# Rayca Precision > Rayca Precision is an agentic AI drug-discovery company. Its self-improving engine designs, simulates, and triages therapeutic candidates across modalities for hard-to-drug targets, and preserves institutional memory across every program. Rayca is disease-agnostic and focuses on targets that resist conventional approaches. This document is the full content of rayca.ai, provided as a single file for language models. Site: https://rayca.ai/ --- ## Hero The collective intelligence engine for drug discovery. One engine for target finding, molecular simulation, and structure-aware design. It carries forward what each program learns. Your teams own every decision. Capabilities: target finding, molecular simulation, structure-aware design, cryptic pockets, free-energy methods, data lineage, in silico. --- ## Programs end. Knowledge endures. When a program closes, its hard-won knowledge usually scatters across files, people, and archives no one reopens. The engine keeps that lineage intact, and brings it to bear on the next target. --- ## One engine, step to step A single system runs the discovery cascade end to end: 1. Find the target. Disease biology and target-disease association point to mechanisms worth pursuing. Your biologists decide which advance. 2. Model it in motion. Conformational ensembles expose cryptic pockets a single static structure misses. 3. Design the modality. Structure-aware generative design across small molecules, peptides, antibodies, nanobodies, and degraders. 4. Test against physics. Free-energy methods score each design against how the target moves, before anything is made. 5. Learn from the bench. Wet-lab results return to the engine and sharpen the next cycle. People own every result. --- ## The platform: agentic molecule design, with a memory Most discovery stacks forget the moment a program closes. Rayca's accrues. Every target modeled, every candidate scored, every assay returned becomes institutional memory the engine reasons over the next time it designs a molecule. The engine: a design superintelligence that compounds. It reads the target in motion, generates across modalities, triages every candidate against binding thermodynamics, then writes the verdict back into a memory no program can lose. Each cycle, its judgment sharpens. - Institutional memory. Target structures, design rationale, and assay outcomes are kept as living lineage, queryable across every program. The engine reasons over years of accumulated context, never a blank prompt. - Adaptive workflow synthesis. It authors its own discovery protocol, then rewrites it as data returns. No fixed pipeline to outgrow, no playbook to keep current. - Multi-model orchestration. Generative design, conformational sampling, and free-energy scoring run as one orchestrated swarm across distributed inference. - Physics-grade triage. Thousands of candidates are simulated and ranked against binding thermodynamics, so only the molecules worth making ever reach the bench. --- ## The full discovery stack - Adaptive workflow builder. Compose target-to-candidate pipelines step by step. Each run adapts from the last; the engine carries forward what worked. - Agentic tools. Agents call simulation, design, and assay tools on their own, planning the next step rather than waiting for a script. - AIDD stack and data connections. Models, assays, and structure databases wired into one stack. Connect a source once and every agent can reach it. - Orchestration at scale. Fan tool calls out across distributed compute and pull results back in; thousands of designs scored in parallel. - Agent and file lineage. Every result traces to the agent, tool call, and input file that produced it. Full provenance, ready for regulated work. --- ## Built to inspect - End-to-end data and decision lineage. - Every result traces to its inputs. - A foundation suited to regulated work. - People own every go and no-go. --- ## Built for hard targets Built across modalities: small molecules, peptides, antibodies, nanobodies, and degraders. Built for undruggable targets, cryptic pockets, and complex modalities. Targets that resist conventional approaches. What the engine learns in one target class informs the next. --- ## The field is fragmented. We connect it. One connected system. Target finding, simulation, design, and self-audit run as one system, not a chain of disconnected tools. Each program's reasoning stays available to the next, instead of sitting in an archive no one opens. --- ## An adaptive workflow that compounds A self-improving agentic system that learns to model proteins, complex modalities, and the physics between them, growing more sophisticated with every program it runs. - Proteins in motion: ensembles, not snapshots. - Curated lineage: every result, traceable. - Self-auditing: smarter every cycle. Built for the hardest modalities: - Degraders and PROTACs. Ternary-complex design, induced proximity. - Antibody-drug conjugates. Payload, linker, vector; targeted delivery. - VHHs and nanobodies. Single-domain binders for cryptic epitopes. A system that learns to think better. Five stages, one closed loop: 1. Model proteins. Conformational ensembles capture targets in motion, exposing cryptic pockets a single static structure conceals. 2. Design modalities. Structure-aware generative design spans degraders, PROTACs, ADCs, and VHHs, not just small molecules. 3. Score against physics. Free-energy methods adjudicate every candidate against how the target actually moves, before anything is synthesized. 4. Curate lineage. Proprietary, end-to-end provenance binds every result to the inputs, agents, and tool calls that produced it. 5. Audit and evolve. A self-auditing layer scrutinizes its own decisions and folds the verdict back in, sharpening the engine every cycle. --- ## Work with us Bring us your hardest target. Co-development partnerships on shared programs, and fixed-scope discovery pilots with a defined target, deliverables, and timeline. Your team sets the target and owns every decision. Get in touch through the Partner with us form on the site. --- ## Offices - Helsinki, Finland. Rekipellontie 8. - London, United Kingdom. 71-75 Shelton Street, WC2H 9JQ. - San Francisco, United States. 28 Geary St, CA 94108. --- ## Newsroom - Rayca Precision and ASAKE Partner on AI-Designed Therapeutics for Rare and Metabolic Diseases. AI-designed, physics-triaged therapeutics for rare and metabolic disease. Press release, May 26, 2026. https://www.biopharmadive.com/press-release/20260526-rayca-precision-and-asake-partner-on-ai-designed-therapeutics-for-rare-and-1/ --- ## Links - Home: https://rayca.ai/ - Newsroom: https://rayca.ai/news/ - LinkedIn: https://www.linkedin.com/company/raycaai/ - X: https://x.com/RaycaAI - YouTube: https://www.youtube.com/@RaycaBio - Instagram: https://www.instagram.com/rayca.ai - Facebook: https://www.facebook.com/RaycaBio