Machine learning empowered drug discovery - Thirumoorthy Durai Ananda Kumar; Naraparaju Swathi (German Journal of Pharmaceuticals and Biomaterials, 2022)
Traditional drug discovery strategies include lead molecule identification, lead optimization, preclinical studies and clinical trials. The pharmaceutical and biotechnology research and development (R&D) department spends more than 10 years and $1 billion to bring the molecule to market successfully. About 90% of drug candidates fail in the drug development due to safety and efficacy issues. The lack of technologies is the main limitation for identifying potential candidates from the available chemical space (>1060 molecules). De Novo design methods explore chemical space through pharmacophore (ligand-based), and docking (structure-based) approaches. Structure-based drug discovery approaches use the insights gained from biological data of target structures. Schrödinger, AutoDock and Biovia (Accelrys) pioneered the development of structure-based tools to improve drug discovery. Libraries of molecules can be screened for their target suitability, known as virtual screening. The structure-based drug discovery approach uses the three-dimensional (3D) details of the target structure and explains the intermolecular interactions (biophysical simulations). Ligand-based drug discovery approaches are based. Read more.........