Exploiting Vector Pattern Diversity of Molecular Scaffolds for Cheminformatics Tasks in Drug Discovery

Area of Science:

• Medicinal Chemistry
• Drug Discovery
• Computational Chemistry

Background:

• Chemical diversity is crucial but challenging to quantify objectively in drug discovery.
• Current methods for assessing chemical diversity can be subjective and time-consuming.

Purpose of the Study:

• To present a quantitative and objective method for describing chemical diversity using Exploited Vector Patterns (EVPs).
• To demonstrate the utility of EVPs in various stages of the drug design process, including fragment library design, hit expansion, and lead optimization.
• To provide a tool for analyzing Structure-Activity Relationship (SAR) data and uncovering vector correlations.

Main Methods:

• Developed a concise and fast approach to enumerate and analyze Exploited Vector Patterns (EVPs) of molecular compound series.
• Applied EVPs to assess compound progressibility in fragment library design.
• Utilized EVP-based, customizable diversity sampling for prioritizing compounds in hit expansion.
• Employed EVPs for analyzing SAR data without predetermining molecular scaffolds.

Main Results:

• EVPs provide a quantitative and objective measure of chemical diversity.
• The EVP approach successfully assessed compound progressibility in fragment library design.
• EVP-based sampling prioritized compounds for hit expansion, reducing time and human bias.
• EVPs effectively analyzed SAR data, revealing vector correlations.

Conclusions:

• Exploited Vector Patterns offer a valuable, objective tool for managing and analyzing chemical diversity in drug discovery.
• The EVP method streamlines compound selection and prioritization, enhancing efficiency and mitigating bias.
• The provided Jupyter notebooks facilitate the adoption and adaptation of EVP analysis for diverse medicinal chemistry tasks.