Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. Predicting the probability of side-effects, before their occurrence, is fundamental to reduce this impact, in particular on drug discovery. Candidate molecules could be screened before undergoing clinical trials, reducing the costs in time, money, and health of the participants. Drug side-effects are triggered by complex biological processes involving many different entities, from drug structures to protein-protein interactions. To predict their occurrence, it is necessary to integrate data from heterogeneous sources. In this work, such heterogeneous data is integrated into a graph dataset, expressively representing the relational information between different entities, such as drug molecules and genes. The relational nature of the dataset represents an important novelty for drug side-effect predictors. Graph Neural Networks (GNNs) are exploited to predict DSEs on our dataset with very promising results. GNNs are deep learning models that can process graph-structured data, with minimal information loss, and have been applied on a wide variety of biological tasks. Our experimental results confirm the advantage of using relationships between data entities, suggesting interesting future developments in this scope. The experimentation also shows the importance of specific subsets of data in determining associations between drugs and side-effects.
University of Florence（佛罗伦萨大学）；University of Pisa（比萨大学）；University of Siena（锡耶纳大学）
Predicting drug side-effects before they occur is a key task in keeping the number of drug-related hospitalizations low and to improve drug discovery processes. Automatic predictors of side-effects generally are not able to process the structure of the drug, resulting in a loss of information. Graph neural networks have seen great success in recent years, thanks to their ability of exploiting the information conveyed by the graph structure and labels. These models have been used in a wide variety of biological applications, among which the prediction of drug side-effects on a large knowledge g…
Seoul National University（首尔大学）；Seoul National University Hospital（首尔大学医院）
Adverse Drug Reaction (ADR) is a significant public health concern world-wide. Numerous graph-based methods have been applied to biomedical graphs for predicting ADRs in pre-marketing phases. ADR detection in post-market surveillance is no less important than pre-marketing assessment, and ADR detection with large-scale clinical data have attracted much attention in recent years. However, there are not many studies considering graph structures from clinical data for detecting an ADR signal, which is a pair of a prescription and a diagnosis that might be a potential ADR. In this study, we develo…
Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have tw…
University of Cambridge（剑桥大学）；Mila – Québec Artificial Intelligence Institute（Mila-魁北克人工智能研究所）；HEC Montréal（蒙特利尔高等商学院）
Complex or co-existing diseases are commonly treated using drug combinations, which can lead to higher risk of adverse side effects. The detection of polypharmacy side effects is usually done in Phase IV clinical trials, but there are still plenty which remain undiscovered when the drugs are put on the market. Such accidents have been affecting an increasing proportion of the population (15% in the US now) and it is thus of high interest to be able to predict the potential side effects as early as possible. Systematic combinatorial screening of possible drug-drug interactions (DDI) is challeng…
University Hospital of Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）
Background: Drug-drug interactions (DDIs) refer to processes triggered by the administration of two or more drugs leading to side effects beyond those observed when drugs are administered by themselves. Due to the massive number of possible drug pairs, it is nearly impossible to experimentally test all combinations and discover previously unobserved side effects. Therefore, machine learning based methods are being used to address this issue. Methods: We propose a Siamese self-attention multi-modal neural network for DDI prediction that integrates multiple drug similarity measures that have bee…
School of Computer Science and Technology, Xidian University（西安电子科技大学计算机科学与技术学院）
Motivation: Drug combination is a sensible strategy for disease treatment by improving the efficacy and reducing concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a plenty of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in performance and scalability. Results: In this paper, we proposed a Network Embedding framework in Multiplex Networks (NEMN) to predict synthetic drug combinations. Based on a multiplex drug similarity netw…
RCC Institute of Information Technology（RCC信息技术学院）；Aliah University（阿里亚大学）
In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we …
CongFu: Conditional Graph Fusion for Drug Synergy Prediction
University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）；The Hospital for Sick Children（病童医院）；University Health Network（大学健康网络）；Peter Munk Cardiac Centre, University Health Network（彼得·蒙克心脏中心，大学健康网络）
The use of multiple drugs accounts for almost 30% of all hospital admission and is the 5th leading cause of death in America. Since over 30% of all adverse drug events (ADEs) are thought to be caused by drug-drug interactions (DDI), better identification and prediction of administration of known DDIs in primary and secondary care could reduce the number of patients seeking urgent care in hospitals, resulting in substantial savings for health systems worldwide along with better public health. However, current DDI prediction models are prone to confounding biases along with either inaccurate or …
Utilizing AI and Social Media Analytics to Discover Adverse Side Effects of GLP-1 Receptor Agonists
The School of Business Administration（商学院）；Bar-Ilan University（巴伊兰大学）；Ben-Gurion University of the Negev（内盖夫本-古里安大学）；Questrom School of Business（奎斯特罗姆商学院）；Boston University（波士顿大学）
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；GlaxoSmithKline（葛兰素史克）；Frederick National Laboratory（弗雷德里克国家实验室）
One of the key requirements for incorporating machine learning into the drug discovery process is complete reproducibility and traceability of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing machine learning models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of machine learning and molecular featurization tools. We have benchmarked AMPL on a large collection of ph…
State-dependent Na+ channel blockers are often prescribed to treat cardiac arrhythmias, but many Na+ channel blockers are known to have pro-arrhythmic side effects. While the anti and proarrhythmic potential of a Na+ channel blocker is thought to depend on the characteristics of its rate-dependent block, the mechanisms linking these two attributes are unclear. Furthermore, how specific properties of rate-dependent block arise from the binding kinetics of a particular drug is poorly understood. Here, we examine the rate-dependent effects of the Na+ channel blocker lidocaine by constructing and …
