Products
Find the right model or tool for your drug discovery work.
AI/ML models and tools available now on TuneLab
AbLab v1, v2
Predicts antibody developability across 22 endpoints, including stability, aggregation, and solubility, from sequence alone.
Antibody-Antigen Binding Affinity (ΔΔG)
TuneLab’s Antibody–Antigen Binding Affinity model predicts how a specific mutation changes binding affinity between an antibody and its antigen.
ChemLab v1, v2 Federated
Predicts ADMET and physicochemical properties to help prioritize compounds for synthesis.
Immunogenicity Model (MHC-II Peptide Presentation)
Predicts MHC class II presentation likelihood and 9-mer binding cores for antibody peptides across 12 HLA-DRB1 alleles.
in vivo PK (developed by insitro)
Predicts preclinical in vivo PK, including clearance and Vss, across multi species, developed by insitro for Lilly.
NanoLab (VHH Developability)
Predicts five biophysical developability endpoints for VHH nanobody sequences to rank leads before wet lab testing.
Small Molecule Computational Workflows
Configurable workflows that generate, filter, score, predict properties and rank small molecules to automate drug discovery campaigns.
The in vivo PK model developed for Lilly by insitro predicts preclinical intravenous pharmacokinetics for small molecules directly from chemical structure — plasma clearance, mean residence time, and volume of distribution at steady state, each across mouse, rat, dog and monkey.
It was developed by insitro for Lilly trained on Lilly's proprietary in vivo pharmacokinetic studies alongside a much larger corpus of Lilly in vitro ADME and physicochemical data. It is built for scientists who need to prioritize compounds on likely in vivo behavior before committing to animal studies.
Plasma clearance, mean residence time, and volume of distribution at steady state, in each of mouse, rat, dog and monkey.
A cross-species view in a single run, so a compound's predicted profile can be read across all four preclinical species together.
A shared molecular representation learned through joint multitask training on both from Lilly's much larger in vitro ADME and physicochemical collections and the in vivo PK endpoints. In vivo pharmacokinetic data is scarce by nature, and this is what allows the model to generalize beyond the compounds with in vivo PK data.