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Lilly TuneLab review

#30 of 30 in AI Drug Discovery Software

Predict small-molecule and antibody properties through a web-based platform.

5.3/10Editor score
Lilly TuneLab5.3 Contact TuneLab

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Lilly TuneLab is a cloud-based web platform for biotechnology teams working in early and preclinical drug discovery. Researchers can submit SMILES strings for small molecules or light- and heavy-chain antibody sequences, create jobs, track progress, and explore completed results interactively. Its scope is predictive characterization: the platform estimates small-molecule drug-like properties and ADMET profiles and assesses antibody developability. Teams seeking target identification, computational molecule design, or drug repurposing should look for a broader discovery tool.

The model offering spans small-molecule and antibody workflows, with models trained on more than 500,000 preclinical datapoints. TuneLab also uses federated learning: participating companies contribute training data while model updates, rather than underlying molecular data, are shared. That arrangement is relevant to organizations interested in collaborative model training without sharing their molecular datasets. It does not change the product's central focus on predicting properties rather than identifying targets or designing molecules.

Access follows a join-and-contribute-data membership process, beginning with a conversation and fit assessment with the TuneLab team. The vendor does not publish plan pricing; prospective participants therefore need to discuss access directly. This process may suit small and mid-market biotechnology companies prepared to contribute data and focused on molecule-level prediction. Teams that need a self-directed purchase path, or a platform covering a wider set of discovery stages, should consider alternatives.

Lilly TuneLab pros and cons

  • Where it wins
    • Predicts small-molecule drug-like properties and ADMET profiles
    • Assesses antibody developability from light- and heavy-chain sequences
    • Federated learning shares model updates without underlying molecular data
  • Where it doesn't
    • Focuses on predictive characterization, not target identification
    • Does not provide computational molecule design or drug repurposing
    • Access requires a conversation and fit assessment with the TuneLab team

Lilly TuneLab fact sheet, pricing and score →

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