Peggy Westlotorn

Richard Harrison

Rachael Burroughs

Yiannis Kiachopoulos

Anna Tzani

Danai Mavreli

Noelle Baquiche

Angeliki Andrianopoulou

Magdalena Sommenvik

Artur Saudabayev

Causaly enables regulatory experts to reduce time spent scanning research literature, while increasing the yield from full-text articles

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

For preclinical safety, gathering all available data regarding side effects for drug candidate is vital.  Causaly facilitates this process and enables experts to minimize the risks of unforeseen toxicities timely and efficiently and increase chances of approval and subsequent market launch. 

Development of safer drugs using Causaly AI on the example of Alzheimer’s Disease. 

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

In the field of SLE, with over 2,000 scientific papers published in 2020 alone, it is important for target identification experts to stay on top of recent advancements to develop novel therapeutics. Causaly can facilitate this process and enable researchers to identify potential targets as well as investigate the underlying mechanism of action and druggability in an efficient and timely manner.

AI-supported target Identification for Systemic Lupus Erythematosus.

AI-supported Target identification for Systemic Lupus Erythematosus.

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

Posts written by Danai Mavreli

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