Mordred: a molecular descriptor calculator

By Hirotomo Moriwaki, Yu-Shi Tian, Norihito Kawashita, Tatsuya Takagi. 2018

1,309 citations recorded by the source corpus.

Molecular descriptors are widely employed to present molecular characteristics in cheminformatics. Various molecular-descriptor-calculation software programs have been developed. However, users of those programs must contend with several issues, including software bugs, insufficient update frequencies, and software licensing constraints. To address these issues, we propose Mordred, a developed descriptor-calculation software application that can calculate more than 1800 two- and three-dimensional descriptors. It is freely available via GitHub. Mordred can be easily installed and used in the command line interface, as a web application, or as a high-flexibility Python package on all major platforms (Windows, Linux, and macOS). Performance benchmark results show that Mordred is at least twice as fast as the well-known PaDEL-Descriptor and it can calculate descriptors for large molecules, which cannot be accomplished by other software. Owing to its good performance, convenience, number of descriptors, and a lax licensing constraint, Mordred is a promising choice of molecular descriptor calculation software that can be utilized for cheminformatics studies, such as those on quantitative structure–property relationships.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Message passing neural networks for molecular property prediction (2019, background): “Improvements in property prediction accuracy using these models has primary come through the development of better molecular descriptors [44, 8, 15, 33, 31, 32].”
A deep learning approach to antibiotic discovery (2020, background): “…were represented by their fingerprint vectors, which reflected the presence or absence of functional groups in the molecule, or by descriptors that include computable molecular properties and require expert knowledge to construct (Mauri et al., 2006; Moriwaki et al., 2018; Rogers and Hahn, 2010).”
A Deep Learning Approach to Antibiotic Discovery. (2020, neutral): “An important development relates to how molecules are represented; traditionally, molecules were represented by their fingerprint vectors, which reflected the presence or absence of functional groups in the molecule, or by descriptors that include computable molecular properties and require expert k”