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Current drug discovery efforts require significant time-consuming manual efforts to search literature, publications, and presentations to identify connections between genes, pathways, molecular targets, and drugs. Such efforts require searching through disparate databases and using search engines with poor understanding of the life sciences language, leading researchers to have an incomplete understanding of all the connections and interactions between biological entities. Innoplexus’s technology enables researchers to have all crawlable online published data at their fingertips and easily visualize connections between closely and distantly related entities. Innoplexus has created a self-learning life sciences ontology that understands life sciences phrases rather than lone words, which empowers users to search concepts and receive relevant entries.
Leverage AI to make process precise, faster & objective. Empower your researchers to identify biomarkers to specifically pinpoint diseased patients, to cluster the patients based on their biological endotypes, and to isolate patient groups who are likely to respond to selected drug(s), leveraging artificial intelligence over aggregated enterprise and public data. Continuous intuitive interface transparently visualizes interpretation of AI output with relevant network and provenance.
Use Innoplexus’ full capabilities, including identifying connections between biological entities, predicting toxicologies, validating the biological activity of new molecules, estimating clinical trial costs, and assessing the competitive landscape to determine drug assets most likely to demonstrate efficacy, safety, and commercial success.
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