  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.
