Abstract: A method and system for interpreting experimental data with automated reasoning. Domain specific knowledge is acquired from one or more pharmaceutical information sources. A semantic representation of the domain specific knowledge is created meeting a desired set of criteria. Pharmaceutical data from a knowledge database is classified with the semantic representation, allowing construction of a set of reasons for any classified pharmaceutical data. The set of reasons may help interpret the classified pharmaceutical data to remove errors, such as “physical errors” and “biological errors”. Removing such errors helps improve fusion of knowledge from multiple data, information and knowledge sources which incorporates activity and selectivity against a target, desired pharmacokinetic and toxicity properties enabling selection of potential pharmaceutical compounds.

Abstract: A method and system for creating and using knowledge patterns. One or more patterns derived from one or more pharmaceutical data sources are acquired. A knowledge map is created using the one or more patterns. The knowledge map, such as a self-organizing knowledge map, includes a selected representation of a pattern space. A set of selected regions from the knowledge map are annotated. Others patterns are classified with annotated regions of the knowledge map, thereby adding additional knowledge to the knowledge map. The knowledge map is used for recognizing previously unseen or unknown patterns from large amounts of pharmaceutical data obtained by automated screening systems. The knowledge map includes knowledge for a new real or virtual drug compounds or drug therapies. The knowledge patterns may be used to select new known pharmaceutical compounds, or model new virtual pharmaceutical compounds.