Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics.

Area of Science:

• Computational chemistry
• Chemoinformatics
• Toxicoinformatics

Background:

• Molecular descriptors are crucial for predicting molecular properties and facilitating drug discovery.
• Existing methods for descriptor calculation vary in complexity, computational cost, and the type of structural information they capture (2D vs. 3D).

Purpose of the Study:

• To introduce Mold (2), a software tool for the rapid calculation of a wide range of 2D molecular descriptors.
• To evaluate the information content and performance of Mold (2) descriptors compared to established software.

Main Methods:

• Shannon entropy analysis was used to compare the information content of descriptors from Mold (2), Cerius (2), Dragon, and Molconn-Z.
• Classification models were built using descriptors from different software packages to assess predictive performance.

Main Results:

• Mold (2) descriptors were found to convey a similar amount of information compared to descriptors from commercial software.
• Models built using Mold (2) descriptors showed slightly improved performance in classification tasks.
• Mold (2) offers a low computing cost, making it suitable for both small and large datasets.

Conclusions:

• Mold (2) is an efficient and reliable tool for generating 2D molecular descriptors.
• The software's performance and low computational requirements make it valuable for quantitative structure-activity relationship (QSAR) studies and virtual screening.
• Mold (2) is freely available, promoting its use in research.