用多模态专家注意力预测临床试验成败，无需实验数据
MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome Prediction
- 设计模式专家模块，融合药物、疾病、入组标准等多模态数据
- 在TOP基准上提升最高11.3%的F1分数，优于HINT模型
- 轻量级结构避免人为偏见，适合药物研发早期决策
临床试验是评估药物疗效与安全性的金标准。由于药物分子设计空间巨大、成本高昂且周期长达数年，临床试验结果预测研究日益受到关注。准确预测需整合药物分子、靶向疾病和入组标准等多模态数据以推断成功或失败。现有深度学习方法如HINT通常依赖合成分子的湿实验数据或先验知识构建交互编码，存在局限性。为此，我们提出轻量级注意力模型MEXA-CTP，通过称为“模式专家”的专用模块整合易获取的多模态数据，生成有效表征，同时避免人工设计带来的偏见。采用柯西损失优化跨模态交互。在临床试验结果预测（TOP）基准上的实验表明，MEXA-CTP相比HINT模型，在F1分数上最多提升11.3%，PR-AUC提升12.2%，ROC-AUC提升2.5%。消融实验验证了各组件的有效性。
原文摘要 · Abstract (English)
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.
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