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Our agents integrate with a vast network of public and private databases. This includes chemical databases like PubChem and ChEMBL, biological databases such as UniProt and PDB, and clinical trial repositories like ClinicalTrials.gov. We can also integrate with your internal systems, such as ELNs or HTS platforms, to create a unified data ecosystem.

The Drug Discovery Agent employs a suite of generative models. After identifying a biological target, these models design novel chemical scaffolds that are predicted to have high binding affinity. The agent then performs a multi-parameter optimization, considering ADMET properties, synthetic accessibility, and potential off-target effects to refine and prioritize the best candidates.

While traditional drug discovery can take over a decade, our AI-powered repurposing agent significantly accelerates this process. By leveraging existing safety and pharmacokinetic data, we can typically move from hypothesis to a list of high-potential, validated candidates in a matter of weeks. The subsequent preclinical and clinical phases are also shortened, with a potential time-to-market of 3-5 years.

Yes. While our agents are autonomous, our platform provides an interface for users to define the scope and parameters of a project. You can specify the disease area, biological target, drug class, or even upload your own proprietary compound library for analysis. Our team works with you to configure the agent for your specific research goals.