Provides tools for cheminformatics, computational chemistry, and predictive modeling. RDKit is a collection of cheminformatics and machine-learning software written in C++ and Python. RDKit provides tools for 2D and 3D molecular operations, descriptor and fingerprint generation for machine learning, molecular database cartridge for PostgreSQL supporting substructure and similarity searches as well as many descriptor calculators, cheminformatics nodes for KNIME.
Calculates molecular descriptors and fingerprints. PaDEL-descriptor was developed using the Java language and consists of a library and an interface component. It currently calculates 797 descriptors (663 1D, 2D descriptors, and 134 3D descriptors) and 10 types of fingerprints. These descriptors and fingerprints are calculated mainly using The Chemistry Development Kit. Some additional descriptors and fingerprints were added, which include atom type electrotopological state descriptors, McGowan volume, molecular linear free energy relation descriptors, ring counts, count of chemical substructures identified by Laggner, and binary fingerprints and count of chemical substructures identified by Klekota and Roth. PaDEL-descriptor is free and open source, which has both graphical user interface (GUI) and command line interfaces, able to work on all major platforms (Windows, Linux, MacOS), supports more than 90 different molecular file formats, and is multithreaded.
An integrated drug discovery software. MOE is able to track design ideas and ligand modifications with property models, produce correlation plots to visualize Structure, Property, Activity Relationships and visualize hydrophobic and charged protein surface to study aggregation prone regions. It can also automatically align and superpose antibody structures using the MOE Project protocol, generate and search advanced antibody queries with the Project Search application and build full length Ig structures including bispecifics with the Antibody Homology Modeler.
Describes alignment-free molecules. xMap derives is based on MaP, a three-dimensional (3D) descriptor tool. This algorithm handles the fourth dimension (4D) and uses an ensemble of conformers generated by conformational searches. It functions through a five-step procedure and the most important descriptor variables are determined with chemometric regression tools. It can also display the derived quantitative structure-activity relationships.
Provides a customizable framework for molecular property calculation geared towards enabling database filtering. MolProp TK was developed to remove all compounds that should not be suggested to a medicinal chemist as a potential hit. The criteria for passing or failing a given molecule fall into three categories: (i) its physical properties such as molecular weight or bioavailability, (ii) the atomic and functional group content, and (iii) the molecular graph topology.
Contains many of the functions useful for examination of initial screening hits. PowerMV is designed to view compound datasets and facilitate the comparison of a target compound to near neighbor compounds in an annotated compound set. With this tool, biologists and statisticians can easily view compounds andcompute basic numerical descriptors to judge if a compoundis drug-like.
Enables the rapid calculation of a large and diverse set of descriptors encoding 2D chemical structure information. Mold(2) easily and quickly calculates molecular descriptors with no missing values, a common problem with most existing commercial systems. The descriptors used by this tool were compared with descriptors from commercial software packages using information entropy analysis, analysis of correlations between descriptors, and Decision Forest classification on several reported data sets.