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See a full list of my publications on Google Scholar.
Physics-Guided Neural Networks for 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.


- Calculating the Binding Entropy of Host-Guest Systems with Physics-Guided Neural Networks. IEEE-BIBM 2022 (LINK

- A Physics-Guided Neural Network for Predicting Protein-Ligand Binding Free Energy: From Host-Guest Systems to the PDBbind Database, Biomolecules 2022 (LINK)

- Calculation of Protein-Ligand Binding Free Energy Using a Physics-Guided Neural Network, IEEE-BIBM 2021 (LINK)

Deep Generative Models for
New Ligand Discovery

Deep generative models have recently been introduced for sampling the chemical space and discovering new ligands. Unlike experimental methods, the computational pipeline enables researchers to modify the generative algorithm quickly and effortlessly test the results from different perspectives. This paradigm shift opens up new opportunities to create a rich database of artificial structures and evaluate their synthesizability and viability.


- Physics-Guided Deep Generative Model For New Ligand Discovery. ACM-BCB 2023 (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.


- 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)

- Binding Free Energy of the Novel Coronavirus Spike Protein and the Human ACE2 Receptor: An MMGB/SA Computational Study, ACM-BCB 2020 (LINK)


- Multidimensional Global Optimization and Robustness Analysis in the Context of Protein-Ligand Binding, JCTC 2020 (LINK)

- Robustness of Multidimensional Optimization Outcomes: A General Approach and a Case Study, SpringSim 2020 (LINK)

- Grid-based surface generalized Born model for calculation of electrostatic binding free energies, JCIM 2017 (LINK)

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.

Global Optimization of Biomolecular Surfaces

Past Projects

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)

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