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

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).

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

“…qsarworld.com/), http://www.opentox.org, and http://www.openqsar. com are making an effort to collect these datasets as an open repository for chemoinformatics data as well as toolkits for models and descriptors, e.g., CDK and Mold2 (Hong et al, 2008).…”

Quantitative Structure‐Fluorescence Property Relationship Analysis of a Large BODIPY Library

“…[46] MOE implements 184 2d descriptors which can be classified as physical properties, subdivided surface areas, atom counts and bond counts, Kier&Hall connectivity & kappa shape indices, adjacency and distance matrix-based, pharmacophore features, and partial charge descriptors. Fur- thermore, 119 MOE 3d descriptors can be classified as potential energy, MOPAC, surface area, volume and shape, and conformation dependent charge descriptors.…”

“…Mold2 implements 777 2d descriptors which can be organized in 14 groups (counts for atoms, counts for bonds, counts for functional groups, chemical & physical properties, structural features, 2d autocorrelation Balaban index, connectivity index, detour index, distance (topological) index, eigenvalue-based descriptors, information content, Kier index, molecular walk counts, Schultz index, topological charge index, Wiener index, and Zagreb index). [46] Three sets of descriptor data were exported as comma-separated values (CSV) files: MOE descriptors, Mold2 descriptors, and Mold2 descriptors with added HOMO-LUMO energy gap of the MOPAC calculations (dubbed: Gap + Mold2 descriptor set).…”

“…Three sets of molecular descriptors were calculated: (i) the MOE set of 304 descriptors (Molecular Operating Environment version 2008.10, Chemical Computing Group, Montreal, Canada), [41] (ii) the Mold2 set of 777 descriptors, [46] and (iii) the Gap + Mold2 set of 778 descriptors (HOMO-LUMO energy gap as calculated by MOE/MOPAC added to the Mold2 descriptors). Subsequently, a multistep feature selection protocol was applied to reduce the number of independent variables.…”

“…Figure 6 shows the frequencies of molecular descriptors that were selected at least 50 times (of 100) in the last step of the feature selection protocol (stepwise MLR). The descriptors are defined in references [41,46,67] . Gap: The HOMO-LUMO energy gap descriptor was selected in 100 % of all runs (for both l abs and l em ) and has a high negative correlation coefficient with l abs (R = À0.70) and l em (R = À0.62).…”