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NOVA Hit-to-Lead Optimization Agent In collaboration with @chutes_ai and @onepot_ai We're developing a Hit-to-Lead optimization agent that integrates an algorithm developed by a NOVA Blueprint winner, extending NOVA's capabilities beyond hit discovery and into the iterative optimization of small molecules. The system combines a domain-specialized AI agent with proprietary combinatorial chemical databases supplied by our partner Onepot AI, spanning billions of possible compounds, that are delivered within 5-7 business days. Researchers will be able to guide the agent in developing, comparing and refining optimization strategies, exploring chemical modifications and prioritizing candidates based on predicted affinity, selectivity, physicochemical properties and specific binding interactions. Confidentiality is a central design requirement. Pharmaceutical R&D involves valuable intellectual property, including compound structures, targets, emerging chemical series and optimization strategies that cannot be exposed publicly. The Chutes team integrated domain specific models so that we can run inference in their verified Trusted Execution Environments (TEEs), with encrypted communication and controlled access to proprietary databases and project records. This architecture is intended to support confidential inference on external infrastructure while preserving organizational control over sensitive research data. Finding a molecule with promising predicted affinity is only the beginning. Hit-to-Lead optimization involves balancing multiple, often competing objectives, including potency, selectivity, physicochemical properties and synthesizability, before progressing toward experimental activity and ADME validation. The next step is to integrate the agent into our own Hit-to-Lead development program. For NOVA, this represents an important step toward translating algorithms developed through open competition into tools for proprietary drug discovery programs, including our own asset-development pipeline and those of external research partners.