Identification of bioactive compounds with popular single-atom modifications: Comprehensive analysis and implications...

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Structure-activity relationship (SAR) analysis is crucial for drug development, relying on molecular similarity assessments.
• Single-atom modifications (SAMs) are a key strategy in hit-to-lead and lead optimization.
• Previous work introduced four SAM types; this study expands the scope.

Purpose of the Study:

• To systematically analyze 10 common single-atom modifications (SAMs) and their impact on bioactivity.
• To identify and characterize SAM-induced activity cliffs (ACs) using a large dataset.
• To provide insights into SAMs for structure-based drug design and optimization.

Main Methods:

• Leveraged high-confidence bioactivity data from ChEMBL (version 34).
• Assembled a dataset of 374,979 SAM pairs covering 10 common SAM types.
• Identified over 7400 SAM-induced activity cliffs (ACs) and performed structural analysis.

Main Results:

• Identified over 7400 activity cliffs (ACs) resulting from 10 common single-atom modifications.
• Analyzed the frequency of different SAM types in medicinal chemistry.
• Structural analysis of ACs provides insights into how SAMs modulate compound activity.

Conclusions:

• This study significantly expands the knowledgebase of activity cliffs associated with single-atom changes.
• Findings offer practical guidance for structure-based optimization of molecular properties in drug development.
• All identified ACs and their structural information are provided open access.