Digitization of molecular complexity with machine learning.

ABSTRACT: Digitization of molecular complexity is of key importance in chemistry and life sciences to develop structure-activity relationships in chemical behavior and biological activity. The complexity of a given molecule compared to others is largely based on intuitive perception and lacks a standardized numerical measure. Quantifying molecular complexity remains a fundamental challenge, with key implications currently remaining controversial. In this study, we introduce a novel machine learning-based framework employing a Learning to Rank (LTR) approach to quantify molecular complexity on the basis of labeled data. As a result, we developed a ranking model utilizing the dataset that comprizes approximately 300 000 data points across diverse chemical structures, leveraging human expertise to capt

SUBMITTER: Tyrin AS 

PROVIDER: S-EPMC11922160 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature