Target identification and validation using AI for literature-based insights: Causaly & Pierre Fabre Joint Webinar

Rachael Burroughs
published on November 04, 2020

Causaly and Pierre Fabre joint webinar

Causaly and Pierre Fabre co-hosted a joint webinar on the 28th of October, addressing how pharmaceutical companies, in the context of Translational Medicine, combine Natural Language Processing and other AI technologies with expert judgement to investigate and select promising therapeutic targets.

Request access for this webinar to learn more about how:

  • Pierre Fabre approaches the data challenges that come with target identification and validation, and how AI contributes effectively to better outcomes.
  • Causaly’s AI platform supports researchers to understand critical relationships between targets, drugs and indications that are continuously mined from millions of research publications and merged into a high-precision Causal Knowledge Graph.

Causaly AI was proven to save 80% of their research time.

Want to know more about this session? Please request access to the recording here.

Causaly vs PubMed®: 2x as many relevant articles identified by Causaly using the same data
use case

Causaly vs PubMed®: 2x as many relevant articles identified by Causaly using the same data

Causaly AI finds more relevant articles than PubMed alone, using its advanced machine-reading technology.

Full-text vs Abstract advantage: Causaly identifies 3x as many relevant articles by machine-reading the full-text
Application

Full-text vs Abstract advantage: Causaly identifies 3x as many relevant articles by machine-reading the full-text

Causaly enables regulatory experts to reduce time spent scanning research literature, while at the same time increase the yield from full-text articles which typically are not selected due to unsuspecting abstracts.

Preclinical safety analysis using Artificial Intelligence on the example of Alzheimer’s Disease.
Application

Preclinical safety analysis using Artificial Intelligence on the example of Alzheimer’s Disease.

Causaly AI enables researchers to identify safety-relevant information in medical literature regarding a drug candidate. Preclinical experts can include this data in the preclinical study design to minimize the risks of unforeseen toxicities and increase chances of approval.

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