Chapter title |
Fragment-Based De Novo Design of Cyclin-Dependent Kinase 2 Inhibitors
|
---|---|
Chapter number | 5 |
Book title |
Cyclin-Dependent Kinase (CDK) Inhibitors
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-2926-9_5 |
Pubmed ID | |
Book ISBNs |
978-1-4939-2925-2, 978-1-4939-2926-9
|
Authors |
Sunil Kumar Tripathi, Poonam Singh, Sanjeev Kumar Singh |
Abstract |
Cyclin-dependent kinases (CDKs) are core components of the cell cycle machinery that govern the transition between phases during cell cycle progression. Abnormalities in CDKs activity and regulation are common features of cancer, making CDK family members attractive targets for the development of anticancer drugs. One of the main bottlenecks hampering the development of drugs for kinase is the difficulty to attain selectivity. A huge variety of small molecules have been reported as CDK inhibitors, as potential anticancer agents, but none of these has been approved for commercial use. Computer-based molecular design supports drug discovery by suggesting novel new chemotypes and compound modifications for lead candidate optimization. One of the methods known as de novo ligand design technique has emerged as a complementary approach to high-throughput screening. Several automated de novo software programs have been written, which automatically design novel structures to perfectly fit in known binding site. The de novo design supports drug discovery assignments by generating novel pharmaceutically active agents with desired properties in a cost as well as time efficient approach. This chapter describes procedure and an overview of computer-based molecular de novo design methods on a conceptual level with successful examples of CDKs inhibitors. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 12 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 3 | 25% |
Student > Master | 2 | 17% |
Student > Bachelor | 1 | 8% |
Other | 1 | 8% |
Student > Doctoral Student | 1 | 8% |
Other | 1 | 8% |
Unknown | 3 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 3 | 25% |
Biochemistry, Genetics and Molecular Biology | 2 | 17% |
Social Sciences | 1 | 8% |
Computer Science | 1 | 8% |
Unknown | 5 | 42% |