sevenTM operates an AI-driven platform that enhances early drug discovery by predicting toxicity and optimizing drug candidates through in-silico and in-vitro validation. The technology specifically targets the development of low-toxicity therapies for oncology and rare diseases, addressing the high toxicity and limited efficacy of existing treatments.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional drug discovery methods often face challenges in predicting toxicity and optimizing drug candidates early in the development process, leading to costly failures and delays in bringing new therapies to market, particularly for oncology and rare diseases. Existing treatments often suffer from high toxicity and limited efficacy, underscoring the need for more precise and less harmful therapeutic options.
Solution
sevenTM offers an AI-driven platform designed to streamline early drug discovery by accurately predicting toxicity and optimizing drug candidates through a combination of in-silico and in-vitro validation techniques. The platform leverages first principles and real-world validation via in-vitro feedback loops to ensure the reliability and effectiveness of its models. By identifying new leading candidates and novel indications for existing drugs, sevenTM focuses on developing low-toxicity therapies, especially for low population diseases and previously "untargetable" protein targets. The platform's high-throughput design and screening capabilities, running on a cloud-native, end-to-end pipeline, significantly accelerate the identification of promising compounds.
Target Audience
The primary target audience includes pharmaceutical companies, research and development teams, and academic partners focused on oncology, rare diseases, and low population diseases seeking to accelerate drug discovery and reduce toxicity.
Features
- AI-powered tools for toxicity prediction, protein structure modeling, candidate docking, and manufacturability prediction
- High-throughput screening of up to 1 billion compounds per hour and design/ranking of 1,000-10,000 compounds per hour
- LLM-assisted target and ligand identification, analyzing 800-1200 pages in 5 minutes
- Molecular Dynamics simulations for target modeling, up to millisecond-scale
- In vitro validation through automated assays characterizing biophysics and cellular effects
- Cloud-native, end-to-end pipeline running on a high-performance computing cluster
- Scalability assessment to determine manufacturability of drug candidates