Chapter title |
Chemical Library Design
|
---|---|
Chapter number | 3 |
Book title |
Chemical Library Design
|
Published in |
Methods in molecular biology, January 2011
|
DOI | 10.1007/978-1-60761-931-4_3 |
Pubmed ID | |
Book ISBNs |
978-1-60761-930-7, 978-1-60761-931-4
|
Authors |
Nicolaou, Christos A, Kannas, Christos C, Nicolaou, Christos A., Kannas, Christos C., Christos A. Nicolaou, Christos C. Kannas |
Abstract |
Advancements in combinatorial chemistry and high-throughput screening technology have enabled the synthesis and screening of large molecular libraries for the purposes of drug discovery. Contrary to initial expectations, the increase in screening library size, typically combined with an emphasis on compound structural diversity, did not result in a comparable increase in the number of promising hits found. In an effort to improve the likelihood of discovering hits with greater optimization potential, more recent approaches attempt to incorporate additional knowledge to the library design process to effectively guide the search. Multi-objective optimization methods capable of taking into account several chemical and biological criteria have been used to design collections of compounds satisfying simultaneously multiple pharmaceutically relevant objectives. In this chapter, we present our efforts to implement a multi-objective optimization method, MEGALib, custom-designed to the library design problem. The method exploits existing knowledge, e.g. from previous biological screening experiments, to identify and profile molecular fragments used subsequently to design compounds compromising the various objectives. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Cyprus | 1 | 8% |
Unknown | 11 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 2 | 17% |
Researcher | 2 | 17% |
Student > Bachelor | 1 | 8% |
Lecturer | 1 | 8% |
Student > Doctoral Student | 1 | 8% |
Other | 1 | 8% |
Unknown | 4 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Chemistry | 4 | 33% |
Computer Science | 3 | 25% |
Agricultural and Biological Sciences | 1 | 8% |
Unknown | 4 | 33% |