Data Science Talk

Vassilis Tsotras (organizer)

Multidisciplinary Research Building (MRB) Seminar Room,

Sponsored by the Data Science Center, the purpose of the Data Science Seminar is to foster collaborations between the core Data Science faculty (CSE/ECE/STAT) and faculty from other sciences that face Data Science problems in their research. After the talk, there will be time for discussions.The talk will be given by Prof. Chia-en Chang, Department of Chemistry, UCRTITLE:Machine Learning Guided Modeling of Ligand-Protein Binding Energy Landscape: Applications in Small Molecule and Protein-based Drug Design.ABSTRACT:Molecules in cells constantly move. The motions of proteins in living cells can be simple fluctuations or functional. Therefore, investigating protein dynamics is crucial for understanding protein function and for accurately compute ligand-protein binding free energy landscape. Because experimental structures are static conformations, classical or enhanced molecular dynamics (MD) simulations are commonly used for conformational sampling. Machine/deep learning approaches can then be used to analyze MD results and assist conformational sampling and energy calculations. In this presentation, we will focus on modeling ligand-receptor binding/unbinding pathways to compute protein-drug binding thermodynamics and kinetics for drug development. We will show the binding free energy landscape constructed by Binding Kinetics Toolkit (BKiT), a program using post-analysis, principal component analysis and milestoning theory to predict drug binding kinetics. We will also discuss use of machine learning and deep learning to enhance protein conformational sampling to model protein conformational transition and other applications.*** Pizza and refreshments will be provided! ***

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Data Science Talk

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Data Science Talk

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