Arjun Saha
- Assistant Professor, Chemistry & Biochemistry
Education
- Post-Doctoral Research Associate, University of Southern California, Los Angeles, (, 2013 Chemistry Nobel Laureate)
- Post-Doctoral Scientist, , San Diego
- Ph.D. in Physical Chemistry, Indiana University, Bloomington ()
- MS in Chemistry, University of North Bengal, Siliguri, West Bengal, India
- BS in Chemistry, University of North Bengal, Siliguri, West Bengal, India
Teaching Schedule
| Course Num | Title | Meets |
|---|---|---|
| CHEM 561-401 | Physical Chemistry I | WF 12:30pm-1:45pm |
| CHEM 561-601 | Physical Chemistry I | F 2pm-2:50pm |
| CHEM 561G-401 | Physical Chemistry I | WF 12:30pm-1:45pm |
| CHEM 561G-601 | Physical Chemistry I | F 2pm-2:50pm |
| CHEM 762-001 | Topics in Physical Chemistry: Computer Modeling in Chemistry | MW 3:30pm-4:45pm |
| CHEM 935-001 | Advanced Seminar in Physical Chemistry | No Meeting Pattern |
Research Interests
Research in Saha lab will involve development and application of novel computational chemistry and biophysics approaches to gain fundamental understanding of remarkable biological processes and their relation to complex diseases. This knowledge will further be used to design small molecules either to inhibit or to enhance particular biological function facilitating drug discovery for cancer and neurodegenerative diseases (e.g., Alzheimer’s, Parkinson’s, Dementia). To achieve this goal, state-of-the-art computer aided drug discovery techniques will be implemented in collaboration with medicinal chemistry, biochemistry and experimental biophysical research groups (both in academia and in pharmaceutical industry). Researchers in Saha lab will develop skills and expertise in advanced molecular modeling techniques (e.g., Machine Learning, Coarse grained Molecular Dynamics, Quantum Mechanics/Molecular Mechanics, Cheminformatics) as well as simulated exposure to computational drug discovery research as in Big Pharma.
Projects
- Understanding protein misfolding through machine learning and large-scale molecular dynamics simulation.
- Design of covalent inhibitors for ubiquitin proteasome system through computational enzymology.
- Understanding mechanochemical process in motor proteins through electrostatic-enhanced coarse-grained molecular dynamics.