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.
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Alagappan, M., Jiang, D., Denko, N., Koong, A.C. (2016). A Multimodal Data Analysis Approach for Targeted Drug Discovery Involving Topological Data Analysis (TDA). In: Koumenis, C., Coussens, L., Giaccia, A., Hammond, E. (eds) Tumor Microenvironment. Advances in Experimental Medicine and Biology, vol 899. Springer, Cham. https://doi.org/10.1007/978-3-319-26666-4_15
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DOI: https://doi.org/10.1007/978-3-319-26666-4_15
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