GEEM LAB
01
Our lab employs Density Functional Theory (DFT) to explore electronic structures and properties of materials at the atomic level.
02
Our laboratory utilizes Monte Carlo methods to model complex systems with multiple variables and uncertainties. We apply these powerful statistical techniques to simulate molecular behavior, predictmaterial properties, and optimize process parameters.
03
Our lab employs molecular docking techniques to investigate and predict interactions between molecules, with a focus on sustainable chemistry applications. We use advanced docking algorithms to study enzyme-substrate binding, design green catalysts, and explore novel materials for environmental remediation.
04
Our lab pioneers the integration of artificial intelligence with molecular dynamics simulations to enhance predictive power and efficiency. We leverage machine learning algorithms to accelerate simulations, identify complex patterns in molecular behavior, and guide the design of novel materials.
05
Our lab specializes in the in silico prediction of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties of chemical compounds. Using advanced computational models and machine learning algorithms, we assess the pharmacokinetic and toxicological profiles of molecules.
06
Our laboratory employs cutting-edge computational methods to assess the potential toxicity of chemicals and materials without relying on animal testing. We utilize a combination of quantitative structure-activity relationship (QSAR) models, machine learning algorithms, and molecular modeling techniques to predict toxicological endpoints.
Our Clients
