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.

Explore the platform

Programsend.Knowledgeendures.

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.

Oneengine.Step tostep.

  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. Ours 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.

See it learn and adapt
01

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.

02

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.

03

Multi-model orchestration

Generative design, conformational sampling, and free-energy scoring run as one orchestrated swarm across distributed inference.

04

Physics-grade triage

Thousands of candidates are simulated and ranked against binding thermodynamics, so only the molecules worth making ever reach the bench.

The fulldiscovery stack.

Target
Simulate
Design
Score
bench results feed back each cycle

Adaptive workflow builder

Compose target-to-candidate pipelines step by step. Each run adapts from the last, carrying forward what worked.

Molecule gen
Protein gen
Docking
FEP
Retro-synth
Scoring
ADMET
Pockets

Agentic tools

One agent orchestrates the full AIDD stack: molecule and protein generators, docking, FEP, scoring, and ADMET. It calls each on its own and plans the next step.

Agents & reasoning
AIDD model stack
Connected data sources

AIDD stack & 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 & file lineage

Every result traces to the agent, tool call, and input file that produced it. Full provenance, ready for regulated work.

inputagenttoolresult

Built to
inspect.

  • 01End-to-end data and decision lineage
  • 02Every result traces to its inputs
  • 03A foundation suited to regulated work
  • 04People own every go and no-go

Built forhard targets.

Targets that resist conventional approaches. What the engine learns in one target class informs the next.

Your silos become memory.

Rayca brings a pharma molecular-design superintelligence into your own secured research environment. Nothing leaves your perimeter. Decades of scattered R&D become one living institutional memory that guides precise molecule design and deepens with every program.

Inside your perimeter

01

A pharma molecular-design superintelligence, deployed inside your own secured research environment. Your data, your IP, your control. Nothing crosses the boundary.

Silos become memory

02

Assays, structures, screens, and program decisions, fragmented across teams, vendors, and tools, unify into one queryable, collective institutional memory.

Memory guides design

03

That memory directs agentic, automated, adaptive computational workflow design and orchestration toward the molecules most likely to succeed, precise by default.

Workflows that build on each other

04

The system designs, orchestrates, and audits its own discovery workflows. Every program leaves the next one sharper, with full lineage and human control at every gate.

Your world, with Rayca

05

Your scientists stop re-deriving what the organisation already knew. Discovery moves at the speed of decision, and institutional knowledge finally accumulates instead of scattering.

The agentic operating layer

An adaptive workflow
that sharpens.

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

Modalities

Built for thehardest modalities.

Degraders, antibody–drug conjugates, and single-domain VHHs each demand their own structural reasoning. The engine designs across every class, and what it learns in one carries into the next.

Small moleculesPeptidesAntibodiesNanobodiesDegraders

Degraders & PROTACs

Ternary-complex design

Induced proximity

Antibody–drug conjugates

Payload · linker · vector

Targeted delivery

VHHs & nanobodies

Single-domain binders

Cryptic epitopes

A system that learnsto think better.

Five stages, one closed loop. Modeling, design, scoring, lineage, and self-audit feed one another. Each pass leaves the operating layer sharper than the last.

Click any node to open its card and trace connected stages.