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Dear All,

SOLID STATE & STRUCTURAL CHEMISTRY UNIT

INDIAN INSTITUTE OF SCIENCE

BANGALORE 12

SPECIAL SEMINAR

 

By

Dr. Arjun Saha

University of Southern California, Los Angeles, USA

Title

Development and Application of QM Methods for Large Molecular Systems and its Future in Drug Discovery

on

Thursday, 18th  October, 2019 at 4:00 PM

 

Venue : SSCU Auditorium

ALL ARE CORDIALLY INVITED TO ATTEND

 

CHAIRMAN, SSCU

Abstract:

Understanding the biochemical process in a certain system in terms of atomistic resolution requires computational treatment of different time scales (e.g. chemical events as well as physical movements). Towards that future of developing multiscale simulation technique, I will present my research in three principle areas of computational chemistry: a) Quantum chemistry b) Molecular mechanics and Molecular dynamics and c) Cheminformatics.

The last three decades have seen dramatic progress in the development and application of ab initio quantum chemical methods. However, as the molecule gets larger, it becomes computationally prohibitive to treat the entire system with accurate and reliable theoretical models. Herein, I will present progress towards this direction by extending the power and applicability of highly accurate ab initio methods for the treatment of large molecular systems. We have proposed new “Fragment-based Quantum Chemical Methods”1,2 that partitions a large “otherwise impossible-to-tackle problem” into a collection of small “computationally tractable problems” to predict the absolute and relative energies of large systems like polypeptides, water clusters and other complex systems. Amidst the exciting time of remarkable computer innovation, I will also discuss the potential and future of these methodologies in many avenues of computational drug discovery. In that direction, first I will present two cutting-edge computational chemistry applications in modern drug discovery: 1) Predicting the binding affinity of fatty acid amide hydrolase inhibitors by free energy perturbation (FEP) combined with enhanced sampling techniques, 3 2) Exploration of vast chemical space of drug molecules for different target classes (Kinase, GPCR, Ion channels and PPIs) using machine-learning based cheminformatics methods. 4 Subsequently, I will briefly show an ongoing application of multiscale simulation technique to understand complete biochemical pathway (e.g. ubiquitin recognition, substrate binding, substrate translocation and proteolysis) in proteasomes. Finally, I will discuss existing challenges of these MM and MD based methodologies and how we can move forward to address those challenges with fragment-based quantum chemistry models to bring the impact and excellence of quantum chemistry in studying biochemical systems and hence their implications in drug discovery.

References:

1. Arjun Saha and Krishnan Raghavachari, Journal of Chemical Theory and Computation, 2013, 10, 58.

2. Krishnan Raghavachari and Arjun Saha, Chemical Reviews, 2015, 115, 5643.

3. Arjun Saha, Amy Shih, Tara Mirzadegan and Mark Seierstad, Journal of Chemical Theory and Computation,

2018, 14, 5815.

4. Arjun Saha, Teena Varghese, Annie Liu, Tara Mirzadegan and Michael Hack, Journal of Chemical Information

and Modeling, 2018, 58, 2057.

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