Welcome to Comistry
Welcome to Comistry
'Comistry' group works in the computational exploration of molecules and the discovery of nature-inspired drugs. We merge the rich, century-long knowledge of chemistry with the prowess of Computational Chemistry, Computational Structural Biology, and Machine Learning.
We combine Molecular Docking (Docking) and Molecular Dynamics (MD) to study drug-like molecules, disease-relevant proteins, enzymes, and protein-ligand complexes. Examples of projects include:
Explore ligand-protein interactions, in collaboration with experimentalists, that are relevant to disease
Explore how metabolites from medicinal herbs and plants interact with druggable protein targets.
Investigate interaction of substrates in the binding pockets of enzymes (L-amino acid oxidases and Nitrilases).
Predict secondary RNA structures from primary sequences using machine learning models.
Explore how DNA polymerase discriminate nucleotides and correct the mismatched ones when replicating DNA. This molecular level of understanding is essential in exploring the working mechanism of DNA- or RNA-replicating associated drugs such as Remdesivir which was authorized for emergency use to treat COVID‑19 in numerous countries. In collaboration with the Simmerling Lab at Stony Brook University.
We utilize the robust DFT to compute electronic structures of a molecule and its associated thermodynamics and kinetics properties such as changes in enthalpies and Gibb free energies, activation energies, and transition state structures. We also harness machine learning models, such as Linear Regression, Artificial Neural Network, Random Forest, and Gradient Boosting, to predict properties based on historical data rather than physical laws. Examples of projects include:
Explore and predict properties of the adducts of Lewis bases (LB) and Lewis acids (LA). This is a classic yet exciting non-conventional chemical bond.
Develop prediction tools for targeted covalent inhibitors. This is an emerging class of drugs that covalently binds to a protein, as compared to mostly Van der Waals interaction in most small molecule drugs. The computational methods involve Density Functional Theory (DFT) calculations, machine learning, and Docking. In collaboration with the Forli Lab at The Scripps Research Institute.
DFT calculations to predict thermodynamic and kinetic properties of organic reactions
Full list of publications can be found here.
Synergy of Machine Learning and Density Functional Theory Calculations for Predicting Experimental Lewis Base Affinity and Lewis Polybase Binding Atoms, Journal of Computational Chemistry, 2024. Full-text.
Quantum Chemistry–Machine Learning Approach for Predicting Properties of Lewis Acid–Lewis Base Adducts, ACS Omega, 2023. Full-text.
Experimental and computational investigation of a green Knoevenagel condensation catalyzed by zeolitic imidazolate framework-8, Environmental Research, 2022. Full-text.
Tuning Optical Properties of Conjugated Molecules by Lewis Acids: Insights from Electronic Structure Modeling, The Journal of Physical Chemistry Letters, 2019. Full-text.
Electrical Double-Slope Nonideality in Organic Field-Effect Transistors, Advanced Functional Materials, 2018. Full-text.
Improving Electrical Stability and Ideality in Organic Field-Effect Transistors by the Addition of Fullerenes: Understanding the Working Mechanism, Advanced Functional Materials, 2017. Full-text.
Biofilm as a redox conductor: a systematic study of the moisture and temperature dependence of its electrical properties, Physical Chemistry Chemical Physics, 2016. Full-text.
Electrical Instability Induced by Electron Trapping in Low-Bandgap Donor–Acceptor Polymer Field-Effect Transistors, Advanced Materials, 2015. Full-text.
Structural and optoelectronic properties of hybrid bulk-heterojunction materials based on conjugated small molecules and mesostructured TiO2, Applied Physics Letters, 2014. Full-text.
Direct Observation of Doping Sites in Temperature-Controlled, p-Doped P3HT Thin Films by Conducting Atomic Force Microscopy, Advanced Materials, 2014. Full-text.
Understanding TiO2 Size-Dependent Electron Transport Properties of a Graphene-TiO2 Photoanode in Dye-Sensitized Solar Cells Using Conducting Atomic Force Microscopy, Advanced Materials, 2013. Full-text.
Reflection of the structural distinctions of source—different humic substances on organic fouling behaviors of SWRO membranes, Desalination, 2013. Full-text.
Behaviors of commercialized seawater reverse osmosis membranes under harsh organic fouling conditions, Desalination and Water Treatment, 2010. Full-text.
Prediction of boron transport through seawater reverse osmosis membranes using solution–diffusion model, Desalination, 2009. Full-text.