Abstract: A system for analyzing a vast amount of data representative of chemical structure and activity information and concisely providing conclusions about structure-to-activity relationships. A computer may adaptively learn new substructure descriptors based on its analysis of the input data. The computer may then apply each substructure descriptor as a filter to establish new groups of molecules that match the descriptor. From each new group of molecules, the computer may in turn generate one or more additional new groups of molecules. A result of the analysis in an exemplary arrangement is a tree structure that reflects pharmacophoric information and efficiently establishes through lineage what effect on activity various chemical substructures are likely to have. The tree structure can then be applied as a multi-domain classifier, to help a chemist classify test compounds into structural subclasses.

Abstract: A computer based system for analyzing a set of data objects and establishing a mechanism model representing a set of features that is likely to correlate with a specified response characteristic. A computer may establish a description for each of the data objects based on a comparison between a set of descriptors and features of the data objects. The computer may then select a group of the data objects that have similar descriptions and that represent the specified response characteristic. The computer may then adaptively learn a mechanism model by mapping the discriminating features of the group back to the objects in the group. The computer may further designate the mechanism model as a new descriptor and iteratively repeat the process to establish yet an improved mechanism model. The invention is particularly well suited for use in establishing pharmacophores representing chemical structures that are likely to correlate with activity in a particular assay.

Abstract: A system for helping a chemist to identify pharmacophoric mechanisms, based on a set of input data representing many chemical compounds. Given an input data set defining for each compound a feature characteristic and an activity characteristic, a computer agglomeratively clusters representations of the molecules based on their feature characteristics. The result of this process is a multi-domain pyramid structure, made up of a number of nodes each representing one or more molecules. For each node, the computer identifies a representative feature set (such as a largest substructure common among the molecules in the node) and a representative activity level (such as an average of the activity levels of the molecules in the node). The computer then provides as output to a chemist a description of all or part of the pyramid. This process thus converts a large set of raw data into an understandable and commercially useful form, which can assist the chemist in developing beneficial new pharmaceuticals.