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RESEARCH INTERESTS
RESEARCH INTERESTS
Molecular Dynamics & Enhanced Sampling Simulations
Machine Learning (Artificial Intelligence in Chemistry)
Development of advanced simulations methods
Applications in biophysics, soft-matter, and nano-bio systems
Our research is mostly interdisciplinary at the interfaces of chemistry, materials, and biology. A major portion of the work is focused on the development of advanced simulation methods and their applications in the study of complex chemical and biological systems and processes. We apply enhanced sampling methods accompanied by advanced machine learning techniques to investigate enzymes relevant to human disease and those of industrial importance. The other projects involve investigating pharmaceutical crystallization, separation, and transport across bio-membranes and porous materials.
News
News
Recent works:
Modelling Enzyme-like Catalytic Reaction on Ligand-protected Metal Nanoclusters
Vikas Tiwari and Tarak Karmakar* Chem. Sci., 2025, link
Vikas Tiwari and Tarak Karmakar*, Nano Lett., 2025 link
Unveiling Interactions of Peptide-bound Monolayer Protected Metal Nanocluster with Lipid Bilayer
Soumya Mondal and Tarak Karmakar*, J. Phys. Chem. Lett., 2025 link
Enhanced Sampling Simulations of RNA-peptide Binding using Deep Learning Collective Variables
Nisha Kumari, Sonam Dhull, and Tarak Karmakar* J. Chem. Inf. Model., 2025, link
Unveiling the Role of Solvent in Solution Phase Chemical Reactions using Deep Potentials-based Enhanced Sampling Simulations, Anmol and Tarak Karmakar*, J. Phys. Chem. Lett. 2024, 15, 39, 9932–9938, link
Graph Neural Networks for Predicting Solubility in Diverse Solvents using MolMerger incorporating Solute-solvent Interactions
Vansh Ramani and Tarak Karmakar*,
J. Chem. Theory Comput., 2024, link
A few simulations movies...
Nano-bio-medicine
Solution Crystallization
Phase Transitions
Biomolecular Simulations
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