期刊
PHARMACOLOGICAL REVIEWS
卷 66, 期 1, 页码 334-395出版社
AMER SOC PHARMACOLOGY EXPERIMENTAL THERAPEUTICS
DOI: 10.1124/pr.112.007336
关键词
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资金
- National Science Foundation through the Office for Cyber Infrastructure Transformative Computational Sciences Fellowship [OCI-1122919]
- National Institutes of Health National Institute of Mental Health [R01 MH090192]
- National Institutes of Health National Institute of General Medical Sciences [R01 GM099842]
- National Institutes of Health National Institute of Diabetes and Digestive and Kidney Diseases [R01 DK097376]
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [R01DK097376] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM099842] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF MENTAL HEALTH [R01MH090192] Funding Source: NIH RePORTER
Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.
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