellence in Computational Biomedicine, BIGDATA: IA: Collaborative Research: In Situ Data Analytics for Next Generation Molecular Dynamics Workflows, G5686627504, G6265512714, Collaborative Research: SI2-CHE: ExTASY Extensible Tools for Advanced Sampling and analYsis, G651204332, G654969073, G6693813198, G6760545195, G7258449643, G7623829941, G7669873645, Center for Theoretical Biological Physics - Houston, D3SC: CDS&E: Learning molecular models from microscopic simulation and experimental data, G8538982517
OpenAlex ID: W2989745558