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Researcher at Research Department
Ask questions about Tahar Lakhlifi's research, publications, and ongoing work
Identified new 1H-pyrrole-2,5-dione derivatives as efficient organic inhibitors of carbon steel corrosion in hydrochloric acid medium through electrochemical, XPS, and DFT studies.
Utilized computational investigations to identify Moroccan medicinal plants as potential inhibitors against the SARS-CoV-2 main protease.
Employed in silico methods to detect potential inhibitors from vitamins and their derivatives against the SARS-CoV-2 main protease using molecular docking, molecular dynamic simulation, and ADMET profiling.
Discovered potent SARS-CoV-2 inhibitors from approved antiviral drugs via docking and virtual screening.
Conducted a QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods.
Identified a novel dual-target scaffold for 3CLpro and RdRp proteins of SARS-CoV-2 using 3D-similarity search, molecular docking, molecular dynamics, and ADMET evaluation.
Designed novel PIN1 inhibitors using a combination of 3D-QSAR, molecular docking, molecular dynamic simulation, and ADMET studies.
Designed new α-glucosidase inhibitors through a combination of 3D-QSAR, ADMET screening, molecular docking, molecular dynamics simulations, and quantum studies.
Tahar Lakhlifi is a leading researcher in computational drug discovery, specializing in the application of QSAR, molecular docking, and molecular dynamics simulations to identify potential inhibitors for various diseases, including viral infections and cancer. His work focuses on leveraging computational methods to accelerate the drug development process and design novel therapeutic agents.
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