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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!

Protein sequence analysis 

Exploring protein sequences using protein language models (pLM)

Protein structure analysis

Analyzing protein structures using geometric deep learning (GDL)
Analyzing protein structures using geometric deep learning (GDL)

Explore the impact of your therapeutic designs on biological systems

Protein interaction network
Protein interaction network and Vascular endothelial growth factor (VEGF)

LabSae allows biopharma companies to capture the behavior of the therapeutic designs within biological systems. It predicts protein-protein and drug-protein interactions and identifies 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?

We are supported by

Logo Bpifrance
Logo Inria
Logo Inria Startup Studio
Alliance Sorbonne Université (ASU)
PUI ASU-myStartup Program

We are scientific reviewers for

International Society for Computational Biology (ISCB)
International Society for Computational Biology (ISCB)
Bioinformatics - Oxford Academic
Neural Information Processing Systems (NeurIPS)

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.

LabSae added values: biopharma competitive advantage

You can apply for the early beta!

Events!

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