@inproceedings{zhang_cladmop_2025,
 abstract = {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 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.},
 author = {Zhang, Yiqing and Liu, Xiaozhong and Murai, Fabricio},
 booktitle = {ACM KDD 2025 (to appear)},
 doi = {10.48550/arXiv.2505.18527},
 file = {Preprint PDF:/Users/fmurai/Zotero/storage/9KUZ27F9/Zhang et al. - 2025 - CLaDMoP Learning Transferrable Models from Successful Clinical Trials via LLMs.pdf:application/pdf;Snapshot:/Users/fmurai/Zotero/storage/6WLWEEG4/2505.html:text/html},
 keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
 month = {May},
 note = {arXiv:2505.18527 [cs]},
 shorttitle = {CLaDMoP},
 title = {CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMs},
 url = {http://arxiv.org/abs/2505.18527},
 urldate = {2025-06-06},
 year = {2025}
}