See a full list of my publications on Google Scholar.
Application of Machine Learning in Protein-Ligand Binding Free Energy Calculation
Development of a physics-based deep learning network to calculate binding free energies of small protein-ligand complexes in an implicit solvent model. We build our model based on a deep learning framework called Deepchem, a python library mainly used in drug discovery processes.
- Calculation of Protein-Ligand Binding Free Energy Using a Physics-Guided Neural Network, IEEE-BIBM 2022 (LINK)
Computational Study of Binding the Novel Coronavirus to the Human ACE2 Receptor
Evaluation of the potential of a molecular mechanics generalized Born surface area (MMGB/SA) approach to estimate the binding free energy between the SARS-CoV-2 spike receptor-binding domain (wild type and mutants) and the human ACE2 receptor.
- Binding Free Energy of the Novel Coronavirus Spike Protein and the Human ACE2 Receptor: An MMGB/SA Computational Study, ACM-BCB 2020 (LINK)
- MMGB/SA Consensus Estimate of the Binding Free Energy Between the Novel Coronavirus Spike Protein to the Human ACE2 Receptor, BioRxic 2020 (LINK)
- An Effective MM/GBSA Protocol for Absolute Binding Free Energy Calculations: A Case Study on SARS-CoV-2 Spike Protein and the Human ACE2 Receptor, Molecules 2021 (LINK)
Global Optimization of Biomolecular Surfaces
Optimization of atomic radii in an implicit solvent model to obtain close agreement with experimental results in terms of calculating electrostatic binding free energies. The underlying massively parallel optimization method, VTDIRECT95, runs on Virginia Tech clusters.
- Multidimensional Global Optimization and Robustness Analysis in the Context of Protein-Ligand Binding, JCTC 202 (LINK)
- Grid-based surface generalized Born model for calculation of electrostatic binding free energies, JCIM 2017 (LINK)
- Robustness of Multidimensional Optimization Outcomes: A General Approach and a Case Study, SpringSim 2020 (LINK)
Structure-Based Analysis of Protein Binding Pockets
Introduction of a novel geometrical metric to identify protein binding pockets. The proposed metric is based on the von Neumann entropy of the weighted Delauney triangulation of protein pockets.
- Structure-Based Analysis of Protein Binding Pockets Using Von Neumann Entropy, ISBRA 2014 (LINK)