CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMs

Many existing models for clinical trial outcome prediction are optimized using task-specific loss functions on trial phase-specific data.While this scheme may boost prediction for common diseases and drugs, it can hinder the learning of generalizable representations, leading to more false positives/negatives.To address this limitation, we introduce CLaDMoP, a new pre-training approach for clinical trial outcome prediction, alongside the Successful Clinical Trials dataset (SCT), specifically designed for this task.CLaDMoP leverages a Large Language Model-to encode trials' eligibility criteria-linked to a lightweight Drug-Molecule branch through a novel multi-level fusion technique.To efficiently fuse long embeddings across levels, we incorporate a grouping block, drastically reducing computational overhead.CLaDMoP avoids reliance on task-specific objectives by pre-training on a "pair matching" proxy task.Compared to established zero-shot and few-shot baselines, our method significantly improves both PR-AUC and ROC-AUC, especially for phase I and phase II trials.We further evaluate and perform ablation on CLaDMoP after Parameter-Efficient Fine-Tuning, comparing it to state-of-the-art supervised baselines, including MEXA-CTP, on the Trial Outcome Prediction (TOP) benchmark.CLaDMoP achieves up to 10.5% improvement in PR-AUC and 3.6% in ROC-AUC, while attaining comparable F1 score to MEXA-CTP, highlighting its potential for clinical trial outcome prediction.Code and SCT dataset can be downloaded from https://github.com/murai-lab/CLaDMoP.