A Multimodal Data Analysis Approach for Targeted Drug Discovery Involving Topological Data Analysis (TDA)

Area of Science:

• Computational chemistry and pharmacology
• Drug discovery and development

Background:

• In silico drug discovery utilizes computational methods to identify drug compounds.
• Current techniques like virtual high-throughput screening (vHTS) and high-throughput screening (HTS) are often used in isolation.
• There is a need for integrated approaches to enhance hit identification and lead generation.

Purpose of the Study:

• To present a novel multimodal in silico technique for drug discovery.
• To integrate independent computational methods for more effective lead generation.
• To leverage topological data analysis (TDA) for enhanced compound selection.

Main Methods:

• Integration of virtual high-throughput screening (vHTS), high-throughput screening (HTS), and structural fingerprint analysis.
• Application of topological data analysis (TDA) to create a network from screening and analysis results.
• Identification of structurally diverse drug leads from compound libraries.

Main Results:

• The multimodal approach successfully transforms independent screening data into a TDA network.
• The technique facilitates the identification of diverse compound families.
• It maintains the advantages of established methods like vHTS and HTS.

Conclusions:

• The proposed multimodal in silico technique offers an advantageous approach for hit identification and lead generation.
• Integrating TDA with vHTS, HTS, and fingerprint analysis enables the selection of structurally diverse drug leads.
• This method enhances traditional drug discovery processes by combining multiple computational strategies.