Digitization of molecular complexity with machine learning.

Area of Science:

• Computational chemistry
• Cheminformatics
• Machine learning in drug discovery

Background:

• Quantifying molecular complexity is crucial for understanding structure-activity relationships but lacks standardization.
• Current measures rely on intuitive perception, hindering objective analysis and comparison.
• This gap limits advancements in chemical behavior and biological activity studies.

Purpose of the Study:

• To develop a novel, standardized numerical measure for molecular complexity.
• To leverage machine learning to digitize human expert perception of molecular complexity.
• To create a robust framework for analyzing chemical and biological data.

Main Methods:

• Implementation of a machine learning-based framework using a Learning to Rank (LTR) approach.
• Development of a ranking model trained on a large dataset of approximately 300,000 diverse chemical structures.
• Incorporation of human expertise to capture intuitive decision-making rules in molecular complexity assessment.

Main Results:

• A novel machine learning model capable of quantifying molecular complexity numerically.
• A large, labeled dataset of diverse chemical structures was generated for future research.
• Demonstrated applications in analyzing organic chemistry trends, FDA-approved drugs, and synthetic strategies.

Conclusions:

• Molecular complexity can be effectively quantified as a numerical characteristic using machine learning.
• The developed framework provides a standardized method for assessing molecular complexity.
• This advancement facilitates deeper insights into structure-activity relationships and guides drug discovery processes.