2020 journal article

Cheminformatics Analysis and Modeling with MacrolactoneDB

SCIENTIFIC REPORTS, 10(1).

By: P. Zin n , G. Williams n  & S. Ekins  n

MeSH headings : Biological Products / chemistry; Cheminformatics; Databases, Chemical; Machine Learning; Macrolides / chemistry; Models, Chemical; Quantitative Structure-Activity Relationship; Software
TL;DR: This study develops MacrolactoneDB, which integrates nearly 14,000 existing macrolactones and their bioactivity information from different public databases, and new molecular descriptors to better characterize macrolide structures, and shows that merging descriptors yields the best predictive power with Random Forest models. (via Semantic Scholar)
UN Sustainable Development Goal Categories
15. Life on Land (OpenAlex)
Source: Web Of Science
Added: September 7, 2020

AbstractMacrolactones, macrocyclic lactones with at least twelve atoms within the core ring, include diverse natural products such as macrolides with potent bioactivities (e.g. antibiotics) and useful drug-like characteristics. We have developed MacrolactoneDB, which integrates nearly 14,000 existing macrolactones and their bioactivity information from different public databases, and new molecular descriptors to better characterize macrolide structures. The chemical distribution of MacrolactoneDB was analyzed in terms of important molecular properties and we have utilized three targets of interest (Plasmodium falciparum, Hepatitis C virus and T-cells) to demonstrate the value of compiling this data. Regression machine learning models were generated to predict biological endpoints using seven molecular descriptor sets and eight machine learning algorithms. Our results show that merging descriptors yields the best predictive power with Random Forest models, often boosted by consensus or hybrid modeling approaches. Our study provides cheminformatics insights into this privileged, underexplored structural class of compounds with high therapeutic potential.