Ontomine Ontomine is a data mining software used for automated Molecular Mining for BioActivity, Toxicity and Side effect prediction. Ontomine is based on experimentally determined properties from around 100.0
Download

Ontomine Ranking & Summary

Ontomine Tags

molecular molecular evolution molecular graphics data mining mining mining engine visualize molecular structures molecular modelling molecular geometry molecular calculator Process Mining molecular structure text mining molecular viewer Molecular View Molecular descriptor Molecular Machine Molecular Dynamics Package molecular interaction networks molecular model molecular visualization molecular editor molecular modeller molecular sketching Molecular Orbital molecular analysis molecular design Molecular Designer interactive sequence mining Molecular Mining Bio-Activity Test mining tool text mining platform text mining algorithm simulate molecular dynamics molecular diameter Molecular Simulation molecular graphic Molecular System find molecular substructure Molecular representation Molecular Dynamics Molecular Simulator animate molecular trajectory molecular surface molecular topology molecular phylogeny neutral molecular molecular sequence molecular phylogenetics sequence mining CDK molecular descriptor molecular 3-D viewer

Ontomine Description

A data mining software used for automated Molecular Mining for BioActivity Ontomine is a data mining software used for automated Molecular Mining for BioActivity, Toxicity and Side effect prediction. Ontomine is based on experimentally determined properties from around 100.000 diverse small molecules, collected from databases, encyclopedias and other literature followed by expert hand-curation. This dataset is formulated into hierarchical trees composed of several thousands of classes of bioactivities, drug targets, therapeutics areas, adverse effects and toxicities. Each class is represented by a fingerprint for the related molecules for their molecular properties, like presence/absence/counts of side chains/chemical activities, ring structures etc. Ontomine and comparison to other existing methods Other currently used bioactivity prediction approaches can be classified into three categories: * graph theoretic and substructure, topological and physicochemical chemical descriptors * maximum common substructures, fingerprints similarities and machine learning methods * atom neighbourhood methods However, these methods are currently limited by factors like distance metrics dependance, cut-offs not being representative of activity, hard to build QSAR models at several different levels, e.g. drug targets, bio processes and therapeutic area as well as interpretation of results. Ontomine (US patented) is alternative to these methods, transforming the structural information for chemically, biologically or pharmacologically related molecules to a hierarchical schema of concepts and descriptors. Ontomine discovers patterns in the related schema and predicts biological activity, using rules inferred from analyzing the patterns. One significant advantage of Ontomine algorithms is that they enable scaffold hopping, since the core scaffold is not conserved when patterns characteristic of an activity is discovered. Ontomine patterns represent "necessary but not sufficient" conditions for bioactivity irrespective of a scaffold. Explicit scaffold hopping can be performed by generating constitutional isomers and selecting interesting molecules using the rule base. The results of Ontomine are presented as simple understandable patterns representative of activity. Further, Ontomine predictions are scalable to millions of molecules. In addition, the curated Ontomine datasets can be appended with internal datasets of measured molecule activities of in-house compound collections.

Ontomine Related Software