STREAMLINING EARLY DRUG DISCOVERY

From idea to preclinical
in 5 clicks

What we do


sevenTM empowers pharmaceutical and biotech companies to design and validate more candidates, in less time, with a focus on minimizing toxicity and side effects.

Our AI-powered tools, based on first principles, revolutionize the current drug discovery process for R&D teams with minimal disruption, while producing measurable improvements. Real-world validation through in-vitro feedback ensures the reliability and effectiveness of our models.

By leveraging our platform, we have identified new leading candidates and found novel indications for existing or approved drugs in the areas of oncology and rare diseases. The most promising compounds are currently undergoing in vitro validation and optimization of their ADMET properties.

Our platform is particularly well suited to design therapeutics for low population diseases, especially those involving protein targets that are considered untargetable. We actively seek collaborations with private and academic partners for these projects.

Why we do it


Low population disease therapeutic design remains difficult, and without  technological advance, these patients will remain underserved.

We have developed stand alone tools that augment and improve the early drug discovery process.  This includes toxicity prediction, protein structure modeling, candidate docking and manufacturability prediction.

Our models gain real-world validation via in-silico/in-vitro feedback loops. We do this by partnering with R&D teams of Pharmaceutical and top researchers to design new candidates to move to clinical trials leveraging our AI-powered drug discovery pipeline.

      Our sponsors

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Our platform

High-throughput design and screening of small molecule drug candidates, accomplished through a cloud-native, end-to-end pipeline running on our cluster.
The top new candidates are tested in a highly automated fashion in our partners' labs.

Target Identification and Modeling

Target identification and modeling

LLM-assisted target and ligand identification
800-1200 pages analyzed in 5 minutes
Input required: user question + 1 click

Target modeling
Up to millisecond-scale
Molecular Dynamics simulations
Input required: FASTA or PDB files + 1 click
Optional inputs: positive control(s), simulation parameters (force field, solvent model, membrane environment)

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High Throughput Ligand Generation and Screening

High-throughput generation and screening

1 billion compounds screened per hour
1,000-10,000 compounds
designed and ranked per hour

Input required: SMILES string(s) + 1 click
Optional input: library of compounds of interest. Default: ZINC20, comprising 800 million small molecules encoded by us in a format optimized for parallel computing

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Toxic

Toxicity prediction and screening

1,000-5,000 candidates analyzed per hour

Input required: tolerance threshold for toxicity + 1 click
Optional input: set or library of compounds of interest

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drug scalability

Drug scalability assessment

1,000-5,000 compounds analyzed per hour

Input required: desired range from our scalability-stability tradeoff map + 1 click
Optional input: set or library of compounds of interest

Under construction

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In vitro testing

In vitro validation

Virtually identified hits and designed lead compounds are assessed through a combination of assays that characterize their biophysics (target affinity) and cellular effects

This process is highly automated, with assays conducted in 96 and 384 plates

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Work in progress

Coming soon

Platform for fusion protein drug discovery

Personalized drug development and drug optimization based on genetic profiles

Quantum algorithms for protein molecular dynamics and docking

Platform for high-throughput development of bRo5 and new therapeutic modalities

Ministère de l'Économie, de l'Innovation et de l'Énergie du 
Québec
NVIDIA
District 3 Innovation Hub Montreal
AWS Marketplace
McGill University Health Centre
MaRS Discovery District
Consortium Québécois Sur La Découverte Du Médicament
C2 Montreal

Support

Ministère de l'Économie, de l'Innovation et de l'Énergie du 
Québec
McGill University Health Centre
PinQ2
Consortium Québécois Sur La Découverte Du Médicament
NVIDIA
AWS Marketplace
District 3 Innovation Hub Montreal
MaRS Discovery District
C2 Montreal