Insilica OS: the AI engine for programmable biosynthesis
Scientists, agents, and knowledge. Working as one.
Insilica OS, our proprietary platform, provides a streamlined workflow that brings together scientists, built-in specialized AI agents, chemical and biochemical databases, scientific literature, domain knowledge, and purpose-built software. Its orchestration engine connects these capabilities and resources throughout the design process, enabling scientists and agents to access relevant evidence, apply computational tools, and build on insights generated at each step. The underlying software infrastructure is optimized for targeted scientific computation, making efficient use of resources across the workflow.
The DBTL loop: from property requirements to genetic blueprints
Inverse QSAR
Translation of desired chemical properties and environmental constraints into targeted structural profiles, defining molecular features that guide candidate selection and provide a foundation for designing molecules suited to specific applications.
- Solubility
- Stability
- Reactivity
- Binding affinity
- Lipophilicity
- Redox potential
AI-based inference
Identification of potential genetic origins of target molecules through computational inference, linking molecular structures to biosynthetic pathways, enzymes, and encoding genes to inform the design of biological systems for production.
Digital chassis
Simulation of gene knockouts within digital models of microbial hosts to identify strategies for maximizing metabolite yield while preserving host viability, supporting the prioritization of genetic interventions for experimental validation.
Reinforcement
Integration of experimental results from biofoundry robotics to recalibrate predictive models and refine subsequent designs, creating a continuously self-improving loop that strengthens predictions and guides successive iterations of biological engineering.
The agentic intelligence powering the platform
Built in-house, our specialized AI agents form the foundation of the platform, operating as an agentic layer that selects the best-suited LLM on demand for each task. Designed for human-in-the-loop collaboration, they support our scientists in setting research priorities and directing development. Vector-based retrieval and cache reuse optimize context and reduce repeated processing. The agents also help develop and invoke purpose-built scientific software within a dedicated computational infrastructure, connecting language-based reasoning with specialized calculations.