Chapter title |
Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery
|
---|---|
Chapter number | 19 |
Book title |
Computational Methods for GPCR Drug Discovery
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7465-8_19 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7464-1, 978-1-4939-7465-8
|
Authors |
Alexander Heifetz, Michelle Southey, Inaki Morao, Andrea Townsend-Nicholson, Mike J. Bodkin, Heifetz, A, Southey, M, Morao, I, Townsend-Nicholson, A, Bodkin, MJ |
Abstract |
GPCR modeling approaches are widely used in the hit-to-lead (H2L) and lead optimization (LO) stages of drug discovery. The aims of these modeling approaches are to predict the 3D structures of the receptor-ligand complexes, to explore the key interactions between the receptor and the ligand and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this book chapter, we present a brief survey of key computational approaches integrated with hierarchical GPCR modeling protocol (HGMP) used in hit-to-lead (H2L) and in lead optimization (LO) stages of structure-based drug discovery (SBDD). We outline the differences in modeling strategies used in H2L and LO of SBDD and illustrate how these tools have been applied in three drug discovery projects. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 59 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 14 | 24% |
Student > Bachelor | 7 | 12% |
Student > Master | 7 | 12% |
Student > Doctoral Student | 4 | 7% |
Professor | 3 | 5% |
Other | 8 | 14% |
Unknown | 16 | 27% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 12 | 20% |
Biochemistry, Genetics and Molecular Biology | 11 | 19% |
Agricultural and Biological Sciences | 4 | 7% |
Computer Science | 3 | 5% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
Other | 6 | 10% |
Unknown | 21 | 36% |