Clinical trial outcome prediction seeks to estimate the likelihood that a
clinical trial will successfully reach its intended endpoint. This process
predominantly involves the development of machine learning models that utilize
a variety of data sources such as descriptions of the clinical trials,
characteristics of the drug molecules, and specific disease conditions being
targeted. Accurate predictions of trial outcomes are crucial for optimizing
trial planning and prioritizing investments in a drug portfolio. While previous
research has largely concentrated on small-molecule drugs, there is a growing
need to focus on biologics-a rapidly expanding category of therapeutic agents
that often lack the well-defined molecular properties associated with
traditional drugs. Additionally, applying conventional methods like graph
neural networks to biologics data proves challenging due to their complex
nature. To address these challenges, we introduce the Language Interaction
Network (LINT), a novel approach that predicts trial outcomes using only the
free-text descriptions of the trials. We have rigorously tested the
effectiveness of LINT across three phases of clinical trials, where it achieved
ROC-AUC scores of 0.770, 0.740, and 0.748 for phases I, II, and III,
respectively, specifically concerning trials involving biologic interventions.

临床试验结果预测利用机器学习模型根据试验描述、药物特性和疾病条件等多种数据源，预测试验成功达到预期终点的可能性。我们提出了一种名为语言交互网络（LINT）的新方法，只使用试验的文本描述来预测试验结果，在临床三个阶段的试验中，它实现了分别为 0.770、0.740 和 0.748 的 ROC-AUC 分数，特别针对涉及生物制剂的试验。