Can artificial intelligence predict clinical trial outcomes?

The increasing complexity and cost of clinical trials, particularly in the context of oncology and advanced therapies, pose significant challenges for drug development. This study evaluates the predictive capabilities of large language models (LLMs) such as GPT-3.5, GPT-4, and HINT in determining clinical trial outcomes. By leveraging a curated dataset of trials from ClinicalTrials.gov, we compare the models' performance using metrics including balanced accuracy, specificity, recall, and Matthews Correlation Coefficient (MCC). Results indicate that GPT-4o demonstrates robust performance in early trial phases, achieving high recall but facing limitations in specificity. Conversely, the HINT model excels in recognizing negative outcomes, particularly in later trial phases, offering a balanced approach across diverse endpoints. Oncology trials, characterized by high complexity, remain challenging for all models. Additionally, trial duration and disease categories influence predictive performance, with longer durations and complex diseases such as neoplasms reducing accuracy. This study highlights the complementary strengths of LLMs and HINT, providing insights into optimizing predictive tools for clinical trial design and risk management. Future advancements in LLMs are essential to address current gaps in handling negative outcomes and complex domains.

A deep graph model for the signed interaction prediction in biological network

In pharmaceutical research, the strategy of drug repurposing accelerates the development of new therapies while reducing R&D costs. Network pharmacology lays the theoretical groundwork for identifying new drug indications, and deep graph models have become essential for their precision in mapping complex biological networks. Our study introduces an advanced graph model that utilizes graph convolutional networks and tensor decomposition to effectively predict signed chemical-gene interactions. This model demonstrates superior predictive performance, especially in handling the polar relations in biological networks. Our research opens new avenues for drug discovery and repurposing, especially in understanding the mechanism of actions of drugs.