Mold², Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics

TLDR

Chemoinformatics and toxicoinformatics increasingly rely on numerical descriptors derived from molecular structures to predict ADME/toxicity, analyze diversity, design libraries, build QSAR/QSPR models, and perform virtual screening. The study introduces Mold (2), a software tool designed to rapidly compute a broad array of two‑dimensional molecular descriptors. Mold (2) computes descriptors directly from 2D chemical structures, avoiding 3D modeling and enabling fast, large‑scale descriptor generation. Comparative analysis shows that Mold (2) descriptors contain comparable information to those from commercial packages, yield slightly better predictive models, and offer low computational cost, making them suitable for both small QSAR studies and large virtual‑screening databases, with high reproducibility and free public availability.

Abstract

Research applications in chemoinformatics and toxicoinformatics increasingly use representations of molecules in the form of numerical descriptors that capture the structural characteristics and properties of molecules. These representations are useful for ADME/toxicity prediction, diversity analysis, library design, QSAR/QSPR, virtual screening, and other purposes. Molecular descriptors have ranged from relatively simple forms calculated from simple two-dimensional (2D) chemical structures to more complex forms representing three-dimensional (3D) chemical structures or complex molecular fingerprints consisting of numerous bit positions to represent specific chemical information. The Mold (2) software was developed to enable the rapid calculation of a large and diverse set of descriptors encoding two-dimensional chemical structure information. Comparative analysis of Mold (2) descriptors with those calculated by Cerius (2), Dragon, and Molconn-Z on several data sets using Shannon entropy analysis demonstrated that Mold (2) descriptors convey a similar amount of information. In addition, using the same classification method, slightly better models were generated using Mold (2) descriptors compared to those generated using descriptors from the compared commercial software packages. The low computing cost for Mold (2) makes it suitable not only for small data sets, such as in QSAR, but also for large databases in virtual screening. High reproducibility and reliability are expected because Mold (2) does not require 3D structures. Mold (2) is freely available to the public ( http://www.fda.gov/nctr/science/centers/toxicoinformatics/index.htm).