The Tiwary Group at
University of Maryland, College Park
Current research highlight
With RAVE we can achieve ligand unbinding (benzene-lysozyme here) with a static bias learnt from deep learning in b/w 2 to 40 nanosec (varying from simulation to simulation, with 100% success), for a 100 millisec process! The method needs minimal human intuition, and gives reasonable estimates of absolute binding affinity.
Reference: Ribeiro & Tiwary, J. Chem. Theor. Comp. (2018)
See previous research highlights on our research page
The Tiwary group does inter-disciplinary theoretical and computational research to model and predict thermodynamics, dynamics and their interplay in complex real-world systems, relevant to biophysical, chemical and materials sciences. A common theme across these diverse systems is that many of these are plagued with hard to model rare events. To tackle these we develop and use theoretical and computational tools drawing primarily from statistical mechanics, information theory and machine learning.
ABOUT PRATYUSH TIWARY:
I am an Assistant Professor at the University of Maryland, College Park. I have a joint position in the Department of Chemistry and Biochemistry and the Institute for Physical Science and Technology. I am also an affiliated faculty member of the Chemical Physics program and the Biophysics program. I can be reached on my office phone 301 405 2148. My office address is Room 1115A, Institute for Physical Science and Technology (Building 085), University of Maryland, College Park, MD 20742.
I received my PhD and MS in Materials Science from Caltech, working with Axel van de Walle, and finished my undergraduate degree in Metallurgical Engineering at the Indian Institute of Technology, Banaras Hindu University, Varanasi. Prior to starting my tenure-track position, I have been a postdoc in the Department of Chemistry at Columbia University, where I worked with Bruce Berne, and at the Department of Chemistry & Applied Biosciences at ETH Zurich, where I worked with Michele Parrinello.
I am a co-organizer of the Informal Statistical Physics seminar series with Chris Jarzynski. Please email me if you have suggestions for speakers!
In the fall 2018 semester, I am teaching the 3-credit course CHEM 481 "Physical Chemistry I".
In the spring 2018 semester, I taught the 3-credit course CHEM 687 "Statistical Mechanics and Chemistry".
In winter 2018, I was one of the several instructors teaching BCHM 677 "Computational Tools in Biochemistry".
Please see the teaching page for more details.
- Dec 14, 2018: Our invited perspective article "Kinetics of Ligand-Protein Dissociation from All-Atom Simulations: Are We There Yet?" has been accepted for publication in the Future of Biochemistry: The International Issue of Biochemistry
- Dec 7, 2018: Our work "Towards Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE" has been accepted for publication in the Journal of Chemical Theory and Computation!
- Nov 30, 2018: Our work "Multi-dimensional spectral gap optimization of order parameters (SGOOP) through conditional probability factorization" has been accepted for publication in the Journal of Chemical Physics. Graduate student Zachary Smith's first publication ever!
- Oct 29, 2018: We are very grateful to @Schrodinger (https://www.schrodinger.com/) for their generous support of the @tiwarylab at University of Maryland. We are very excited about our collaboration and look forward to testing and applying our methods in the real world!
- Oct 13, 2018: Physics grad student Freddy Cisneros from has won the best oral presentation award in Physics & Astronomy category at the annual SACNAS conference in San Antonio, Texas for his work on using machine learning to study rare events in condensed matter systems. The Presentation awards recognize the next generation of scientists and STEM leaders for exemplary science. Freddy's achievement also made it to UMD Physics front page, as archived here. Congratulations Freddy!
- Sep 18, 2018: We have been awarded 1.7 million and 1.8 million CPU+GPU hours respectively on XSEDE-PSC-Bridges and MARCC-Blue Crab supercomputers respectively.