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.
TuneLab’s Immunogenicity (MHC-II Peptide Presentation) model predicts which peptides from an antibody sequence are likely to be presented on MHC class II — the key upstream step in mounting an anti-drug antibody response. For any input sequence, it returns a presentation likelihood for each peptide across a panel of 12 common HLA-DRB1 alleles, together with the predicted 9-mer binding core.
It is trained on public data, combined with experimentally observed MAPPs peptides from Lilly proprietary therapeutic antibody data.¹⁻³ It is built for biologics engineers who need to rank candidate sequences on immunogenicity risk and pinpoint the specific residues worth re-engineering, before committing to wet lab studies.
Simultaneous peptide-presentation predictions across 12 HLA-DRB1 alleles, so a sequence can be assessed for broad population coverage.
Identification of the predicted 9-mer binding core within each presented peptide, localizing the liability to specific residues so de-immunization edits can be designed and re-scored.
- ¹(B. Reynisson et al., “NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data,” Nucleic Acids Research, vol. 48, no. W1, pp. W449–W454, 2020, doi: 10.1093/nar/gkaa379)
- ²(D. M. Tadros, S. Eggenschwiler, J. Racle, and D. Gfeller, “The MHC Motif Atlas: a database of MHC binding specificities and ligands,” Nucleic Acids Research, vol. 51, no. D1, pp. D428–D437, 2023, doi: 10.1093/nar/gkac965)
- ³(R. Vita et al., “The Immune Epitope Database (IEDB): 2018 update,” Nucleic Acids Research, vol. 47, no. D1, pp. D339–D343, 2019, doi: 10.1093/nar/gky1006.)
- ⁴Wen Y, Cahya S, Zeng W, et al. Development of a FRET-Based Assay for Analysis of mAbs Internalization and Processing by Dendritic Cells in Preclinical Immunogenicity Risk Assessment. The AAPS Journal 22, 68 (2020). doi:10.1208/s12248-020-00444-1