QSARis™ is a leading-edge database information system for creating Quantitative Structure-Activity Relationships (QSAR), to accelerate the discovery of new compounds in drug and agrochemical research.
Its comprehensive database functionality, use of leading-edge descriptors, built-in statistical analysis routines, and ease-of-use make it a powerful tool for medicinal chemists and QSAR professionals. QSARis is truly in a class by itself.
QSARis Features
- Visualize data for selection. 2D & 3D relational plots (cluster and similarity) allow identification of compounds versus activities, in order to construct data subsets.
- 2D and 3D atomic and molecular descriptors. 2D descriptors (e.g., E-state, topological, connectivity, shape) and 3D descriptors (e.g., charge, dipole moment, and advanced molecular moment descriptors) result in robust QSAR modeling. Over 400 descriptors are available including an advanced LogP model based on 8,900 known logP values (see SciLogP® Ultra).
- Descriptor selection. Automatic or manual.
- Statistical routines. Built-in multi-linear regression (MLR), genetic analysis, principle component analysis (PCA), and partial least squares (PLS) make it easy to construct QSARs.
- Dynamic QSAR Modeling. Ability to delete columns and rows "on-the-fly" to study subsets of compounds and descriptors. Use 2D or 3D or both classes of descriptors.
- 2D and 3D small molecule builder. Make changes on existing compounds or submit new compounds to predict bioactivities or run similarity comparisons.
- Rapid Database Search and Retrieval Capabilities.. Whether sub-structure or similarity searches, 100,000 compounds can be searched in seconds with our unique database structure.
- Qbase. Optional database of several million compounds with bibliographic data and 350 bioactivity keywords for identifying compounds.
- Key Benefits
- The advantage of QSARis is that it performs rapid processing of thousands of molecules using 2D descriptor variables. QSARis runs advanced similarity analysis and QSAR modeling on small datasets, based on molecular charge distribution and shape using Comparative Molecular Moment Analysis CoMMA) descriptors.