AUCTORES
Globalize your Research
Research Article | DOI: https://doi.org/10.31579/2690-4861/220
1101 30th Street NW Suite #500 (Fifth Floor), Washington, D.C. 20007, United States.
*Corresponding Author: Julian Lloyd Bruce, 1101 30th Street NW Suite #500 (Fifth Floor), Washington, D.C. 20007, United States.
Citation: Julian L. Bruce, (2025), An Overview of Unsupervised Machine Learning in Drug Discovery, Pharmacogenomics, and Pharmacovigilance, J. Biomedical Research and Clinical Reviews, 11(2); DOI:10.31579/2690-4861/220
Copyright: © 2025, Julian Lloyd Bruce. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Received: 27 June 2025 | Accepted: 31 July 2025 | Published: 05 September 2025
Keywords: artificial intelligence; drug discovery; pharmacogenomics; pharmacovigilance; computational biology
As biomedical data continue to grow in complexity and often lack annotation, unsupervised machine learning (UML) has emerged as a powerful approach for uncovering hidden patterns and enabling data-driven discovery in pharmacology. This review examines the expanding role of UML in drug discovery, pharmacogenomics, and pharmacovigilance. Core techniques such as clustering, dimensionality reduction, and generative modeling support hypothesis-free analysis and contribute to translational insight. In drug discovery, UML enhances molecular representation, facilitates lead optimization, and guides scaffold development. Graph neural networks (GNNs) are emphasized for their ability to capture complex structural and chemical features that improve drug–target interaction prediction and de novo molecular generation. In clinical contexts, UML enables stratification of pharmacogenomic profiles and supports early detection of adverse drug events through anomaly detection and natural language processing applied to real-world data. By integrating insights from both algorithmic development and real-world applications, this review underscores the growing value of UML as a pivotal tool in advancing contemporary pharmacological research and data-driven decision-making.
As biomedical datasets continue to expand in size, complexity, and diversity, unsupervised machine learning (UML) has become an increasingly important tool in pharmacological research. These datasets originate from a range of sources including chemical compound libraries, multi-omics platforms, electronic health records (EHRs), and adverse event databases. Traditional supervised approaches often require extensive annotation and are limited by labeling biases or incomplete clinical outcomes. In contrast, UML algorithms operate without labeled outcomes, enabling autonomous identification of latent structures, statistical regularities, and hidden relationships that support hypothesis generation and translational insight.
This review examines the role of UML across three major domains of pharmaceutical science: drug discovery, pharmacogenomics, and pharmacovigilance. In drug discovery, clustering, dimensionality reduction, and generative modeling techniques improve molecular representation, facilitate scaffold identification, and accelerate lead optimization [1]. Special attention is given to graph neural networks (GNNs), which capture both local and global chemical structure features and enhance predictions of drug–target interactions as well as de novo compound generation. In pharmacogenomics, UML supports the stratification of patients by identifying molecular subgroups and integrating genomic, transcriptomic, and epigenomic signals to inform individualized dosing and therapeutic selection. In pharmacovigilance, UML enables the detection of latent safety signals using techniques such as anomaly detection and natural language processing, applied to large-scale structured and unstructured data from clinical and post-market surveillance settings [2].
By analyzing recent methodological developments and applied case studies, this review underscores UML’s emergence as both a foundational methodology and a practical framework for uncovering biologically meaningful patterns in high-dimensional, unlabeled datasets. Its flexibility and scalability position UML as a central component of next-generation pharmacological research and data-driven clinical innovation.
A targeted literature review was performed using Google Scholar, PubMed, and IEEE Xplore to identify peer-reviewed articles relevant to unsupervised machine learning in drug discovery, pharmacogenomics, and pharmacovigilance. The search was restricted to publications from the past five years to maintain contemporary relevance. Keyword combinations such as “unsupervised learning,” “graph neural networks,” “pharmacogenomics,” and “pharmacovigilance” were applied to capture both methodological advancements and translational applications. Articles were screened based on their abstracts, methodological integrity, and relevance to data-driven pharmaceutical research.
Methodology of Unsupervised Machine Learning
At its core, UML seeks to uncover latent structures, statistical regularities, or manifold representations within datasets that lack explicit labels or target variables. Unlike supervised machine learning (SML), which learns a function 
by minimizing error between predicted outputs and known ground truth labels Y, UML operates solely on the input space X [3]. This distinction is critical: whereas SML is constrained by the availability and quality of labeled data, UML autonomously infers patterns, enabling discovery in settings where annotation is scarce, noisy, or infeasible—such as high-dimensional omics data, unlabeled chemical libraries, or unstructured clinical narratives. UML algorithms identify clusters, reduce dimensionality, or learn generative representations that expose underlying biological heterogeneity, facilitate hypothesis generation, and augment downstream tasks like drug repurposing or patient stratification without relying on prior assumptions or labeling biases [4].
Key Components of UML
To operationalize these capabilities, UML relies on a structured pipeline composed of interdependent stages. Each component contributes to the model’s ability to uncover meaningful representations in complex biomedical data. The key elements include:
Summary of Existing Literature
Recent advances underscore the growing utility of UML in modeling molecular interactions, optimizing lead compounds, and enhancing post-market drug safety surveillance. For example, Mena-Yedra et al. introduced ALMERIA, a decision-support tool that estimates compound similarity and predicts molecular activity by accounting for conformational variability across large chemical libraries. Implemented with scalable infrastructure, ALMERIA demonstrated exceptional performance on the DUD-E benchmark dataset, achieving ROC AUC scores of 0.99, 0.96, and 0.87 across various partitions. Notably, the study emphasized the model’s generalization capacity and interpretability, using SHAP analysis to elucidate feature contributions—an essential step toward transparent AI in drug discovery [6].
Complementing this, Yin et al. developed DeepDrug, a unified deep learning framework that integrates residual graph convolutional networks (Res-GCNs) and convolutional neural networks (CNNs) to learn both structural and sequential representations of drugs and proteins. DeepDrug outperformed state-of-the-art models across multiple tasks, including binary and multi-label classification of drug–drug and drug–target interactions. Beyond predictive accuracy, the authors applied DeepDrug to the DrugBank database for drug repurposing, identifying top-ranked candidates against SARS-CoV-2—seven of which had independent support for potential efficacy [7]. This highlights the model’s translational potential in real-world therapeutic contexts.
Polanski’s review offers a broader conceptual lens, tracing the evolution of UML in cheminformatics from early self-organizing maps (SOMs) to modern deep chemistry paradigms. He argues that UML excels not only in identifying chemically intuitive features but also in uncovering latent molecular patterns beyond human perception. The review underscores the promise of deep unsupervised architectures in scaffold hopping, feature learning, and molecular representation, while also noting current limitations—particularly the scarcity of high-quality, labeled chemical property data. Polanski concludes that while deep chemistry is still maturing, UML is poised to play a pivotal role in bridging data-driven discovery with rational drug design [8].
Further illustrating the breadth of UML applications in drug discovery, Zhang et al. introduced CASTELO, a hybrid framework that integrates machine learning with molecular modeling to streamline lead optimization workflows. By leveraging contact matrices derived from molecular dynamics simulations and encoding temporal dynamics through convolutional variational autoencoders (CVAEs), CASTELO identifies submolecular “hot spots” for chemical modification without requiring extensive structure–activity relationship data. The study demonstrated that CVAE-based clustering outperformed conventional methods in ranking atom subtypes for optimization, offering medicinal chemists a data-driven strategy to enhance potency while reducing development time [9].
In the realm of post-translational modification prediction, Luo et al. developed DeepPhos, a deep learning model tailored to identify protein phosphorylation sites with high accuracy. Unlike traditional predictors reliant on handcrafted features, DeepPhos employs densely connected convolutional blocks to capture hierarchical sequence representations. The model supports both general and kinase-specific predictions, outperforming existing tools across multiple benchmarks. Its architecture enables nuanced detection of phosphorylation motifs, which are critical for understanding signaling cascades and for designing kinase-targeted therapeutics. This underscores the value of deep unsupervised representations in proteomics [10].
Expanding into clinical informatics, Miotto et al. proposed Deep Patient, an unsupervised deep feature learning framework that generates patient-level embeddings from EHRs using stacked denoising autoencoders. Trained on data from over 700,000 patients, Deep Patient captured latent health representations that significantly improved disease prediction across 78 conditions, including schizophrenia, diabetes, and various cancers. The model’s ability to generalize across diverse clinical domains highlights the translational power of UML in precision medicine, enabling early risk stratification and personalized care strategies [11].
Offering a broader lens on machine learning’s role in pharmaceutical R&D, Ibáñez Antolín emphasized the importance of unsupervised techniques—particularly clustering and dimensionality reduction—in early-stage applications such as target identification, biomarker discovery, and digital pathology analysis. The study highlighted that machine learning workflows often begin with extensive data preprocessing, where UML plays a pivotal role in revealing latent structure within high-dimensional omics and imaging datasets. This exploratory capability is especially valuable when labeled data are limited or incomplete, reinforcing the utility of unsupervised methods in hypothesis generation and mechanistic insight across translational research domains [1].
Finally, Vamathevan et al. provided a comprehensive review of ML applications across the drug development pipeline, highlighting UML’s contributions to target validation, compound screening, and clinical trial optimization. The authors acknowledged challenges such as interpretability and reproducibility but emphasized that, when paired with high-quality data, UML can reduce attrition rates and accelerate decision-making. Notably, the review underscored the synergy between UML and supervised learning in hybrid models, advocating for integrative approaches that combine predictive power with biological plausibility [2].
Innovative Approaches: UML for Lead Optimization
The rapid advancement and adoption of UML in lead optimization reflects its growing utility in navigating high-dimensional chemical space without reliance on labeled potency data. By uncovering latent structural and physicochemical patterns through clustering, dimensionality reduction, and representation learning, UML enables the identification of scaffold relationships, substituent effects, and bioisosteric transformations that might otherwise remain obscured. This is particularly advantageous in early-stage optimization, where empirical activity data are often sparse or noisy. When integrated with graph-based encodings or generative frameworks, UML serves as a foundational layer for downstream predictive modeling, offering a scalable, hypothesis-free strategy for rational compound refinement [12].
In her chapter on AI-driven drug development, Ashenden frames lead optimization as a multidimensional balancing act—one that extends beyond potency to include solubility, metabolic stability, and toxicity [13]. She outlines how AI, including unsupervised methods, can integrate heterogeneous datasets such as high-throughput screening results, physicochemical descriptors, and ADMET profiles. This integration enables medicinal chemists to prioritize chemical modifications that enhance drug-likeness while minimizing downstream attrition. Ashenden emphasizes that UML is particularly well-suited for identifying liabilities early in the pipeline, where labeled outcomes are often unavailable or incomplete [13].
A broader methodological perspective is offered by Li et al., who review the landscape of machine learning-based scoring functions (ML-SFs) for structure-based lead optimization. While their primary focus is on supervised models, they highlight key limitations of classical scoring functions and emphasize the need for approaches that generalize across diverse protein–ligand complexes. The authors argue that unsupervised pretraining, including methods such as autoencoders, graph embeddings, and contrastive learning, can significantly improve the quality of molecular representations used in predictive tasks. Their analysis supports a hybrid modeling framework in which UML-derived features form the foundation for more accurate and transferable ML-SFs [14].
A more targeted application of unsupervised spatial learning can be seen in the development of DeltaDelta, a deep 3D convolutional neural network introduced by Jiménez-Luna et al. to rank congeneric ligands based on predicted potency differences [15]. The model architecture incorporates an unsupervised pretraining phase that learns spatial features from protein–ligand complexes, which are then fine-tuned using potency labels. To evaluate its performance, the researchers conducted one of the largest blind assessments to date, involving over 3,000 ligands and 13 targets sourced from Janssen, Pfizer, and Biogen. Across these datasets, DeltaDelta consistently outperformed traditional docking-based methods in ranking accuracy. The study highlights how embedding unsupervised spatial representation into lead optimization pipelines can produce models that are both predictive and broadly generalizable across diverse chemical series [15].
In the domain of fragment-based design, Green and Durrant introduced DeepFrag, a deep convolutional neural network trained on protein–ligand complexes with systematically removed fragments. The model learns to predict fragment vectors that complement the receptor environment, effectively identifying fragment–receptor compatibility patterns without explicit supervision. Benchmarking revealed that DeepFrag could recover the correct fragment from a library of over 6,500 candidates approximately 58% of the time. Even when the exact fragment was not retrieved, top-ranked alternatives were often chemically similar and synthetically tractable. The authors also released an open-source browser-based implementation, democratizing access to AI-assisted fragment elaboration and accelerating hypothesis generation in structure-guided design [16].
Visualizing Drug Data: The Role of Graph Neural Networks
Graph neural networks (GNNs) have become key technology in molecular representation learning, offering a flexible and expressive architecture for encoding chemical structures as graphs. In this framework, atoms are treated as nodes and covalent bonds as edges, allowing GNNs to capture both local atomic environments and long-range topological dependencies. This capability supports the modeling of stereoelectronic effects, conformational dynamics, and functional group connectivity, all of which are essential for accurate prediction of pharmacological properties. Unlike traditional descriptor-based models that depend on handcrafted molecular fingerprints, GNNs learn task-specific representations directly from graph topology through iterative message-passing mechanisms. This approach has demonstrated strong performance across a range of applications, including drug–target interaction (DTI) prediction, binding affinity estimation, molecular property regression, and de novo molecular generation [12].
A comparative study by Jiang et al. evaluated the performance of four descriptor-based models (SVM, XGBoost, RF, DNN) and four graph-based models (GCN, GAT, MPNN, Attentive FP) across 11 public datasets covering endpoints such as solubility, toxicity, and ADME properties. While descriptor-based models generally outperformed GNNs on smaller datasets, Attentive FP and MPNN demonstrated superior performance in multi-task and large-scale settings. The authors concluded that GNNs offer complementary advantages in capturing structural nuances and recommended their integration into hybrid modeling pipelines [17].
To enhance predictive robustness, Bongini et al. developed FP-GNN, a hybrid architecture that fuses molecular fingerprints with graph-based embeddings. Evaluated on 13 public datasets and 14 phenotypic screening datasets, FP-GNN consistently outperformed both traditional machine learning and deep learning baselines. The model also demonstrated resilience to noise and improved interpretability, suggesting that combining topological and fingerprint-derived features enhances generalizability in real-world drug discovery scenarios [18].
In the generative modeling space, Xiong et al. explored the use of GNNs for de novo molecular generation by integrating them with reinforcement learning and generative frameworks. Their approach enabled the generation of chemically valid, synthetically accessible molecules optimized for properties such as binding affinity and logP. Compared to SMILES-based models, GNN-based architectures achieved higher validity, novelty, and property alignment—highlighting their suitability for scaffold-constrained and multi-objective optimization in early-stage design [19].
For DTI prediction, Zhang et al. reviewed recent GNN-based models that incorporate attention mechanisms, multi-modal embeddings, and 3D structural information. These architectures outperform traditional sequence-based models by capturing spatial and topological dependencies critical for accurate interaction prediction. The authors emphasized that GNNs, particularly when combined with protein structure data, offer scalable and interpretable solutions for virtual screening and drug repurposing [20].
A more targeted application was proposed by Wang et al., who introduced DataDTA—a dual-graph GNN framework for predicting drug–target binding affinities. The model integrates molecular graphs with predicted protein pocket descriptors and sequence embeddings, using a dual-interaction aggregation strategy to capture both intra- and inter-molecular interactions. On benchmark datasets, DataDTA achieved a concordance index of 0.806 and a Pearson correlation of 0.814, outperforming several state-of-the-art baselines. These results underscore the value of combining structural and sequence-level features in affinity prediction [21].
Looking ahead, Abate et al. offered a forward-looking perspective on conditional de novo drug design using GNNs. Their review emphasized the importance of conditioning mechanisms, such as scaffold constraints or pharmacological profiles, to guide molecular generation toward specific objectives. Advances in graph-based generative models now support multi-objective optimization across drug-likeness, synthetic accessibility, and target specificity. These developments position GNNs as a scalable and customizable platform for rational drug design [22].
Stratifying Complexity: UML in Pharmacogenomics
The variability in drug response across individuals, shaped by genomic variation, epigenetic regulation, and environmental exposures, remains a central challenge in precision medicine. UML offers a data-driven framework for addressing this complexity by revealing latent structure in high-dimensional pharmacogenomic datasets without relying on predefined phenotypic labels. Unlike supervised models that require annotated outcomes such as therapeutic efficacy or adverse events, UML algorithms autonomously identify patterns in genomic variants, transcriptomic signatures, and pharmacokinetic trajectories. This enables the stratification of patients into molecularly coherent subgroups, the discovery of novel biomarkers, and the elucidation of genotype–phenotype relationships that may otherwise remain obscured. Techniques such as clustering, dimensionality reduction, and autoencoder-based representation learning are particularly well suited for integrating multi-omics data and generating biologically grounded hypotheses to inform individualized treatment strategies [12].
A widely accepted perspective on this approach is offered by Kalinin et al., who explored the use of deep unsupervised architectures such as autoencoders and deep belief networks to learn hierarchical representations from heterogeneous data sources, including gene expression, epigenetic marks, and electronic health records (EHRs). Their work demonstrated that these representations enhance downstream tasks such as drug response prediction and adverse event forecasting, particularly through the identification of regulatory variants in noncoding regions. The authors argue that unsupervised machine learning will play a central role in the development of scalable and interpretable frameworks that enable personalized medication selection and dosing strategies [23].
In a complementary study, Lautier et al. applied clustering algorithms to pharmacokinetic time-series data, specifically plasma concentration–time curves, to identify latent subgroups of drug metabolism. Using methods such as k-means and hierarchical clustering, they showed that UML could recover clinically meaningful pharmacokinetic phenotypes, including fast and slow metabolizers, without prior knowledge of covariates or outcomes. Their case study involving 250 PK curves demonstrated that unsupervised clustering could independently validate pharmacogenomic findings. This suggests its utility in guiding individualized dosing regimens for drugs with narrow therapeutic indices [24].
Expanding the scope to healthcare operations, Lopez et al. developed a hybrid framework that integrates UML with process mining and discrete-event simulation to model patient flow and treatment trajectories across clinical settings. By analyzing EHR-derived event logs, they identified common care pathways and deviations that may influence drug response and safety. Their approach enables simulation of treatment outcomes under varying protocols, offering a systems-level perspective on how molecular subtypes interact with real-world clinical workflows. This integration of UML with operational modeling highlights its potential to bridge molecular stratification with actionable clinical decision-making [25].
Uncovering Latent Safety Signals: UML in Pharmacovigilance
The growing complexity of pharmacotherapy, driven by demographic aging, multimorbidity, and widespread polypharmacy, has revealed critical limitations in conventional pharmacovigilance systems. These systems often depend on static alert thresholds, spontaneous reporting, and predefined rule sets, which may fail to capture emerging or context-specific safety concerns. UML introduces a data-centric alternative by enabling the detection of latent safety signals within large-scale, unlabeled clinical datasets. By modeling the probability distributions of high-dimensional sources such as EHRs, prescription logs, and adverse event registries, UML algorithms can autonomously identify anomalous prescribing behaviors, rare adverse drug reactions (ADRs), and systemic deviations in medication use [12]. Techniques such as one-class support vector machines (OCSVMs), isolation forests, and density-based clustering are particularly effective for detecting these outliers, especially when configured to incorporate patient-level covariates like renal function, age, and comorbidity burden. When combined with natural language processing (NLP), UML further extends its utility to unstructured data sources including clinical notes, discharge summaries, and social media content [4][5]. This integration supports a multi-modal, scalable pharmacovigilance framework that is better equipped to respond to evolving safety risks in real-world settings.
A compelling demonstration of this approach comes from Nagata et al., who applied OCSVMs to detect overdose and underdose prescriptions across 21 commonly used drugs using EHR data from Kyushu University Hospital. Each model was trained on three patient-specific features—age, weight, and prescribed dose—and evaluated against both real-world and synthetic dosing errors. The OCSVMs successfully identified 87.1% of clinically confirmed dosing anomalies and achieved high F1-scores on synthetic test sets (0.973 for overdose, 0.839 for underdose). Comparative analysis with other anomaly detection methods confirmed the superior precision and recall of the OCSVM approach, underscoring its potential as a real-time safeguard in high-risk prescribing environments [26].
Expanding the methodological scope, Yap advocates for integrating UML across diverse pharmacovigilance data streams, including EHRs, spontaneous reporting systems, and social media platforms. His work emphasizes the synergy between UML and NLP, particularly in mining unstructured text for early detection of rare ADRs and drug–drug interactions. By combining clustering, anomaly detection, and sentiment analysis, Yap proposes a hybrid framework that enhances signal detection while preserving clinical interpretability. He also stresses the importance of regulatory alignment and domain expertise to ensure that UML-generated insights are both actionable and trustworthy [27].
From an ethical and operational standpoint, Zou highlights the challenges of deploying UML in clinical pharmacovigilance. His analysis calls for transparent model design, clinician engagement, and robust data governance to mitigate risks such as alert fatigue, algorithmic bias, and overfitting. Zou also notes that UML can improve pharmacovigilance efficiency by filtering noise and prioritizing high-risk signals in large-scale surveillance systems—an essential capability in resource-constrained healthcare settings [28].
Finally, a systems-level lens was applied by Basile et al., who examined how UML can be used to link multi-omics datasets with clinical phenotypes to uncover mechanistic insights into ADRs. Their approach employed dimensionality reduction and clustering to delineate patient subpopulations with differential drug responses, providing a foundation for hypothesis generation, biomarker discovery, and targeted safety assessments. Basile’s work highlights UML not only as a detection engine but as a strategic tool for shaping future pharmacovigilance and regulatory science efforts [29].
Unsupervised machine learning (UML) is transforming pharmacological research by uncovering latent structure within complex, unlabeled datasets. This review has illustrated how UML contributes to drug discovery through scaffold identification and lead optimization with graph neural networks, supports patient stratification in pharmacogenomics using deep representations, and facilitates the detection of safety signals in pharmacovigilance by leveraging anomaly detection and natural language processing. These methods enhance molecular design, personalized medicine, and real-world drug safety monitoring. Although interpretability and regulatory challenges remain, UML offers a foundational approach for data-driven discovery in environments where annotation is limited or unavailable.
Clearly Auctoresonline and particularly Psychology and Mental Health Care Journal is dedicated to improving health care services for individuals and populations. The editorial boards' ability to efficiently recognize and share the global importance of health literacy with a variety of stakeholders. Auctoresonline publishing platform can be used to facilitate of optimal client-based services and should be added to health care professionals' repertoire of evidence-based health care resources.
Journal of Clinical Cardiology and Cardiovascular Intervention The submission and review process was adequate. However I think that the publication total value should have been enlightened in early fases. Thank you for all.
Journal of Women Health Care and Issues By the present mail, I want to say thank to you and tour colleagues for facilitating my published article. Specially thank you for the peer review process, support from the editorial office. I appreciate positively the quality of your journal.
Journal of Clinical Research and Reports I would be very delighted to submit my testimonial regarding the reviewer board and the editorial office. The reviewer board were accurate and helpful regarding any modifications for my manuscript. And the editorial office were very helpful and supportive in contacting and monitoring with any update and offering help. It was my pleasure to contribute with your promising Journal and I am looking forward for more collaboration.
We would like to thank the Journal of Thoracic Disease and Cardiothoracic Surgery because of the services they provided us for our articles. The peer-review process was done in a very excellent time manner, and the opinions of the reviewers helped us to improve our manuscript further. The editorial office had an outstanding correspondence with us and guided us in many ways. During a hard time of the pandemic that is affecting every one of us tremendously, the editorial office helped us make everything easier for publishing scientific work. Hope for a more scientific relationship with your Journal.
The peer-review process which consisted high quality queries on the paper. I did answer six reviewers’ questions and comments before the paper was accepted. The support from the editorial office is excellent.
Journal of Neuroscience and Neurological Surgery. I had the experience of publishing a research article recently. The whole process was simple from submission to publication. The reviewers made specific and valuable recommendations and corrections that improved the quality of my publication. I strongly recommend this Journal.
Dr. Katarzyna Byczkowska My testimonial covering: "The peer review process is quick and effective. The support from the editorial office is very professional and friendly. Quality of the Clinical Cardiology and Cardiovascular Interventions is scientific and publishes ground-breaking research on cardiology that is useful for other professionals in the field.
Thank you most sincerely, with regard to the support you have given in relation to the reviewing process and the processing of my article entitled "Large Cell Neuroendocrine Carcinoma of The Prostate Gland: A Review and Update" for publication in your esteemed Journal, Journal of Cancer Research and Cellular Therapeutics". The editorial team has been very supportive.
Testimony of Journal of Clinical Otorhinolaryngology: work with your Reviews has been a educational and constructive experience. The editorial office were very helpful and supportive. It was a pleasure to contribute to your Journal.
Dr. Bernard Terkimbi Utoo, I am happy to publish my scientific work in Journal of Women Health Care and Issues (JWHCI). The manuscript submission was seamless and peer review process was top notch. I was amazed that 4 reviewers worked on the manuscript which made it a highly technical, standard and excellent quality paper. I appreciate the format and consideration for the APC as well as the speed of publication. It is my pleasure to continue with this scientific relationship with the esteem JWHCI.
This is an acknowledgment for peer reviewers, editorial board of Journal of Clinical Research and Reports. They show a lot of consideration for us as publishers for our research article “Evaluation of the different factors associated with side effects of COVID-19 vaccination on medical students, Mutah university, Al-Karak, Jordan”, in a very professional and easy way. This journal is one of outstanding medical journal.
Dear Hao Jiang, to Journal of Nutrition and Food Processing We greatly appreciate the efficient, professional and rapid processing of our paper by your team. If there is anything else we should do, please do not hesitate to let us know. On behalf of my co-authors, we would like to express our great appreciation to editor and reviewers.
As an author who has recently published in the journal "Brain and Neurological Disorders". I am delighted to provide a testimonial on the peer review process, editorial office support, and the overall quality of the journal. The peer review process at Brain and Neurological Disorders is rigorous and meticulous, ensuring that only high-quality, evidence-based research is published. The reviewers are experts in their fields, and their comments and suggestions were constructive and helped improve the quality of my manuscript. The review process was timely and efficient, with clear communication from the editorial office at each stage. The support from the editorial office was exceptional throughout the entire process. The editorial staff was responsive, professional, and always willing to help. They provided valuable guidance on formatting, structure, and ethical considerations, making the submission process seamless. Moreover, they kept me informed about the status of my manuscript and provided timely updates, which made the process less stressful. The journal Brain and Neurological Disorders is of the highest quality, with a strong focus on publishing cutting-edge research in the field of neurology. The articles published in this journal are well-researched, rigorously peer-reviewed, and written by experts in the field. The journal maintains high standards, ensuring that readers are provided with the most up-to-date and reliable information on brain and neurological disorders. In conclusion, I had a wonderful experience publishing in Brain and Neurological Disorders. The peer review process was thorough, the editorial office provided exceptional support, and the journal's quality is second to none. I would highly recommend this journal to any researcher working in the field of neurology and brain disorders.
Dear Agrippa Hilda, Journal of Neuroscience and Neurological Surgery, Editorial Coordinator, I trust this message finds you well. I want to extend my appreciation for considering my article for publication in your esteemed journal. I am pleased to provide a testimonial regarding the peer review process and the support received from your editorial office. The peer review process for my paper was carried out in a highly professional and thorough manner. The feedback and comments provided by the authors were constructive and very useful in improving the quality of the manuscript. This rigorous assessment process undoubtedly contributes to the high standards maintained by your journal.
International Journal of Clinical Case Reports and Reviews. I strongly recommend to consider submitting your work to this high-quality journal. The support and availability of the Editorial staff is outstanding and the review process was both efficient and rigorous.
Thank you very much for publishing my Research Article titled “Comparing Treatment Outcome Of Allergic Rhinitis Patients After Using Fluticasone Nasal Spray And Nasal Douching" in the Journal of Clinical Otorhinolaryngology. As Medical Professionals we are immensely benefited from study of various informative Articles and Papers published in this high quality Journal. I look forward to enriching my knowledge by regular study of the Journal and contribute my future work in the field of ENT through the Journal for use by the medical fraternity. The support from the Editorial office was excellent and very prompt. I also welcome the comments received from the readers of my Research Article.
Dear Erica Kelsey, Editorial Coordinator of Cancer Research and Cellular Therapeutics Our team is very satisfied with the processing of our paper by your journal. That was fast, efficient, rigorous, but without unnecessary complications. We appreciated the very short time between the submission of the paper and its publication on line on your site.
I am very glad to say that the peer review process is very successful and fast and support from the Editorial Office. Therefore, I would like to continue our scientific relationship for a long time. And I especially thank you for your kindly attention towards my article. Have a good day!
"We recently published an article entitled “Influence of beta-Cyclodextrins upon the Degradation of Carbofuran Derivatives under Alkaline Conditions" in the Journal of “Pesticides and Biofertilizers” to show that the cyclodextrins protect the carbamates increasing their half-life time in the presence of basic conditions This will be very helpful to understand carbofuran behaviour in the analytical, agro-environmental and food areas. We greatly appreciated the interaction with the editor and the editorial team; we were particularly well accompanied during the course of the revision process, since all various steps towards publication were short and without delay".
I would like to express my gratitude towards you process of article review and submission. I found this to be very fair and expedient. Your follow up has been excellent. I have many publications in national and international journal and your process has been one of the best so far. Keep up the great work.
We are grateful for this opportunity to provide a glowing recommendation to the Journal of Psychiatry and Psychotherapy. We found that the editorial team were very supportive, helpful, kept us abreast of timelines and over all very professional in nature. The peer review process was rigorous, efficient and constructive that really enhanced our article submission. The experience with this journal remains one of our best ever and we look forward to providing future submissions in the near future.
I am very pleased to serve as EBM of the journal, I hope many years of my experience in stem cells can help the journal from one way or another. As we know, stem cells hold great potential for regenerative medicine, which are mostly used to promote the repair response of diseased, dysfunctional or injured tissue using stem cells or their derivatives. I think Stem Cell Research and Therapeutics International is a great platform to publish and share the understanding towards the biology and translational or clinical application of stem cells.
I would like to give my testimony in the support I have got by the peer review process and to support the editorial office where they were of asset to support young author like me to be encouraged to publish their work in your respected journal and globalize and share knowledge across the globe. I really give my great gratitude to your journal and the peer review including the editorial office.
I am delighted to publish our manuscript entitled "A Perspective on Cocaine Induced Stroke - Its Mechanisms and Management" in the Journal of Neuroscience and Neurological Surgery. The peer review process, support from the editorial office, and quality of the journal are excellent. The manuscripts published are of high quality and of excellent scientific value. I recommend this journal very much to colleagues.
Dr.Tania Muñoz, My experience as researcher and author of a review article in The Journal Clinical Cardiology and Interventions has been very enriching and stimulating. The editorial team is excellent, performs its work with absolute responsibility and delivery. They are proactive, dynamic and receptive to all proposals. Supporting at all times the vast universe of authors who choose them as an option for publication. The team of review specialists, members of the editorial board, are brilliant professionals, with remarkable performance in medical research and scientific methodology. Together they form a frontline team that consolidates the JCCI as a magnificent option for the publication and review of high-level medical articles and broad collective interest. I am honored to be able to share my review article and open to receive all your comments.
“The peer review process of JPMHC is quick and effective. Authors are benefited by good and professional reviewers with huge experience in the field of psychology and mental health. The support from the editorial office is very professional. People to contact to are friendly and happy to help and assist any query authors might have. Quality of the Journal is scientific and publishes ground-breaking research on mental health that is useful for other professionals in the field”.
Dear editorial department: On behalf of our team, I hereby certify the reliability and superiority of the International Journal of Clinical Case Reports and Reviews in the peer review process, editorial support, and journal quality. Firstly, the peer review process of the International Journal of Clinical Case Reports and Reviews is rigorous, fair, transparent, fast, and of high quality. The editorial department invites experts from relevant fields as anonymous reviewers to review all submitted manuscripts. These experts have rich academic backgrounds and experience, and can accurately evaluate the academic quality, originality, and suitability of manuscripts. The editorial department is committed to ensuring the rigor of the peer review process, while also making every effort to ensure a fast review cycle to meet the needs of authors and the academic community. Secondly, the editorial team of the International Journal of Clinical Case Reports and Reviews is composed of a group of senior scholars and professionals with rich experience and professional knowledge in related fields. The editorial department is committed to assisting authors in improving their manuscripts, ensuring their academic accuracy, clarity, and completeness. Editors actively collaborate with authors, providing useful suggestions and feedback to promote the improvement and development of the manuscript. We believe that the support of the editorial department is one of the key factors in ensuring the quality of the journal. Finally, the International Journal of Clinical Case Reports and Reviews is renowned for its high- quality articles and strict academic standards. The editorial department is committed to publishing innovative and academically valuable research results to promote the development and progress of related fields. The International Journal of Clinical Case Reports and Reviews is reasonably priced and ensures excellent service and quality ratio, allowing authors to obtain high-level academic publishing opportunities in an affordable manner. I hereby solemnly declare that the International Journal of Clinical Case Reports and Reviews has a high level of credibility and superiority in terms of peer review process, editorial support, reasonable fees, and journal quality. Sincerely, Rui Tao.
Clinical Cardiology and Cardiovascular Interventions I testity the covering of the peer review process, support from the editorial office, and quality of the journal.
Clinical Cardiology and Cardiovascular Interventions, we deeply appreciate the interest shown in our work and its publication. It has been a true pleasure to collaborate with you. The peer review process, as well as the support provided by the editorial office, have been exceptional, and the quality of the journal is very high, which was a determining factor in our decision to publish with you.
The peer reviewers process is quick and effective, the supports from editorial office is excellent, the quality of journal is high. I would like to collabroate with Internatioanl journal of Clinical Case Reports and Reviews journal clinically in the future time.
Clinical Cardiology and Cardiovascular Interventions, I would like to express my sincerest gratitude for the trust placed in our team for the publication in your journal. It has been a true pleasure to collaborate with you on this project. I am pleased to inform you that both the peer review process and the attention from the editorial coordination have been excellent. Your team has worked with dedication and professionalism to ensure that your publication meets the highest standards of quality. We are confident that this collaboration will result in mutual success, and we are eager to see the fruits of this shared effort.
Dear Dr. Jessica Magne, Editorial Coordinator 0f Clinical Cardiology and Cardiovascular Interventions, I hope this message finds you well. I want to express my utmost gratitude for your excellent work and for the dedication and speed in the publication process of my article titled "Navigating Innovation: Qualitative Insights on Using Technology for Health Education in Acute Coronary Syndrome Patients." I am very satisfied with the peer review process, the support from the editorial office, and the quality of the journal. I hope we can maintain our scientific relationship in the long term.
Dear Monica Gissare, - Editorial Coordinator of Nutrition and Food Processing. ¨My testimony with you is truly professional, with a positive response regarding the follow-up of the article and its review, you took into account my qualities and the importance of the topic¨.
Dear Dr. Jessica Magne, Editorial Coordinator 0f Clinical Cardiology and Cardiovascular Interventions, The review process for the article “The Handling of Anti-aggregants and Anticoagulants in the Oncologic Heart Patient Submitted to Surgery” was extremely rigorous and detailed. From the initial submission to the final acceptance, the editorial team at the “Journal of Clinical Cardiology and Cardiovascular Interventions” demonstrated a high level of professionalism and dedication. The reviewers provided constructive and detailed feedback, which was essential for improving the quality of our work. Communication was always clear and efficient, ensuring that all our questions were promptly addressed. The quality of the “Journal of Clinical Cardiology and Cardiovascular Interventions” is undeniable. It is a peer-reviewed, open-access publication dedicated exclusively to disseminating high-quality research in the field of clinical cardiology and cardiovascular interventions. The journal's impact factor is currently under evaluation, and it is indexed in reputable databases, which further reinforces its credibility and relevance in the scientific field. I highly recommend this journal to researchers looking for a reputable platform to publish their studies.
Dear Editorial Coordinator of the Journal of Nutrition and Food Processing! "I would like to thank the Journal of Nutrition and Food Processing for including and publishing my article. The peer review process was very quick, movement and precise. The Editorial Board has done an extremely conscientious job with much help, valuable comments and advices. I find the journal very valuable from a professional point of view, thank you very much for allowing me to be part of it and I would like to participate in the future!”
Dealing with The Journal of Neurology and Neurological Surgery was very smooth and comprehensive. The office staff took time to address my needs and the response from editors and the office was prompt and fair. I certainly hope to publish with this journal again.Their professionalism is apparent and more than satisfactory. Susan Weiner
My Testimonial Covering as fellowing: Lin-Show Chin. The peer reviewers process is quick and effective, the supports from editorial office is excellent, the quality of journal is high. I would like to collabroate with Internatioanl journal of Clinical Case Reports and Reviews.
My experience publishing in Psychology and Mental Health Care was exceptional. The peer review process was rigorous and constructive, with reviewers providing valuable insights that helped enhance the quality of our work. The editorial team was highly supportive and responsive, making the submission process smooth and efficient. The journal's commitment to high standards and academic rigor makes it a respected platform for quality research. I am grateful for the opportunity to publish in such a reputable journal.
My experience publishing in International Journal of Clinical Case Reports and Reviews was exceptional. I Come forth to Provide a Testimonial Covering the Peer Review Process and the editorial office for the Professional and Impartial Evaluation of the Manuscript.
I would like to offer my testimony in the support. I have received through the peer review process and support the editorial office where they are to support young authors like me, encourage them to publish their work in your esteemed journals, and globalize and share knowledge globally. I really appreciate your journal, peer review, and editorial office.
Dear Agrippa Hilda- Editorial Coordinator of Journal of Neuroscience and Neurological Surgery, "The peer review process was very quick and of high quality, which can also be seen in the articles in the journal. The collaboration with the editorial office was very good."
I would like to express my sincere gratitude for the support and efficiency provided by the editorial office throughout the publication process of my article, “Delayed Vulvar Metastases from Rectal Carcinoma: A Case Report.” I greatly appreciate the assistance and guidance I received from your team, which made the entire process smooth and efficient. The peer review process was thorough and constructive, contributing to the overall quality of the final article. I am very grateful for the high level of professionalism and commitment shown by the editorial staff, and I look forward to maintaining a long-term collaboration with the International Journal of Clinical Case Reports and Reviews.
To Dear Erin Aust, I would like to express my heartfelt appreciation for the opportunity to have my work published in this esteemed journal. The entire publication process was smooth and well-organized, and I am extremely satisfied with the final result. The Editorial Team demonstrated the utmost professionalism, providing prompt and insightful feedback throughout the review process. Their clear communication and constructive suggestions were invaluable in enhancing my manuscript, and their meticulous attention to detail and dedication to quality are truly commendable. Additionally, the support from the Editorial Office was exceptional. From the initial submission to the final publication, I was guided through every step of the process with great care and professionalism. The team's responsiveness and assistance made the entire experience both easy and stress-free. I am also deeply impressed by the quality and reputation of the journal. It is an honor to have my research featured in such a respected publication, and I am confident that it will make a meaningful contribution to the field.
"I am grateful for the opportunity of contributing to [International Journal of Clinical Case Reports and Reviews] and for the rigorous review process that enhances the quality of research published in your esteemed journal. I sincerely appreciate the time and effort of your team who have dedicatedly helped me in improvising changes and modifying my manuscript. The insightful comments and constructive feedback provided have been invaluable in refining and strengthening my work".
I thank the ‘Journal of Clinical Research and Reports’ for accepting this article for publication. This is a rigorously peer reviewed journal which is on all major global scientific data bases. I note the review process was prompt, thorough and professionally critical. It gave us an insight into a number of important scientific/statistical issues. The review prompted us to review the relevant literature again and look at the limitations of the study. The peer reviewers were open, clear in the instructions and the editorial team was very prompt in their communication. This journal certainly publishes quality research articles. I would recommend the journal for any future publications.
Dear Jessica Magne, with gratitude for the joint work. Fast process of receiving and processing the submitted scientific materials in “Clinical Cardiology and Cardiovascular Interventions”. High level of competence of the editors with clear and correct recommendations and ideas for enriching the article.
We found the peer review process quick and positive in its input. The support from the editorial officer has been very agile, always with the intention of improving the article and taking into account our subsequent corrections.
My article, titled 'No Way Out of the Smartphone Epidemic Without Considering the Insights of Brain Research,' has been republished in the International Journal of Clinical Case Reports and Reviews. The review process was seamless and professional, with the editors being both friendly and supportive. I am deeply grateful for their efforts.
To Dear Erin Aust – Editorial Coordinator of Journal of General Medicine and Clinical Practice! I declare that I am absolutely satisfied with your work carried out with great competence in following the manuscript during the various stages from its receipt, during the revision process to the final acceptance for publication. Thank Prof. Elvira Farina
Dear Jessica, and the super professional team of the ‘Clinical Cardiology and Cardiovascular Interventions’ I am sincerely grateful to the coordinated work of the journal team for the no problem with the submission of my manuscript: “Cardiometabolic Disorders in A Pregnant Woman with Severe Preeclampsia on the Background of Morbid Obesity (Case Report).” The review process by 5 experts was fast, and the comments were professional, which made it more specific and academic, and the process of publication and presentation of the article was excellent. I recommend that my colleagues publish articles in this journal, and I am interested in further scientific cooperation. Sincerely and best wishes, Dr. Oleg Golyanovskiy.
Dear Ashley Rosa, Editorial Coordinator of the journal - Psychology and Mental Health Care. " The process of obtaining publication of my article in the Psychology and Mental Health Journal was positive in all areas. The peer review process resulted in a number of valuable comments, the editorial process was collaborative and timely, and the quality of this journal has been quickly noticed, resulting in alternative journals contacting me to publish with them." Warm regards, Susan Anne Smith, PhD. Australian Breastfeeding Association.
Dear Jessica Magne, Editorial Coordinator, Clinical Cardiology and Cardiovascular Interventions, Auctores Publishing LLC. I appreciate the journal (JCCI) editorial office support, the entire team leads were always ready to help, not only on technical front but also on thorough process. Also, I should thank dear reviewers’ attention to detail and creative approach to teach me and bring new insights by their comments. Surely, more discussions and introduction of other hemodynamic devices would provide better prevention and management of shock states. Your efforts and dedication in presenting educational materials in this journal are commendable. Best wishes from, Farahnaz Fallahian.
Dear Maria Emerson, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews, Auctores Publishing LLC. I am delighted to have published our manuscript, "Acute Colonic Pseudo-Obstruction (ACPO): A rare but serious complication following caesarean section." I want to thank the editorial team, especially Maria Emerson, for their prompt review of the manuscript, quick responses to queries, and overall support. Yours sincerely Dr. Victor Olagundoye.
Dear Ashley Rosa, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews. Many thanks for publishing this manuscript after I lost confidence the editors were most helpful, more than other journals Best wishes from, Susan Anne Smith, PhD. Australian Breastfeeding Association.
Dear Agrippa Hilda, Editorial Coordinator, Journal of Neuroscience and Neurological Surgery. The entire process including article submission, review, revision, and publication was extremely easy. The journal editor was prompt and helpful, and the reviewers contributed to the quality of the paper. Thank you so much! Eric Nussbaum, MD
Dr Hala Al Shaikh This is to acknowledge that the peer review process for the article ’ A Novel Gnrh1 Gene Mutation in Four Omani Male Siblings, Presentation and Management ’ sent to the International Journal of Clinical Case Reports and Reviews was quick and smooth. The editorial office was prompt with easy communication.
Dear Erin Aust, Editorial Coordinator, Journal of General Medicine and Clinical Practice. We are pleased to share our experience with the “Journal of General Medicine and Clinical Practice”, following the successful publication of our article. The peer review process was thorough and constructive, helping to improve the clarity and quality of the manuscript. We are especially thankful to Ms. Erin Aust, the Editorial Coordinator, for her prompt communication and continuous support throughout the process. Her professionalism ensured a smooth and efficient publication experience. The journal upholds high editorial standards, and we highly recommend it to fellow researchers seeking a credible platform for their work. Best wishes By, Dr. Rakhi Mishra.
Dear Jessica Magne, Editorial Coordinator, Clinical Cardiology and Cardiovascular Interventions, Auctores Publishing LLC. The peer review process of the journal of Clinical Cardiology and Cardiovascular Interventions was excellent and fast, as was the support of the editorial office and the quality of the journal. Kind regards Walter F. Riesen Prof. Dr. Dr. h.c. Walter F. Riesen.
Dear Ashley Rosa, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews, Auctores Publishing LLC. Thank you for publishing our article, Exploring Clozapine's Efficacy in Managing Aggression: A Multiple Single-Case Study in Forensic Psychiatry in the international journal of clinical case reports and reviews. We found the peer review process very professional and efficient. The comments were constructive, and the whole process was efficient. On behalf of the co-authors, I would like to thank you for publishing this article. With regards, Dr. Jelle R. Lettinga.
Dear Clarissa Eric, Editorial Coordinator, Journal of Clinical Case Reports and Studies, I would like to express my deep admiration for the exceptional professionalism demonstrated by your journal. I am thoroughly impressed by the speed of the editorial process, the substantive and insightful reviews, and the meticulous preparation of the manuscript for publication. Additionally, I greatly appreciate the courteous and immediate responses from your editorial office to all my inquiries. Best Regards, Dariusz Ziora
Dear Chrystine Mejia, Editorial Coordinator, Journal of Neurodegeneration and Neurorehabilitation, Auctores Publishing LLC, We would like to thank the editorial team for the smooth and high-quality communication leading up to the publication of our article in the Journal of Neurodegeneration and Neurorehabilitation. The reviewers have extensive knowledge in the field, and their relevant questions helped to add value to our publication. Kind regards, Dr. Ravi Shrivastava.
Dear Clarissa Eric, Editorial Coordinator, Journal of Clinical Case Reports and Studies, Auctores Publishing LLC, USA Office: +1-(302)-520-2644. I would like to express my sincere appreciation for the efficient and professional handling of my case report by the ‘Journal of Clinical Case Reports and Studies’. The peer review process was not only fast but also highly constructive—the reviewers’ comments were clear, relevant, and greatly helped me improve the quality and clarity of my manuscript. I also received excellent support from the editorial office throughout the process. Communication was smooth and timely, and I felt well guided at every stage, from submission to publication. The overall quality and rigor of the journal are truly commendable. I am pleased to have published my work with Journal of Clinical Case Reports and Studies, and I look forward to future opportunities for collaboration. Sincerely, Aline Tollet, UCLouvain.
Dear Ms. Mayra Duenas, Editorial Coordinator, International Journal of Clinical Case Reports and Reviews. “The International Journal of Clinical Case Reports and Reviews represented the “ideal house” to share with the research community a first experience with the use of the Simeox device for speech rehabilitation. High scientific reputation and attractive website communication were first determinants for the selection of this Journal, and the following submission process exceeded expectations: fast but highly professional peer review, great support by the editorial office, elegant graphic layout. Exactly what a dynamic research team - also composed by allied professionals - needs!" From, Chiara Beccaluva, PT - Italy.
Dear Maria Emerson, Editorial Coordinator, we have deeply appreciated the professionalism demonstrated by the International Journal of Clinical Case Reports and Reviews. The reviewers have extensive knowledge of our field and have been very efficient and fast in supporting the process. I am really looking forward to further collaboration. Thanks. Best regards, Dr. Claudio Ligresti
Dear Chrystine Mejia, Editorial Coordinator, Journal of Neurodegeneration and Neurorehabilitation. “The peer review process was efficient and constructive, and the editorial office provided excellent communication and support throughout. The journal ensures scientific rigor and high editorial standards, while also offering a smooth and timely publication process. We sincerely appreciate the work of the editorial team in facilitating the dissemination of innovative approaches such as the Bonori Method.” Best regards, Dr. Giselle Pentón-Rol.
