Chapter title |
Tumor Microenvironment
|
---|---|
Chapter number | 15 |
Book title |
Tumor Microenvironment
|
Published in |
Advances in experimental medicine and biology, January 2016
|
DOI | 10.1007/978-3-319-26666-4_15 |
Pubmed ID | |
Book ISBNs |
978-3-31-926664-0, 978-3-31-926666-4
|
Authors |
Alagappan, Muthuraman, Jiang, Dadi, Denko, Nicholas, Koong, Albert C, Muthuraman Alagappan, Dadi Jiang, Nicholas Denko, Albert C. Koong, Alagappan M, Jiang D, Denko N, Koong AC, Koong, Albert C. |
Abstract |
In silico drug discovery refers to a combination of computational techniques that augment our ability to discover drug compounds from compound libraries. Many such techniques exist, including virtual high-throughput screening (vHTS), high-throughput screening (HTS), and mechanisms for data storage and querying. However, presently these tools are often used independent of one another. In this chapter, we describe a new multimodal in silico technique for the hit identification and lead generation phases of traditional drug discovery. Our technique leverages the benefits of three independent methods-virtual high-throughput screening, high-throughput screening, and structural fingerprint analysis-by using a fourth technique called topological data analysis (TDA). We describe how a compound library can be independently tested with vHTS, HTS, and fingerprint analysis, and how the results can be transformed into a topological data analysis network to identify compounds from a diverse group of structural families. This process of using TDA or similar clustering methods to identify drug leads is advantageous because it provides a mechanism for choosing structurally diverse compounds while maintaining the unique advantages of already established techniques such as vHTS and HTS. |
X Demographics
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United States | 2 | 40% |
Unknown | 3 | 60% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 60% |
Scientists | 2 | 40% |
Mendeley readers
Geographical breakdown
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Singapore | 1 | 6% |
Unknown | 15 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 4 | 25% |
Student > Bachelor | 3 | 19% |
Student > Master | 2 | 13% |
Researcher | 2 | 13% |
Professor | 2 | 13% |
Other | 3 | 19% |
Readers by discipline | Count | As % |
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Computer Science | 4 | 25% |
Mathematics | 2 | 13% |
Engineering | 2 | 13% |
Chemistry | 2 | 13% |
Agricultural and Biological Sciences | 1 | 6% |
Other | 4 | 25% |
Unknown | 1 | 6% |