AIID

AI-Integrated Drug Discovery

Shorten Discovery Cycles with A Closed-Loop, AI-Enhanced Platform.

We fuse multimodal models (LLMs, GNNs, diffusion) with simulation, lab automation, and active learning to optimize potency, selectivity, and ADMET from target to candidate.

Talk to us How it works
De-novo generation QSAR/GNN Docking/MD PK/PD
active-learning.py 
# pseudo-loop
for cycle in range(1, N):
    designs   = generator.propose(objectives)
    selected  = bayes_opt.rank(designs, data)
    results   = lab.run(selected)
    data     += analyze(results)
    model     = retrain(data)

Cycle time

−42%

AI-proposed synthesized

68%

Data layer

Unified ingest of ELN/LIMS, omics, structures, HTS—cleaned and versioned.

Model layer

Foundation/LLMs, QSAR/GNNs, docking/MD, de-novo generators, PK/PD.

Orchestration

Active learning & multi-objective optimization to pick next-best experiments.

Execution

Automated synthesis/assay; standardized QC. Results loop back to retrain models.

How it works

  1. Define objective & metrics.
  2. Generate & prioritize with AI + domain constraints.
  3. Automate experiments, analyze, and retrain.

Ready to explore a pilot?

Email hello@aiidlabs.com and we’ll respond within 1 business day.

Book a demo