ABSTRACT
Engineering of molecules for applications in drug discovery, materials design, and effective catalysts require traversal of large molecular design spaces and elucidation of the design rules. Molecular simulations can furnish detailed insights into the governing forces and mechanisms of various molecular systems and are also – particularly when coupled with machine learning-enabled techniques – capable of performing high throughput virtual screening of large candidate libraries and property predictions. In my talk, I will present the development and application of computational approaches involving molecular dynamics simulations, enhanced sampling methods and machine learning to (a) understand the design rules for engineering peptide assembly on material surfaces for biosensor applications, (b) discovery of molecular probes with optimal sensitivity and selectivity for simplifying purification of organic water contaminants, and (c) prediction of the currently inaccessible molecular structure of integrin along its activation pathway.
Biosketch
Siva Dasetty is a postdoctoral associate in Ferguson Lab at the Pritzker School of Molecular Engineering at the University of Chicago. He received bachelor’s in chemical engineering from National Institute of Technology, Surathkal, India in 2012, master’s in chemical engineering from Clemson University in 2015, and a Ph.D. in chemical engineering from Clemson University in 2019.