Advancing Drug Discovery Through AI-Powered Solutions

Kinase Foundation ModelNew!

Two models, one question each. Give the first a kinase sequence and two compounds and it says which binds more tightly. Give the second one compound and two kinases and it says which kinase the compound prefers, the selectivity question. Both read the kinase’s amino-acid sequence itself, with no structure, no docking and no binding-site definition, and both are trained on the Kinase Knowledgebase.

Tested on ChEMBL measurements the models never saw: 0.690 over 1,836,100 held-out potency comparisons across 477 targets, and 75.3% over 3,137,588 selectivity comparisons. Acting only on the confident calls raises those to 0.842 and 92.3%. Version 1, the original scorer across 478 kinases, remains published alongside them.

Kinase Knowledgebase (KKB)

Currently the Kinase Knowledgebase Q2 2026 Release includes the following data:

• Journal articles and patents: 10,326
• Number of Biological Activity Data Points: 3,336,903
• Number of unique kinase molecules with annotated assay data: 509,691
• Number of all unique kinase molecules from patents and articles (with or without bio-activity data): 854,437
• Number of unique kinase targets with assay data: 579
• Number of annotated assay protocols: 105,916
• Machine-learning models built from this release’s SAR: activity classifiers across 392 kinase targets, median ROC-AUC 0.91 (0.95 for well-studied kinases) – view model performance report

To show what this depth of curation supports, we build machine-learning models from the KKB SAR and report how they perform: an activity classifier for each of 392 kinase targets and a potency regressor for 319 of them. The panel at left summarises classifier accuracy: ROC curves grouped by how much data each kinase has, the spread of ROC-AUC, and how accuracy rises with the depth of measured chemistry.

Accuracy is highest for compounds chemically related to what a target already has in KKB and declines for novel scaffolds, so every prediction is reported with a similarity score against the model’s own training set. Performance is also measured against published data absent from the knowledgebase.

Oncology Knowledgebase (OKB)

Currently the Oncology Knowledgebase Q2 2023 Release includes the following data:

• Journal articles and patents: 977
• Number of Biological Activity Data Points: 146,206
• Number of molecules from patents and articles: 66,198
• Number of unique oncology targets with assay data: 1,158
• Number of annotated assay protocols: 5,614
• Number of disease models: 137

Dataset Overlap AnalysisNew

How much of a dataset do you already have? Our toolkit answers that for any two SAR datasets, in any target family, without either side revealing a structure. Each party encodes its own data locally into irreversible SHA-256 fingerprints; only fingerprints are compared, and only the overlap figure is shared.

Target Informatics Platform (TIP™)

The Target Informatics Platform's content grows weekly with more than 200K high resolution protein structures, ~1.2M annotated co-complex sites, and an additional ~875K predicted sites. TIP contains more than 710K protein chains representing every major drug target family.