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The value of biomedical research--a $1.7 trillion annual investment--is ultimately determined by its downstream, real-world impact. Current objective predictors of impact rest on proxy, reductive metrics of dissemination, such as paper…
Numerous challenges persist that delay clinical interpretation of human genetic variants, to name a few: (1) un- structured PubMed articles are the most abundant source of evidence, yet their variant annotations are difficult to query…
Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug…
Clinical and biomedical research in low-resource settings often faces significant challenges due to the need for high-quality data with sufficient sample sizes to construct effective models. These constraints hinder robust model training…
Understanding the interaction between different drugs (drug-drug interaction or DDI) is critical for ensuring patient safety and optimizing therapeutic outcomes. Existing DDI datasets primarily focus on textual information, overlooking…
We develop a method for assigning high-quality labels to unstructured text. This method is based on fine-tuning an efficient, open-source language model with data extracted from a large, proprietary language model. We apply this method to…
In recent decades, traditional drug research and development have been facing challenges such as high cost, long timelines, and high risks. To address these issues, many computational approaches have been suggested for predicting the…
There is a widespread perception that pharmaceutical R&D is facing a productivity crisis characterised by stagnation in the numbers of new drug approvals in the face of increasing R&D costs. This study explores pharmaceutical R&D dynamics…
Due to their excellent drug-like and pharmacokinetic properties, small molecule drugs are widely used to treat various diseases, making them a critical component of drug discovery. In recent years, with the rapid development of deep…
Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in a wide range of applications, such as virtual screening and drug design. In this survey, we first…
Transdisciplinary research, the co-creation of scientific knowledge by multiple stakeholders, is considered essential for addressing major societal problems. Research policy makers and academic leaders frequently call for closer…
It is well known that individuals who abuse drugs usually use more than one substance. Toxic consequences of single and multiple drug use are well documented in the Treatment Episodes Data Set that lists combinations that result in hospital…
Frontier large language models generate clinically accurate outputs, but their citations are often fabricated. We term this the Provenance Gap. We tested five frontier LLMs across 36 clinician-validated scenarios for three rare…
The Web is a potentially huge source of medical drug leads. But despite the significant amount of multi- dimensional information about drugs, currently commercial search engines accept only linear keyword strings as inputs. This work uses…
Clinical trials offer a fundamental opportunity to discover new treatments and advance the medical knowledge. However, the uncertainty of the outcome of a trial can lead to unforeseen costs and setbacks. In this study, we propose a new…
Introduction: Even in effectively conducted randomised trials, the probability of a successful study remains relatively low. With recent advances in the next-generation sequencing technologies, there is a rapidly growing number of…
Combination drug therapies hold significant promise for enhancing treatment efficacy, particularly in fields such as oncology, immunotherapy, and infectious diseases. However, designing clinical trials for these regimens poses unique…
Drug-Drug Interactions (DDIs) significantly influence therapeutic efficacy and patient safety. As experimental discovery is resource-intensive and time-consuming, efficient computational methodologies have become essential. The predominant…
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…
Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction. However, this fast development inevitably made…