Danai Mavreli

Noelle Baquiche

Yiannis Kiachopoulos

Angeliki Andrianopoulou

Magdalena Sommenvik

Artur Saudabayev

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.

An interview with Imad Yassin on how AI is transforming translational research and why Causaly is one of the biggest game-changers he's seen to date.

How Causaly AI is transforming translational research

How Causaly AI is transforming translational research: Interview with Imad Yassin

In December 2019, a series of respiratory infection cases emerged in Wuhan City,
China. After sequencing analysis, they have been...

How can AI accelerate the COVID-19 research?

The process of finding and evaluating existing clinical research is central to
all areas of biomedicine, providing the foundations upon...

Can AI enhance traditional clinical literature research methods?

The objective of this study was to evaluate all possible symptoms of SMA to identify relevant research articles and to define SMA prevalence comprehensively. We asked the question: What are the disorders and syndromes associated with SMA?

Understanding Clinical Outcomes of Spinal Muscular Atrophy

Application of Artificial Intelligence how to rapidly identify under-explored drug candidates on the the example of PLK1. 

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

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