A Multimodal Data Analysis Approach for Targeted Drug Discovery Involving Topological Data Analysis (TDA)
Open accessAbstract
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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- Authors
- Alagappan, M., Jiang, D., Denko, N., & Koong, A. C.
- Affiliations
- Stanford Medicine (US)
- The Ohio State University Wexner Medical Center (US)
- Corresponding author
- Koong, Albert C.
- Venue
- Advances in Experimental Medicine and Biology
- Publisher
- Springer International Publishing
- Published
- 2016
- Volume
- 899
- Pages
- 253-268
- ISSN
- 0065-2598 (ISSN-L)
- 2214-8019
- PMC
- PMC5519345
- OpenAlex
- W2472847454
- Cited by
- 10
- References
- 24
- Citations by year
- 2025: 1 · 2024: 2 · 2022: 3 · 2021: 1 · 2018: 1 · 2017: 2
- FWCI
- 2.14
- Type
- book chapter
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Advances in experimental medicine and biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (P01CA067166, R01CA163581)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- computational drug discovery, topological data analysis, high-throughput virtual screening, high-throughput screening, structural fingerprints, hit identification, lead generation, chemical libraries, structural diversity, multimodal data integration
- MeSH terms
- Statistics as Topic, User-Computer Interface, Small Molecule Libraries, Drug Discovery, High-Throughput Screening Assays
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