Research Keywords:
- Drug Discovery
- Molecular Modeling
- Multi-targeting
- Ligand-based models
- Structure-based models
Research Highlights:
The Goldblum lab develops and employs computational (in-silico) methods for discovering novel drugs for various diseases. Our own algorithm called “Iterative Stochastic Elimination” (ISE) provides sets of solutions to highly complex combinatorial problems and is a generic “optimizer” in such problems. In drug discovery, we construct models of ligand-based physicochemical properties that distinguish between active molecules and random ones on a specific target. A model is a set of physicochemical properties through which we screen and score any molecule to pick the top-scored ones for superfast synthesis and subsequent in-vitro/in-vivo studies. We also use structure-based docking to confirm the binding of our top candidates to the relevant protein structures in a disease. The modeling, screening, and getting the new molecules are done very fast, usually in a few months. We focus mostly on multifactorial diseases and search for multitargeted single molecules.
Our range of molecular discoveries of novel drugs includes Viral and Bacterial diseases, Malaria, Cancer microenvironment, Diabetes, Metabolic diseases, Neurological disorders including depression and anxiety, Alzheimer’s Disease, Duchenne muscular Dystrophy, Aging, Hypercholesterolemia, Osteoarthritis, and more. In addition, we predict novel High Energy Materials for potential military and civil uses.