AI Model Predicts Which Leukemia Drugs Will Work for Individual Patients

Acute myeloid leukemia has long frustrated oncologists precisely because it refuses to behave like a single disease. Two patients with seemingly similar diagnoses can respond in opposite ways to the same drug, and clinicians have had few reliable tools to anticipate which therapy will work before committing a patient to a course of treatment. A new machine learning framework called PREDICT-AML, described in the Journal of Translational Medicine, aims to change that by predicting how an individual patient’s leukemia will respond to a wide panel of drugs using nothing more than the patient’s clinical, genomic, and transcriptomic profiles.

The study, led by Mohammed Al-Ani of Hamad Bin Khalifa University in Qatar together with Siddhi P. Jani, Halima Bensmail, and Raghvendra Mall, builds on one of the most valuable datasets in leukemia research: the BeatAML cohort. That resource contains specimens from 805 patients with acute myeloid leukemia, each profiled for drug sensitivity across 165 different compounds in laboratory assays, alongside rich clinical annotations and both DNA and RNA sequencing data. Rather than treating this trove as a static catalog, the researchers turned it into training material for a model that learns how the molecular identity of a drug interacts with the cellular traits of a patient’s cancer.

What makes PREDICT-AML technically distinctive is the way it represents both sides of the drug-patient equation. On the drug side, the framework employs four complementary representations: classical physicochemical descriptors that capture properties such as molecular weight and lipophilicity; a novel set of knowledge-distilled embeddings, abbreviated KD-Embed, which compress what the model has learned about drug behavior into compact numerical vectors; and embeddings generated by two chemical language models, ChemBERTa and MolFormer, which are transformer architectures trained to read molecular structures the way language models read text. These representations allow the model to reason about drugs it has effectively seen in training data even when their chemical details differ substantially.

On the patient side, the model integrates several novel features that go beyond a simple list of mutations. The researchers engineered patient-specific oncogenic pathway proximity scores, which quantify how close a patient’s tumor expression profile sits to known cancer-driving signaling pathways. They also computed cell-state enrichment scores, which describe whether the leukemia cells resemble particular normal cell types, such as monocyte-like states, and they incorporated the patient’s genomic profile of somatic mutations. This combination of features lets the model capture the biological context in which a drug must act, rather than treating each patient as a bag of independent genetic alterations.

For the predictive engine itself, the team systematically searched for the best architecture using Optuna, a hyperparameter optimization framework, and identified TabPFN, a tabular foundation model, as the optimal choice. TabPFN represents a relatively new class of models designed specifically for the kind of structured, tabular data that dominates biomedical research, and its selection here underscores how foundation-model approaches are migrating from images and text into clinical prediction. When paired with the knowledge-distilled drug embeddings, TabPFN achieved a Pearson correlation of 0.690 between predicted and observed drug responses on a held-out test set, with a mean absolute error of 37.712.

One of the study’s most important methodological findings concerns how such models should be evaluated. The researchers found that random cross-validation, a common practice in machine learning studies, overestimates performance by approximately 13 percent relative to patient-stratified cross-validation, in which the model is tested on patients it has never encountered during training. Because the clinical goal is to predict responses for new patients rather than to memorize existing ones, the authors argue that patient-stratified evaluation is the appropriate benchmark. The distinction matters enormously for translational research: models that look impressive under random splits may fail when deployed on the next patient who walks through the clinic door.

The team did not stop at internal validation. They tested PREDICT-AML on two completely independent external datasets, LeeAML and FIMM-AML, generated at other institutions. The model achieved a Pearson correlation of 0.597 on LeeAML and an absolute correlation of 0.557 on FIMM-AML, confirming that its predictions generalize across patient populations, assay platforms, and clinical settings. In head-to-head comparisons, PREDICT-AML clearly outperformed established baselines: ElasticNet, a regularized regression method, managed only a correlation of 0.360, while MDREAM reached a Spearman correlation of 0.680. These results suggest that the combination of rich drug representations, multi-omics patient features, and the TabPFN architecture captures signal that simpler approaches miss.

Ablation experiments, in which the researchers systematically removed categories of input features, yielded biological insights of their own. Gene expression emerged as the single dominant predictor of drug response, reflecting the fact that a cell’s transcriptional state largely determines its vulnerability to any given compound. Perhaps more striking, the combination of somatic mutations with cell-state enrichment alone matched the performance of the full multi-omics feature set, hinting that a leaner panel of measurements could someday deliver comparable predictive power at lower cost. The knowledge-distilled embeddings proved to be the primary drug-identity signal, confirming the value of learning compact drug representations from large sensitivity datasets.

Interpretability analysis using SHAP values, a technique that quantifies each feature’s contribution to individual predictions, revealed recurring biological determinants of drug sensitivity across multiple compounds. Monocyte-like cell-state enrichment, expression of the BCL6 gene, and activity of the immunogenic cell death pathway all appeared repeatedly as markers of sensitivity. These findings are more than statistical curiosities; they point toward biological mechanisms that could be exploited therapeutically and suggest that the model is learning genuine biology rather than spurious correlations. For clinicians, such interpretability is essential before any predictive tool can inform treatment decisions.

The authors position PREDICT-AML as an interpretable, externally validated tool for individualized therapeutic prioritization in acute myeloid leukemia, a disease with limited therapeutic options and pressing clinical need. While the model’s predictions would still require prospective clinical validation before guiding real-world prescribing, the framework demonstrates that integrating chemical language models, tabular foundation models, and carefully engineered biological features can produce drug response predictions that hold up across institutions. As datasets like BeatAML continue to grow and similar resources accumulate for other cancers, the approach pioneered here may mark a step toward a future where a leukemia patient’s treatment is selected by an algorithm that has already seen, in molecular detail, how thousands of tumors responded to hundreds of drugs.

Subject of Research: Machine learning prediction of personalized drug response in acute myeloid leukemia using multi-omics data

Article Title: PREDICT-AML: personalized response via drug interaction with cellular traits for acute myeloid leukemia

Article References: Al-Ani, M., Jani, S. P., Bensmail, H., & Mall, R. (2026). PREDICT-AML: personalized response via drug interaction with cellular traits for acute myeloid leukemia. Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-08817-4

Image Credits: AI Generated

DOI: 10.1186/s12967-026-08817-4

Keywords: acute myeloid leukemia, PREDICT-AML, machine learning, TabPFN, drug response prediction, multi-omics, BeatAML, chemical language models, precision oncology, SHAP interpretability, external validation, transcriptomics