Prediction Of Drug Side Effects Research Articles

Transforming Drug Discovery: The Impact of Artificial Intelligence and Machine Learning from Initial Screening to Clinical Trials

Abstract: The landscape of drug discovery is undergoing a profound transformation driven by the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This paper explores the impact of these advancements on various stages of the drug discovery process, from initial screening to clinical trials. Initially, AI and ML are revolutionizing the early stages of drug discovery by enhancing the efficiency of target identification and lead compound screening. Traditional methods, which often rely on labor-intensive processes and high costs, are being supplemented with AI-driven algorithms that analyze vast datasets to identify potential drug targets and predict the biological activity of compounds with unprecedented accuracy. In the drug design and optimization phase, ML models facilitate the prediction of drug interactions and side effects, thus accelerating the development of safer and more effective therapeutics. Advanced simulations and predictive models reduce the reliance on experimental trials, thereby streamlining the development pipeline. The clinical trials phase also benefits significantly from AI and ML. These technologies improve patient stratification by identifying suitable candidates based on genetic and clinical data, optimizing trial designs, and predicting patient responses to treatment. This not only enhances the efficiency of clinical trials but also increases the likelihood of successful outcome. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

PU‐GNN: A Positive‐Unlabeled Learning Method for Polypharmacy Side‐Effects Detection Based on Graph Neural Networks

The simultaneous use of multiple drugs, known as polypharmacy, heightens the risks of harmful side effects due to drug‐drug interactions. Predicting these interactions is crucial in drug research due to the rising prevalence of polypharmacy. Researchers employ a graphical structure to model these interactions, representing drugs and side effects as nodes and their interactions as edges. This creates a multipartite graph that encompasses various interactions such as protein‐protein interactions, drug‐target interactions, and side effects of polypharmacy. In this study, a method named PU‐GNN, based on graph neural networks, is introduced to predict drug side effects. The proposed method involves three main steps: (1) drug features extraction using a novel biclustering algorithm, (2) reducing uncertainity in input data using a positive‐unlabeled learning algorithm, and (3) prediction of drug’s polypharmacies by utilizing a graph neural network. Performance evaluation using 5‐fold cross‐validation reveals that PU‐GNN surpasses other methods, achieving high scores of 0.977, 0.96, and 0.949 in the AUPR, AUC, and F1 measures, respectively.

Chem2Side: A Deep Learning Model with Ensemble Augmentation (Conventional + Pix2Pix) for COVID-19 Drug Side-Effects Prediction from Chemical Images

Drug side effects (DSEs) or adverse drug reactions (ADRs) are a major concern in the healthcare industry, accounting for a significant number of annual deaths in Europe alone. Identifying and predicting DSEs early in the drug development process is crucial to mitigate their impact on public health and reduce the time and costs associated with drug development. Objective: In this study, our primary objective is to predict multiple drug side effects using 2D chemical structures, especially for COVID-19, departing from the conventional approach of relying on 1D chemical structures. We aim to develop a novel model for DSE prediction that leverages the CNN-based transfer learning architecture of ResNet152V2. Motivation: The motivation behind this research stems from the need to enhance the efficiency and accuracy of DSE prediction, enabling the pharmaceutical industry to identify potential drug candidates with fewer adverse effects. By utilizing 2D chemical structures and employing data augmentation techniques, we seek to revolutionize the field of drug side-effect prediction. Novelty: This study introduces several novel aspects. The proposed study is the first of its kind to use 2D chemical structures for predicting drug side effects, departing from the conventional 1D approaches. Secondly, we employ data augmentation with both conventional and diffusion-based models (Pix2Pix), a unique strategy in the field. These innovations set the stage for a more advanced and accurate approach to DSE prediction. Results: Our proposed model, named CHEM2SIDE, achieved an impressive average training accuracy of 0.78. Moreover, the average validation and test accuracy, precision, and recall were all at 0.73. When evaluated for COVID-19 drugs, our model exhibited an accuracy of 0.72, a precision of 0.79, a recall of 0.72, and an F1 score of 0.73. Comparative assessments against established transfer learning and machine learning models (VGG16, MobileNetV2, DenseNet121, and KNN) showcased the exceptional performance of CHEM2SIDE, marking a significant advancement in drug side-effect prediction. Conclusions: Our study introduces a groundbreaking approach to predicting drug side effects by using 2D chemical structures and incorporating data augmentation. The CHEM2SIDE model demonstrates remarkable accuracy and outperforms existing models, offering a promising solution to the challenges posed by DSEs in drug development. This research holds great potential for improving drug safety and reducing the associated time and costs.

MSDSE: Predicting drug-side effects based on multi-scale features and deep multi-structure neural network

Unexpected side effects may accompany the research stage and post-marketing of drugs. These accidents lead to drug development failure and even endanger patients' health. Thus, it is essential to recognize the unknown drug-side effects. Most existing methods in silico find the answer from the association network or similarity network of drugs while ignoring the drug-intrinsic attributes. The limitation is that they can only handle drugs in the maturation stage.To be suitable for early drug-side effect screening, we conceive a multi-structural deep learning framework, MSDSE, which synthetically considers the multi-scale features derived from the drug. MSDSE can jointly learn SMILES sequence-based word embedding, substructure-based molecular fingerprint, and chemical structure-based graph embedding. In the preprocessing stage of MSDSE, we project all features to the abstract space with the same dimension. MSDSE builds a bi-level channel strategy, including a convolutional neural network module with an Inception structure and a multi-head Self-Attention module, to learn and integrate multi-modal features from local to global perspectives. Finally, MSDSE regards the prediction of drug-side effects as pair-wise learning and outputs the pair-wise probability of drug-side effects through the inner product operation. MSDSE is evaluated and analyzed on benchmark datasets and performs optimally compared to other baseline models. We also set up the ablation study to explain the rationality of the feature approach and model structure. Moreover, we select model partial prediction results for the case study to reveal actual capability. The original data are available at http://github.com/yuliyi/MSDSE.

Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel.

Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions. Its purpose is to timely detect and control drug safety risks. Traditional methods are time-consuming. To accelerate the discovery of side effects, we propose a machine learning based method, called correntropy-loss based matrix factorization with neural tangent kernel (CLMF-NTK), to solve the prediction of drug side effects. Our method and other computational methods are tested on three benchmark datasets, and the results show that our method achieves the best predictive performance.

A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs.

Predicting drug side effects before they occur is a critical task for keeping the number of drug-related hospitalizations low and for improving 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 graph. Exploiting the molecular graph encoding the structure of the drug represents a novel approach, in which the problem is formulated as a multi-class multi-label graph-focused classification. We developed a methodology to carry out this task, using recurrent Graph Neural Networks, and building a dataset from freely accessible and well established data sources. The results show that our method has an improved classification capability, under many parameters and metrics, with respect to previously available predictors. The method is not ready for clinical tests yet, as the specificity is still below the preliminary 25% threshold. Future efforts will aim at improving this aspect.

DDI-SSL: Drug–Drug Interaction Prediction Based on Substructure Signature Learning

Drug–drug interactions (DDIs) are entities composed of different chemical substructures (functional groups). In existing methods that predict drug–drug interactions based on the usage of substructures, each node is perceived as the epicenter of a sub-pattern, and adjacent nodes eventually become centers of similar substructures, resulting in redundancy. Furthermore, the significant differences in structure and properties among compounds can lead to unrelated pairings, making it difficult to integrate information. This heterogeneity negatively affects the prediction results. In response to these challenges, we propose a drug–drug interaction prediction method based on substructure signature learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent collaborative attention mechanism to model the mutual influence between drugs, generating signal strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction compared to state-of-the-art methods.

BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs.

Knowledge graphs are an increasingly common data structure for representing biomedical information. These knowledge graphs can easily represent heterogeneous types of information, and many algorithms and tools exist for querying and analyzing graphs. Biomedical knowledge graphs have been used in a variety of applications, including drug repurposing, identification of drug targets, prediction of drug side effects, and clinical decision support. Typically, knowledge graphs are constructed by centralization and integration of data from multiple disparate sources. Here, we describe BioThings Explorer, an application that can query a virtual, federated knowledge graph derived from the aggregated information in a network of biomedical web services. BioThings Explorer leverages semantically precise annotations of the inputs and outputs for each resource, and automates the chaining of web service calls to execute multi-step graph queries. Because there is no large, centralized knowledge graph to maintain, BioThings Explorer is distributed as a lightweight application that dynamically retrieves information at query time. More information can be found at https://explorer.biothings.io and code is available at https://github.com/biothings/biothings_explorer.

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects.

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

Prediction of drug side effects with transductive matrix co-completion.

Side effects of drugs could cause severe health problems and the failure of drug development. Drug-target interactions are the basis for side effect production and are important for side effect prediction. However, the information on the known targets of drugs is incomplete. Furthermore, there could be also some missing data in the existing side effect profile of drugs. As a result, new methods are needed to deal with the missing features and missing labels in the problem of side effect prediction. We propose a novel computational method based on transductive matrix co-completion and leverage the low-rank structure in the side effects and drug-target data. Positive-unlabelled learning is incorporated into the model to handle the impact of unobserved data. We also introduce graph regularization to integrate the drug chemical information for side effect prediction. We collect the data on side effects, drug targets, drug-associated proteins and drug chemical structures to train our model and test its performance for side effect prediction. The experiment results show that our method outperforms several other state-of-the-art methods under different scenarios. The case study and additional analysis illustrate that the proposed method could not only predict the side effects of drugs but also could infer the missing targets of drugs. The data and the code for the proposed method are available at https://github.com/LiangXujun/GTMCC. Supplementary data are available at Bioinformatics online.

CIAE: class imbalance aware ensemble framework to predict drug side effects

CIAE: class imbalance aware ensemble framework to predict drug side effects

DeepSide: A Deep Learning Approach for Drug Side Effect Prediction.

Drug failures due to unforeseen adverse effects at clinical trials pose health risks for the participants and lead to substantial financial losses. Side effect prediction algorithms have the potential to guide the drug design process. LINCS L1000 dataset provides a vast resource of cell line gene expression data perturbed by different drugs and creates a knowledge base for context specific features. The state-of-the-art approach that aims at using context specific information relies on only the high-quality experiments in LINCS L1000 and discards a large portion of the experiments. In this study, our goal is to boost the prediction performance by utilizing this data to its full extent. We experiment with 5 deep learning architectures. We find that a multi-modal architecture produces the best predictive performance among multi-layer perceptron-based architectures when drug chemical structure (CS), and the full set of drug perturbed gene expression profiles (GEX) are used as modalities. Overall, we observe that the CS is more informative than the GEX. A convolutional neural network-based model that uses only SMILES string representation of the drugs achieves the best results and provides 13.0% macro-AUC and 3.1% micro-AUC improvements over the state-of-the-art. We also show that the model is able to predict side effect-drug pairs that are reported in the literature but was missing in the ground truth side effect dataset. DeepSide is available at http://github.com/OnurUner/DeepSide.

Drug Side Effect Prediction with Deep Learning Molecular Embedding in a Graph-of-Graphs Domain

Drug side effects (DSEs), or adverse drug reactions (ADRs), constitute an important health risk, given the approximately 197,000 annual DSE deaths in Europe alone. Therefore, during the drug development process, DSE detection is of utmost importance, and the occurrence of ADRs prevents many candidate molecules from going through clinical trials. Thus, early prediction of DSEs has the potential to massively reduce drug development times and costs. In this work, data are represented in a non-euclidean manner, in the form of a graph-of-graphs domain. In such a domain, structures of molecule are represented by molecular graphs, each of which becomes a node in the higher-level graph. In the latter, nodes stand for drugs and genes, and arcs represent their relationships. This relational nature represents an important novelty for the DSE prediction task, and it is directly used during the prediction. For this purpose, the MolecularGNN model is proposed. This new classifier is based on graph neural networks, a connectionist model capable of processing data in the form of graphs. The approach represents an improvement over a previous method, called DruGNN, as it is also capable of extracting information from the graph-based molecular structures, producing a task-based neural fingerprint (NF) of the molecule which is adapted to the specific task. The architecture has been compared with other GNN models in terms of performance, showing that the proposed approach is very promising.

Learning temporal difference embeddings for biomedical hypothesis generation.

Hypothesis generation (HG) refers to the discovery of meaningful implicit connections between disjoint scientific terms, which is of great significance for drug discovery, prediction of drug side effects and precision treatment. More recently, a few initial studies attempt to model the dynamic meaning of the terms or term pairs for HG. However, most existing methods still fail to accurately capture and utilize the dynamic evolution of scientific term relations. This article proposes a novel temporal difference embedding (TDE) learning framework to model the temporal difference information evolution of term-pair relations for predicting future interactions. Specifically, the HG problem is formulated as a future connectivity prediction task on a temporal sequence of a dynamic attributed graph. Our approach models both the local neighbor changes of the term-pairs and the changes of the global graph structure over time, learning local and global TDE of node-pairs, respectively. Future term-pair relations can be inferred in a recurrent network based on the local and global TDE. Experiments on three real-world biomedical term relationship datasets show the effectiveness and superiority of the proposed approach. The data and source codes related to TDE are publicly available at https://github.com/Huiweizhou/TDE. Supplementary data are available at Bioinformatics online.

A novel machine learning model based on sparse structure learning with adaptive graph regularization for predicting drug side effects

Drug side effects are closely related to the success and failure of drug development. Here we present a novel machine learning method for side effect prediction. The proposed method treats side effect prediction as a multi-label learning problem and uses sparse structure learning to model the relationships between side effects. Additionally, the proposed method adopts the adaptive graph regularization strategy to explore the local structure in drug data and fuse multiple types of drug features. An alternating optimization algorithm is proposed to solve the optimization problem. We collected chemical structures and biological pathway features of drugs as the inputs of our method to predict drug side effects. The results of the cross-validation experiment showed that our method could significantly improve the prediction performance compared to the other state-of-the-art methods. Besides, our model is highly interpretable. It could learn the drug neighbourhood relationships, side effect relationships, and drug features related to side effects. We systematically validated the information extracted by the model with independent data. Some prediction results could also be supported by literature reports. The proposed method could be applied to integrate both chemical and biological data to predict side effects and helps improve drug safety.

Idse-HE: Hybrid embedding graph neural network for drug side effects prediction

In drug development, unexpected side effects are the main reason for the failure of candidate drug trials. Discovering potential side effects of drugsin silicocan improve the success rate of drug screening. However, most previous works extracted and utilized an effective representation of drugs from a single perspective. These methods merely considered the topological information of drug in the biological entity network, or combined the association information (e.g. knowledge graph KG) between drug and other biomarkers, or only used the chemical structure or sequence information of drug.Consequently, to jointly learn drug features from both the macroscopic biological network and the microscopic drug molecules. We propose a hybrid embedding graph neural network model named idse-HE, which integrates graph embedding module and node embedding module. idse-HE can fuse the drug chemical structure information, the drug substructure sequence information and the drug network topology information. Our model deems the final representation of drugs and side effects as two implicit factors to reconstruct the original matrix and predicts the potential side effects of drugs. In the robustness experiment, idse-HE shows stable performance in all indicators. We reproduce the baselines under the same conditions, and the experimental results indicate that idse-HE is superior to other advanced methods. Finally, we also collect evidence to confirm several real drug side effect pairs in the predicted results, which were previously regarded as negative samples. More detailed information, scientific researchers can access the user-friendly web-server of idse-HE at http://bioinfo.jcu.edu.cn/idse-HE. In this server, users can obtain the original data and source code, and will be guided to reproduce the model results.

Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects

Drugs can treat different diseases but also bring side effects. Undetected and unaccepted side effects for approved drugs can greatly harm the human body and bring huge risks for pharmaceutical companies. Traditional experimental methods used to determine the side effects have several drawbacks, such as low efficiency and high cost. One alternative to achieve this purpose is to design computational methods. Previous studies modeled a binary classification problem by pairing drugs and side effects; however, their classifiers can only extract one feature from each type of drug association. The present work proposed a novel multiple-feature sampling scheme that can extract several features from one type of drug association. Thirteen classification algorithms were employed to construct classifiers with features yielded by such scheme. Their performance was greatly improved compared with that of the classifiers that use the features yielded by the original scheme. Best performance was observed for the classifier based on random forest with MCC of 0.8661, AUROC of 0.969, and AUPR of 0.977. Finally, one key parameter in the multiple-feature sampling scheme was analyzed.

Expression of Concern: Potential Risks and Unknown Effects of mRNA Vaccines on Population Health (6th Rev). Damages Are Being Materialized

Several mRNA vaccines are used on the population in the U.S. I started predicting the dangers of mRNA vaccines before March 2021 and update my findings periodically. My prior model study enabled me to identify many flaws in clinical trials, side-effect evaluation methods and mechanism studies, and I also considered consistent failure in predicting drug side effects in the past and systematic failure of FDA in keeping out dangerous drugs from market. I found that the risks of vaccination cannot be determined by experiments alone and must be determined by using a combination of methods. By studying mRNA expression dynamics and kinetics, I predict that vaccination with mRNA vaccines may increase cancer risks, multiple organ failure risks, earlier death risks, genome alteration speeds by one or more mechanisms, alter the normal selection process for viral evolution resulting in more virulent viruses, and aggravate chronic diseases or cause healed diseases to relapse. Two root problems are practical inability to control expression sites and severe adverse reactions from repeated vaccination. Based on mRNA bio-distribution, the mRNA mainly strikes the liver and other vital organs, and poses grave dangers to persons whose vascular functional reserves are relatively small, or whose vascular systems are temporarily burdened by other causes such as viral infections or life activities. If an mRNA vaccine is administered on a pregnant woman by second or booster shots, spike protein synthesis in fetus brain disrupts the highly regulated protein synthesis processes, resulting in potential brain damages. In less than a year, most of my early predicted damages are being materialized or are on the track to hit the population. In this update, I present a benefits-and-risks map to show how the number of deaths caused by mRNA vaccines is grossly underestimated and why claimed benefits like 95% effectiveness rate and 90% death rate reduction are meaningless and misleading.

A Deep Learning Framework for Multi Drug Side Effects Prediction with Drug Chemical Substructure

Nowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of multiple side effects of drugs with the chemical structure of drugs. As it is a common experience that a single drug can cause multiple side effects, that’s why we have proposed a deep learning model that can predict multiple side effects for a single drug. We have considered three side effects (Dizziness, Allergy, Headache) in this study. We have collected the drug side effects information from the SIDER database. We have achieved an accuracy of ‘0.9494’ with our multi-label classification based proposed model. The proposed model can be used in different stages of the drug development process.

Machine Learning Prediction of Drug Side Effects in Clinical Trials

Machine Learning Prediction of Drug Side Effects in Clinical Trials