G raph T ranslate: Predicting Clinical Trial Translation using Graph Neural Networks on Biomedical Literature (2025)

Abstract: The translation of basic science into clinical interventions represents a critical yet prolonged pathway in biomedical research, with significant implications for human health. While previous translation prediction approaches have focused on citation-based and metadata metrics or semantic analysis, the complex network structure of scientific knowledge remains under-explored. In this work, we present a novel graph neural network approach that leverages both semantic and structural information to predict which research publications will lead to clinical trials. Our model analyses a comprehensive dataset of 19 million publication nodes, using transformer-based title and abstract sentence embeddings within their citation network context. We demonstrate that our graph-based architecture, which employs attention mechanisms over local citation neighbourhoods, outperforms traditional convolutional approaches by effectively capturing knowledge flow patterns (F1 improvement of 4.5 and 3.5 percentage points for direct and indirect translation). Our metadata is carefully selected to eliminate potential biases from researcher-specific information, while maintaining predictive power through network structural features. Notably, our model achieves state-of-the-art performance using only content-based features, showing that language inherently captures many of the predictive features of translation. Through rigorous validation on a held-out time window (2021), we demonstrate generalisation across different biomedical domains and provide insights into early indicators of translational research potential. Our system offers immediate practical value for research funders, enabling evidence-based assessment of translational potential during grant review processes.

## introduction the path from scientific discovery to clinical application remains a critical challenge in biomedical research. although laboratory research and preclinical studies can lead to advances in scientific understanding, translating these findings into realworld clinical interventions that directly benefit patients is a complex process involving multiple stages, including experimental validation, regulatory approval, and clinical trials, each of which introduces uncertainty and challenges @xcite . moreover, despite substantial global investment in medical research and development, only a tiny fraction of basic research findings successfully translate into clinical treatments @xcite . this inefficiency in the translation pipeline, combined with the decades-long timeframe typically required for bench-to-bedside translation @xcite , creates an urgent need for tools that can identify promising translational research early in its lifecycle. previous approaches to predicting translational success have primarily relied on citation patterns and metadata features, or focused solely on semantic analysis of research content @xcite . while these methods have shown promise, they often overlook the complex network of scientific knowledge through which research findings propagate towards clinical applications. citation networks represent more than just academic impact-they capture the flow of knowledge from fundamental discoveries towards clinical implementation. however, effectively modelling these knowledge transmission pathways requires both understanding the semantic content of research and its structural position within the broader scientific landscape. we address this challenge by introducing a graph neural network architecture that integrates both semantic and structural information from research publications. by analysing a comprehensive dataset of 19 million publications using transformer-based embeddings within their citation network context, our model captures subtle patterns in how knowledge flows from basic science towards clinical applications. crucially, we demonstrate that content-based features inherently encode many signals predictive of translational potential, allowing us to achieve state-of-the-art performance while minimising reliance on potentially biased metadata features. as a result, this approach offers practical value in identifying promising translational research early, helping researchers, funders, and institutions prioritise high-impact projects. ## related work recent academic enquiry has focused on predicting the relationship between a paper and its translational outcomes via citation analysis. hutchins et al. discovered a complex relationship between a paper's content, its citing articles, and citation rates, affecting its likelihood of being cited in clinical articles @xcite . the study used human-annotated medical subject headings (mesh) and 22 features in a random forest model to predict translational success. the model achieved good accuracy with just two years of data, and the authors showed diminishing improvement when more years of data were added. this is crucial because early identification facilitates translation prediction within the timeframe of a grant. the predictive power of mesh tags is attributed to its identification of the clinical stage a paper lies @xcite . the use of mesh terms, however, is limiting, as it requires extensive human labelling. as an alternative, modern natural language processing methods can also identify the translational stage of a paper @xcite . full-mlp-cnn model predicted patent citations (@xmath0.915) and guidelines and policy documents (@xmath1.918) without mesh terms @xcite . this model used sentence embeddings of the title and abstract alongside extensive metadata from the microsoft academic graph (mag). this included microsoft's ranking features based on eigencentrality. these features assess the scientific network surrounding entities such as papers, journals, or authors, with the rank of the paper emerging as the most influential metadata feature. the full-mlp-cnn study did not address how the age of the paper affects the accuracy of the prediction. this is important as, when time limited, more complex network measures can perform poorly compared to citation counts @xcite . however, microsoft explicitly sought to mitigate this age bias in their entity centrality metrics via reinforcement learning @xcite . in fact, a time-balanced network centrality measure has been shown to be more effective than simple citation counts in identifying nobel prize winning papers even in the first few years @xcite . this indicates that even a time-limited citation network structure contains valuable information for translation prediction. the delphi model is a clear example of this @xcite . it combined article and journal metadata alongside a time-limited citation network to predict 5-year post-publication time-balanced network centrality using only 2 years of data, while identifying seminal biotechnology papers. nelson et al. found that removing citation metadata features had a moderate reduction in predictive performance. this is supported by @xcite , who expanded on the nih study using a total of 91 citation and mesh based features to predict the clinical citation count of papers @xcite . the authors found the expanded citation network from paper references were more influential than those of the predicted paper or its early citations (xin li, xuli tang and qukai cheng, 2022). beinat et al. meanwhile, showed within the fields of dementia and cancer, translation could be predicted without any citation network data @xcite . they used an array of article metadata features alongside title and abstract embeddings in a catboost model to predict patent (@xmath2.84) and clinical trial citations (@xmath3.81) for dementia research. removing citation features is attractive, as it allows for translation prediction without any time delay. however, models from @xcite an array of researcher features in their models, raising concerns about increased model bias. while author popularity may relate to past translational success, it is difficult to separate this from potential structural biases when predicting future success. we argue that assessing a paper's translational potential should not consider personal researchers attributes such as h-index, institution or country. evidence suggests that both paper content and a time-limited scientific network structure around papers can be used to effectively predict biomedical translation. however, no study to date has successfully integrated both elements. our graph neural network approach combines paper embeddings alongside a time-limited scientific network structure to achieve this. the final model successfully predicts translational impact without depending on extensive feature engineering (mesh), a now discontinued service (mag), and minimises bias by excluding sensitive author features. ## experiments to evaluate the efficacy of our graph-based approach for predicting research translation into clinical trials, we conducted four sets of experiments designed to test key hypotheses about model performance, feature importance, early detection capabilities and comparison to previous literature.