Machine learning empowered drug discovery
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 successf...
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| Main Authors: | , |
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| Format: | Book |
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German Journal of Pharmaceuticals and Biomaterials,
2022-06-01T00:00:00Z.
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| Summary: | 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......... |
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| Item Description: | 2750-624X 2750-6258 10.5530/gjpb.2022.2.6 |