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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.
Generate real-time, updated data from various scientific sources (publications, clinical trials, congresses etc.) with the most comprehensive life sciences ontology for concept-based contextual and relevant search results. Search for dissimilar concepts and discover previously unknown information. Get summarized snapshots of up-to-date clinical development pipelines across indications, interventions, and therapies to aid informed decisions for strategizing your clinical research projects.
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.
Generate bias-free insights to make infinite dynamic molecular interactions clear and enable faster target identification. Visualize novel and well-established relationships connected to each other and their correlation scores from the literature, to explore all possible direct and indirect associations. Prioritized targets based on a custom druggability score, which includes number of relevant data sources (publications, trials, etc.), approved drugs for a given target, experimentally known structures, number of antibodies, genetic associations, and more.
Data as a Service
Endotype Response & Personalized Medicine