The discovery of new therapeutics is a race against time and disease complexity. In the PROPA Lab, we leverage the immense power of computational biology, bioinformatics, and AI-driven modeling to dramatically accelerate this process. We operate at the digital frontier of medicine, screening millions of molecules to identify promising drug candidates for a range of diseases, including infectious, neurological, and metabolic disorders.
Our computational drug discovery research focuses on structure-based modeling, molecular docking, and molecular dynamics simulations to investigate protein–ligand interactions and identify therapeutically relevant targets. We apply these approaches to viral proteins, enzymes, and transport systems, including studies on how the SARS-CoV-2 Omicron variant interacts with blood–brain barrier proteins, revealing altered binding profiles that may contribute to neurological complications. Through rigorous docking and simulation analyses, we evaluate binding stability, interaction networks, and conformational dynamics to generate mechanistic insights into disease-relevant molecular processes.
In parallel, we employ virtual screening, pharmacophore modeling, and post-simulation free-energy calculations to identify and optimize small-molecule inhibitors. Our work includes the discovery of potential antiviral inhibitors targeting Dengue virus NS2B/NS3 protease, computational evaluation of anti-inflammatory and analgesic small molecules, and mechanistic studies of metal-dependent viral proteins to guide rational drug design. These computational efforts are tightly integrated with experimental validation and medicinal chemistry, supporting the development of mechanism-driven therapeutic candidates across infectious, inflammatory, and metabolic diseases.
The ultimate aim of our computational efforts is to bridge the gap between a digital model and a real-world therapeutic. We focus on identifying and optimizing high-potential inhibitors that target critical viral and human proteins. By doing so, we provide a validated, shortlist of candidate molecules for experimental testing, streamlining the path from a computational prediction to a tangible therapeutic candidate.