MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome Prediction
This addresses the problem of predicting clinical trial outcomes for researchers and pharmaceutical companies, offering a more efficient and unbiased approach, though it appears incremental as it builds on existing deep learning methods.
The paper tackles clinical trial outcome prediction by proposing MEXA-CTP, a lightweight attention-based model that integrates multi-modal data without requiring wet lab data or prior knowledge, achieving improvements of up to 11.3% in F1 score, 12.2% in PR-AUC, and 2.5% in ROC-AUC over existing methods.
Clinical trials are the gold standard for assessing the effectiveness and safety of drugs for treating diseases. Given the vast design space of drug molecules, elevated financial cost, and multi-year timeline of these trials, research on clinical trial outcome prediction has gained immense traction. Accurate predictions must leverage data of diverse modes such as drug molecules, target diseases, and eligibility criteria to infer successes and failures. Previous Deep Learning approaches for this task, such as HINT, often require wet lab data from synthesized molecules and/or rely on prior knowledge to encode interactions as part of the model architecture. To address these limitations, we propose a light-weight attention-based model, MEXA-CTP, to integrate readily-available multi-modal data and generate effective representations via specialized modules dubbed "mode experts", while avoiding human biases in model design. We optimize MEXA-CTP with the Cauchy loss to capture relevant interactions across modes. Our experiments on the Trial Outcome Prediction (TOP) benchmark demonstrate that MEXA-CTP improves upon existing approaches by, respectively, up to 11.3% in F1 score, 12.2% in PR-AUC, and 2.5% in ROC-AUC, compared to HINT. Ablation studies are provided to quantify the effectiveness of each component in our proposed method.