Probabilistic graph-based model uncovers previously unseen druggable vulnerabilities in major solid cancers
Bibliographic record
Abstract
Abstract Over half cancer patients lack safe, effective, targeted therapies despite abundant molecular profiling data. Statistically recurrent cancer drivers have provided fertile ground for drug discovery where they exist. But in rare, complex, and heterogeneous cancers, strong driver signals are elusive. Moreover, therapeutically exploitable molecular vulnerabilities extend beyond classical drivers. Here we describe a novel, integrative, generalizable graph-based, cooperativity-led Markov chain model, A 3 D 3 a’s MVP (Adaptive AI-Augmented Drug Discovery and Development Molecular Vulnerability Picker), to identify and prioritize key druggable molecular vulnerabilities in cancer. The algorithm exploits cooperativity of weak signals within a cancer molecular network to enhance the signal of true molecular vulnerabilities. We apply A 3 D 3 a’s MVP to 19 solid cancer types and demonstrate that it outperforms standard approaches for target hypothesis generation by >3-fold as benchmarked against cell line genetic perturbation and drug screening data. Importantly, we demonstrate its ability to identify non-driver druggable vulnerabilities and highlight 43 novel or emergent druggable targets for these tumors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".