We simulate a real cellular environment
by explaining protein interaction networks.
Computational predictions are just ideas until they are validated experimentally in a real cellular environment. LabSae captures the essence of biological systems and advances in-silico drug development by in-depth analysis of proteins and their interaction networks!
LabSae allows biopharma companies to identify targets, map signaling pathways, and explore the therapeutic agents' impact on biological systems. It predicts protein-protein and protein-drug interactions and highlights potential interaction sites, including hotspots and pockets.
Discover how deep learning is revolutionizing computational biology—check out our blogs!
LabSae focuses on answering four essential protein-related questions
Network
Which proteins are interacting with each other?
Conformations
In what conformation do two proteins interact?
Hotspots
Where are the crucial binding sites on the protein surface?
Mutations
How do mutations affect protein interactions?
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The in-silico pre-clinical development acts as the foundation for therapeutic discovery. Despite its importance, it often oversimplifies biological processes and suffers from various challenges, such as low predictive accuracy, small-scale analysis, and reliance on reference biological data. This creates a vicious try-and-error cycle between the pre-clinical and clinical phases to find suitable pharmaceutical interventions.
This problem stems from our very limited understanding of biological systems, which are primarily governed by proteins and their interactions. Large-scale computational approaches can address this problem and will enable us to dramatically improve our knowledge on proteins.
LabSae provides a holistic analysis of biological systems at molecular resolution.
