Amidst the demand for large-scale data processing and analysis, researchers grapple with vast, diverse data, especially text-based data with its inherent complexity, high dimensionality, and lack of standardization. Navigating biopharma’s intricate regulatory demands further complicates this. Causal inference is pivotal in deciphering disease biology in this intricate environment.  

In the world of life sciences, we’re standing at the edge of a transformative era. The last decade has seen machine learning carve out significant advancements, enabling experts to predict protein interactions and design molecules with unprecedented precision. 

Causaly cofounders Yiannis Kiachopoulos and Artur Saudabayev hosted a webinar, “From AI to Impact”, in April 2023, exploring the transformative power of AI and GPT applications in early drug discovery. During this captivating session, they demonstrated how Causaly combines visual evidence with high precision knowledge graphs to answer complex research questions in mechanistic biology.