EN中文
Request a trial

Home / ReAIX Platforms

ReAIX Platforms

ReAIX

Three platforms across drug substance and drug product

From how the drug substance is made to how it becomes a stable drug product. ReAIX Chem and ReAIX Bio optimize drug substance processes for small molecules and biologics; ReAIX Formulation develops the drug product.

ReAIX Chem

Drug substance · Small molecules

Optimizes reaction conditions for API synthesis and ranks the next experiments to run.

  • Solvent, catalyst and ligand selection
  • Temperature and stoichiometry optimization
  • Yield and impurity optimization
  • Process safety and risk assessment
  • Cost of goods (COGS) evaluation
Request a trial →

ReAIX Bio

Drug substance · Biologics

From antibody developability and sequence to upstream process development and scale-up, with AI models that replace slow simulations.

  • Antibody developability screening and assessment
  • Antibody sequence optimization
  • Upstream process development with AI predictions
  • AI agent–guided optimization of scale-up parameters
  • Physics-informed AI for CFD analysis
Request a trial →

ReAIX Formulation

Drug product · Formulation

Predicts API–excipient compatibility and formulation stability before lab work begins. Currently focused on small-molecule drug products.

  • API–excipient compatibility screening
  • Stability prediction
  • Pharmacokinetic (PK) prediction models, in development
  • Faster formulation development
  • AI-assisted literature and data review
Request a trial →

How it works

From billions of conditions to five experiments

ReAIX scores every combination of your process parameters and returns a short, ranked list to test in your lab.

Example: Suzuki coupling · illustrative —

  1. Define the objective

    Set the target (yield, purity, cost or cycle time), the constraints and the data you already have.

    past experimental data · literature data · AI-generated data

  2. Search the condition space

    GPU-accelerated annealing and AI models score billions of candidate conditions.

    GPU annealing · AI models · quantum chemistry

  3. Validate and iterate

    Your team tests the top-ranked conditions. Each result updates the model for the next round.

    active learning