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Mar 24

Partex NV announces collaboration with Althea DRF Lifesciences to provide comprehensive end-to-end services to accelerate drug discovery and development

Frankfurt, Germany; 28 March 2024 – Partex Group, a pioneer in AI-driven drug discovery, announces a collaboration with Althea DRF...
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18

Mar 24

Innovative AI technology in oncology: Partex Group presents results from a pilot project

During the German Cancer Congress 2024 in Berlin, the companies Innoplexus AG /Amrit AG, companies of Partex N.V., presented the...
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Innoplexus wins Horizon Interactive Gold Award for Curia App

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Innoplexus Featured in DataQuest

Innoplexus launches iPlexus to transform decision support in the Life Sciences Industry

Innoplexus, the technology and product development company focused on AI and advanced analytics, announced the launch of its product iPlexus, an end to end platform for Life Sciences which leverages artificial intelligence to generate continuous intelligence and insights across discovery, clinical development, regulatory and commercial stages of drug development, spanning all major therapeutic areas and indications. iPlexus facilitates better ways of discovering, exploring and analyzing biomedical research.

The platform has crawled, curated and indexed hundreds of terabytes of scientific data across hundreds of clinical trial databases, biological databases, major patent offices, congresses, theses, forums, regulatory bodies and much more. Users get quick, crisp and summarized snapshots of the current landscape in any given context, be it important deals and mergers, fast-tracked clinical trials etc, helping them make informed and data-backed decisions. In the drug development process, traditionally, there was a lack of a single source of information for reference.

Speaking on the launch, Dr.Gunjan Bharadwaj, Founder and Chairman, Innoplexus, commented, “We are extremely excited to launch iPlexus. Traditionally, both production and consumption of intelligence and insights in enterprises remain predominantly manual. In the current scenario of big data, analytics and machine learning, teams of analysts in companies still curate data manually. These companies then sell it to customers at very high prices. Today, the volume, velocity, variability and veracity of data makes this manual process impossible. Our goal is to make access to insights real-time, comprehensive and cost effective. iPlexus ensures instant availability of insights across deep, dense and diverse data sets for taking informed decisions in key therapeutic areas, while saving time and effort from collecting and updating the same information from different sources manually. The platform makes use of machine learning and AI to generate continuous intelligence. It’s also a mission to democratize data and insights for small and medium organizations which cannot afford traditional offerings”

“iPlexus helps in the better discovery of valuable research available with institutes/labs, small pharma and large pharma organizations, niche biotech companies, academic institutes, various conferences, congresses, societies etc. This research data may not be instantly discoverable as the source of each is different. Information is therefore scattered and is not readily available to a researcher, for consumption. Our platform bridges this gap by collecting, cleaning, categorizing, analyzing and arranging various kinds of information readily for the user, backed by cutting edge tech.”, he added.

iPlexus makes relevant information readily available to a researcher which can be referred to at any point, as per their requirement.

For instance, if a researcher is working on a niche molecule and iPlexus gives him/her an information byte that a close competitor has already got positive results working with a similar molecule, then the researcher can use this information to decide his/her next steps in the research chain. Similarly, if a user is looking for research papers on a specific topic and he/she gets to know about something that is trending within the topic domain, it would be an added advantage.

The original article was published on DataQuest.

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