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ChemSuite: A package for chemoinformatics calculations and machine learning
Author(s) -
Tangadpalliwar Sujit R.,
Vishwakarma Sachin,
Nimbalkar Rakesh,
Garg Prabha
Publication year - 2019
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.13479
Subject(s) - cheminformatics , computer science , machine learning , force field (fiction) , artificial intelligence , interface (matter) , graphical user interface , field (mathematics) , software , minimax , r package , in silico , data mining , computational science , chemistry , mathematics , mathematical optimization , computational chemistry , biochemistry , bubble , maximum bubble pressure method , parallel computing , pure mathematics , gene , programming language
Prediction of biological and toxicological properties of small molecules using in silico approaches has become a wide practice in pharmaceutical research to lessen the cost and enhance productivity. The development of a tool “ChemSuite,” a stand‐alone application for chemoinformatics calculations and machine‐learning model development, is reported. Availability of multi‐functional features makes it widely acceptable in various fields. Force field such as UFF is incorporated in tool for optimization of molecules. Packages like RDKit, PyDPI and PaDEL help to calculate 1D, 2D and 3D descriptors and more than 10 types of fingerprints. MinMax Scaler and Z ‐Score algorithms are available to normalize descriptor values. Varied descriptor selection and machine‐learning algorithms are available for model development. It allows the user to add their own algorithm or extend the software for various scientific purposes. It is free, open source and has user‐friendly graphical interface, and it can work on all major platforms.