AI4Proteins: Designing molecular models by ML & experimental data – Professor Cecilia Clementi скачать в хорошем качестве

AI4Proteins: Designing molecular models by ML & experimental data – Professor Cecilia Clementi

This seminar forms part of the AI3SD and RSC-CICAG AI4Proteins Series. This series is sponsored by Arctoris and Schrödinger. This video is the eleventh talk in the Protein Structure Prediction Conference. Designing molecular models by machine learning and experimental data – Professor Cecilia Clementi Abstract: The last years have seen an immense increase in high-throughput and high-resolution technologies for experimental observation as well as high-performance techniques to simulate molecular systems at a microscopic level, resulting in vast and ever-increasing amounts of high-dimensional data. However, experiments provide only a partial view of macromolecular processes and are limited in their temporal and spatial resolution. On the other hand, atomistic simulations are still not able to sample the conformation space of large complexes, thus leaving significant gaps in our ability to study molecular processes at a biologically relevant scale. We present our efforts to bridge these gaps, by exploiting the available data and using state-of-the-art machine-learning methods to design optimal coarse models for complex macromolecular systems. We show that it is possible to define simplified molecular models to reproduce the essential information contained both in microscopic simulation and experimental measurements. Bio: Cecilia Clementi is Einstein Professor of Physics at Freie Universität (FU) Berlin, Germany. She joined the faculty of FU in June 2020 after 19 years as a Professor of Chemistry at Rice University in Houston, Texas. Cecilia obtained her Ph.D. in Physics at SISSA and was a postdoctoral fellow at the University of California, San Diego, where she was part of the La Jolla Interfaces in Science program. Her research focuses on the development and application of methods for the modeling of complex biophysical processes, by means of molecular dynamics, statistical mechanics, coarse-grained models, experimental data, and machine learning. Cecilia’s research has been recognized by a National Science Foundation CAREER Award (2004), and the Robert A. Welch Foundation Norman Hackerman Award in Chemical Research (2009). Since 2016 she is also a co-Director of the National Science Foundation Molecular Sciences Software Institute. Further details from this series can be found at: https://www.ai3sd.org/ai4proteins This video is an output from the AI3SD Network+ (Artificial Intelligence and Augmented Intelligence for Automated Investigations for Scientific Discovery) which is funded by EPSRC under Grant Number EP/S000356/1 DOI Link: http://dx.doi.org/10.5258/SOTON/P0104