Chapter title |
De novo drug design.
|
---|---|
Chapter number | 12 |
Book title |
Chemoinformatics and Computational Chemical Biology
|
Published in |
Methods in molecular biology, September 2010
|
DOI | 10.1007/978-1-60761-839-3_12 |
Pubmed ID | |
Book ISBNs |
978-1-60761-838-6, 978-1-60761-839-3
|
Authors |
Hartenfeller M, Schneider G, Markus Hartenfeller, Gisbert Schneider, Hartenfeller, Markus, Schneider, Gisbert |
Abstract |
Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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India | 3 | 2% |
United Kingdom | 2 | 1% |
Cyprus | 1 | <1% |
France | 1 | <1% |
Sweden | 1 | <1% |
Portugal | 1 | <1% |
Sri Lanka | 1 | <1% |
United States | 1 | <1% |
Unknown | 129 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 42 | 30% |
Researcher | 25 | 18% |
Student > Master | 17 | 12% |
Student > Bachelor | 15 | 11% |
Student > Doctoral Student | 9 | 6% |
Other | 6 | 4% |
Unknown | 26 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 41 | 29% |
Agricultural and Biological Sciences | 18 | 13% |
Biochemistry, Genetics and Molecular Biology | 15 | 11% |
Computer Science | 10 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 9 | 6% |
Other | 16 | 11% |
Unknown | 31 | 22% |