Target validation

Area: definition. Apply the shared measurement protocol.

Estimated progress toward AI-managed drug discovery

25% complete — judgment-based estimate as of November 7, 2026. Confidence: low. The scale starts at 0% before ChatGPT's public release in 2022 and ends at 100% when AI manages the field's continuing advancement without human participation. This rates the outcome's contribution to that transition, not the percentage of tasks solved, a measured fraction of the field, or time remaining.

The shared scoring rubric uses five equally weighted dimensions: execution, breadth, agenda-setting, validation, and sustained autonomous improvement. Points for this outcome are 10 + 5 + 5 + 5 + 0 = 25/100, in that order; each dimension has a maximum of 20. These component judgments make the estimate inspectable, but are not independently measured scores.

Reason for the estimate: Disease-relevant target evidence supports useful selection, while outside replication rates and autonomous experimental validation remain incomplete. The dated baseline below supplies the findings and source links used for this assessment. Unmeasured components receive no demonstrated-progress credit; missing evidence does not prove a missing capability.

What would move the score: Publish the full target queue and experimental failures; require outside replication in disease-relevant systems before counting validated targets. Higher autonomy scores additionally require AI to select worthwhile work, validate results and improve the process over repeated cycles without human research management, repair or adjudication. Lower the score if replication fails, quality regresses or previously hidden human assistance is found. Preserve this dated estimate when adding future assessments.

Measurement and acceptance rule

Proposed targets with independently reproduced disease-relevant experimental support / all targets selected for validation.

Evidence requirements

Document exactly where AI contributed: target choice, molecular design, experiments, or trial operations. Compare matched programs and control for indication, modality, starting maturity, and selection bias. Use registered trial protocols, full outcome reporting, and independent analysis. Docking scores, animal responses, and biomarker changes must not be presented as established patient benefit.

Related milestones

See the area's proposed milestones. Proposed targets are distinct from the dated evidence below.

Baseline as of November 7 2026

Status: Disease-relevant target evidence; independent hit rate unknown. Sources accessed November 7, 2026. This is a documentary baseline, not an independent replication or an assertion of the worldwide best result. An unknown measurement is not zero.

Dated findings

2024 publication: The TNIK study connects AI target selection to experimental antifibrotic evidence. TNIK inhibitor discovery, Nature Biotechnology.

Interpretation and tracking value

This is evidence for one target, not independently reproduced targets/all selected targets. Clinical activity also cannot by itself isolate the target-discovery method's contribution.

Next comparable measurement

Publish the full target queue and experimental failures; require outside replication in disease-relevant systems before counting validated targets. Preserve this dated observation; append future results with their model/system version, denominator, budget and evidence level rather than overwriting it.