Advances in Structure-Based Drug Design: Challenges and Opportunities

Abstract

Drugs for a range of illnesses have been developed with the help of SBDD. Structure-based medication design uses three-dimensional geometric information about macromolecules, including proteins or nucleic acids, to identify suitable ligands. Examine the many studies conducted by researchers on the prospects and difficulties in structure-based medication discovery in this page. This study comes to the conclusion that because of the flexibility of proteins, solvent effects, and important water molecules in target proteins, the existing scoring methods have difficulty properly estimating binding free energy. Improving predictions of protein-ligand interactions still requires addressing these issues. SBDD has a lot of potential to speed up drug development with cutting-edge computational methods, despite these obstacles. Drug-target interaction prediction, ligand binding site identification, and de novo ligand generation provide practical ways to target unknown macromolecular structures. By improving structural refinement, drug association mapping, and protein structure prediction, the use of deep learning into SBDD has created new opportunities. These developments increase the possibility of finding potent medicinal molecules by enabling a more effective search of chemical space.