Biomedical knowledge discovery increasingly relies on computational tools to uncover patterns in complex datasets, yet generating explainable, evidence-based hypotheses about biological interactions remains challenging. This study introduces XAIPath, an interpretable pipeline that leverages biomedical knowledge graphs and Graph Neural Networks (GNNs) to uncover and explain novel drug-disease relationships, aiding applications such as drug repurposing. The pipeline combines GNN-based predictions with a post-hoc interpretability layer that extracts simple paths connecting drug and disease nodes in a biomedical knowledge graph and compares them using MinHash-based similarity. Similar paths are grouped via K-means clustering to build interpretable clusters that represent alternative mechanistic hypotheses. The method was applied to the NeDRex knowledge graph for drug indication prediction, with performance evaluated using AUROC, AUPRC, precision, sensitivity, and specificity. XAIPath achieved strong predictive performance, with AUROC exceeding 95% and AUPRC over 90% across training, validation, and test sets, while precision, sensitivity, and specificity all surpassed 85%. Most high-scoring predictions were supported by existing literature, and the extracted path clusters closely aligned with DrugMechDB annotations, supporting the plausibility of the generated hypotheses. Overall, XAIPath offers a scalable and explainable approach for identifying drug-disease associations, facilitating hypothesis generation and biological validation, and highlighting the value of explainable AI in biomedical research and drug repurposing.

Motivation Most diseases result from complex molecular interactions of genes and proteins. Various network-based methods characterize these mechanisms by expanding seed genes into disease modules. Their underlying algorithmic strategies differ, making it difficult to determine which of the created modules are most useful or biologically plausible.Results To address this challenge, we developed an all-in-one pipeline that handles installation, input preparation, execution, and systematic evaluation of six widely used module detection tools, considering module topology, functional coherence, robustness, and the capacity to recover seeds. To showcase the value of our pipeline and provide guidance to potential users, we conducted a comprehensive evaluation across 50 different disease-network combinations, revealing substantial variability among the derived disease modules, driven by both network and algorithm choices. We show that methods are robust to minor perturbations but struggle to recover omitted seeds. None consistently outperforms all others, underscoring the need for careful method selection. Our work enables the systematic comparison of disease module discovery approaches and promotes reproducible network medicine research. Integrated into the nf-core project, it is intended as an extendable, long-term resource for tracking progress in the field.Availability and Implementation The pipeline is implemented in Nextflow. Code and documentation are available through GitHub (https://github.com/nf-core/diseasemodulediscovery) and the nf-core website (https://nf-co.re/diseasemodulediscovery). Code and data used for demonstrating the pipeline are available through GitHub (https://github.com/REPO4EU/modulediscovery_demonstration).

Quality control constitutes a critical component of any next-generation sequencing (NGS) pipeline; however, most existing pipelines emphasize technical quality assessment (e.g. read quality, alignment metrics, duplication rates) while overlooking other equally important dimensions, such as sample identity verification, contamination detection, kinship analysis, and metadata concordance. Detecting issues like cross-sample contamination and sample swaps is essential to control data integrity. Here, we present NGSTroubleFinder, a novel tool to detect cross-sample contamination in human whole-genome and whole-transcriptome sequencing data, sample swaps, and mismatches between the reported and the inferred genetic and transcriptomic sexes. It can be run directly on BAM/CRAM files without requiring additional variant-calling steps and offers an integrated pipeline for ensuring quality control on NGS data, generated particularly within the context of clinical studies or research projects involving family members. It produces a detailed report that combines the results of its multiple analyses, including kinship, sex prediction, and contamination metrics. The tool reports extensive information on the samples, both in textual and HTML formats, including key plots for easy interpretation of the results. NGSTroubleFinder is written in Python and incorporates a custom-built parallelized pileup engine written in C, and it can be easily installed with pip. The tool source code and the models are freely available on GitHub (https://github.com/STALICLA-RnD/NGSTroubleFinder), and a containerized version is available on Docker Hub (https://hub.docker.com/r/staliclarnd/ngstroublefinder).

Repurposing off-patent drugs can be a potential source of low-cost treatments for patients with unmet medical needs. Here, we review the proposed new European Union (EU) pharmaceutical legislation in which two articles address drug repurposing. We find certain barriers hindering the adoption of these new incentives by academic and not-for-profit stakeholders, including lack of knowledge on regulatory aspects, pharmacovigilance, and restrictions in data protection. To further empower the intended stakeholders of the legislation, these initiatives can be strengthened by creating additional scientific, regulatory, and health technology assessment (HTA) support for not-for-profit repurposers, and by determining fair data protection periods and pricing policies. To support drug repurposing, Europe should work toward a comprehensive drug-repurposing strategy that fosters the repurposing of generic, shelved, and protected drugs.

Brain morphological abnormalities are common in patients with neurodevelopmental disorders (NDDs) and other neuropsychiatric disorders, often reflecting abnormal brain development and function. Genetic studies have found common genetic factors in NDDs and other neuropsychiatric disorders, although the etiology of brain structural changes in these disorders remains poorly understood. In this study, we analyzed magnetic resonance imaging (MRI) and genetic data from more than 30K individuals from the UK Biobank to evaluate whether NDD-risk copy number variants (CNVs) are also associated with neuroanatomical changes in both patients and neurotypical individuals. We found that the size differences in brain regions such as corpus callosum and cerebellum were associated with the deletions of specific areas of the human genome, and that specific neuroanatomical changes confer a risk of neuropsychiatric disorders. Furthermore, we observed that gene sets located in these genomic regions were enriched for pathways crucial for brain development and for phenotypes commonly observed in patients with NDDs. These findings highlight the link between CNVs, brain structure abnormalities, and the shared pathophysiology of NDDs and other neuropsychiatric disorders, providing new insights into the underlying mechanisms of these disorders and the identification of potential biomarkers for better diagnosis.

Network and Systems Medicine returns at a time when the discipline has advanced from conceptual foundations to demonstrated translational impact. Network pharmacology and systems approaches now provide clinical proof-of-concept, including synergistic interventions in ischemic stroke, mechanistic stratification of hypertension, and nutritional targeting in peripheral artery disease. These examples illustrate a shift from describing biological complexity to addressing causal mechanisms. Supported by initiatives such as REPO4EU and highlighted at RExPO’25, the journal will serve as a diamond open-access platform for sharing computational models, multi-omics integration, and drug repurposing strategies. Its mission is to accelerate collaboration and innovation across stakeholders and ensure that systems and network medicine deliver tangible patient benefit.

Drug discovery is both a long and expensive process, characterized by low success rates and high costs of development. By identifying new therapeutic applications for existing drugs, drug repurposing represents another faster and less expensive alternative using safety profiles and pharmacokinetic data that are already established. Nevertheless, the drug repurposing field faces obstacles such as data scarcely integrated with one another, a lack of insight into molecular mechanisms, and difficulties in the integration of different types of such data. Chemoinformatics addresses the gaps of repurposing drug information by employing methods such as ligand- and structure-based virtual screening, molecular docking, and pharmacophore modeling. A number of tools are available for identifying drug–target interactions, making a shift toward a polypharmacological perspective. The use of three-dimensional molecular descriptors enables more accurate screening, mainly accounting for the molecular conformation and complex interactions. On the other hand, machine learning and deep learning, by using large amounts of data, help to predict drug–target interaction and new therapeutic uses on an unprecedented scale. Recent advances, such as AlphaFold for protein folding and more recently interaction prediction, increase the accuracy of drug repurposing while accelerating the candidate hit discovery timelines. In this review, we highlight several chemoinformatics and machine learning approaches used for different drug development-related tasks and discuss how these approaches can guide drug repurposing to tackle complex diseases and rapidly address emerging health crises.

Computational drug repurposing integrates data from diverse sources, such as sequence databases, GWAS studies, or high-throughput screens. Depending on the original use case or field of research, they vary in availability, timeliness, and compatibility with other data sources. Further, numerous computational tools have been introduced designed to identify active disease modules, indications, or drug-target interactions that use different methods and strategies while not adhering to standard guidelines. Clearing and harmonising the resulting inconsistencies consume essential resources such that compiling a well-structured work plan is fundamental. This whitepaper demonstrates the results of a systematic review effort of about 400 publications and proposes valuable resources and specific strategies for the REPO4EU consortium. We present reviews, databases and computational methods by their applicability to work package-specific tasks and suggest using popular data standards such as FASTQ, SAM and VCF for sequencing data. In detail, we argue how NeDRexDB should serve as an instance of a knowledge base in this project, outline how to create a reproducible yet flexible pipeline for module discovery, and lay out the application of the BioPAX standard for disease module representation. Future challenges include establishing guidelines for computational drug repurposing, flexible and standardised workflows, and comprehensive in silico validation. We are confident that this work will provide a solid basis for tackling them.

The amount of scientific literature available is overwhelming, especially in fast-evolving fields like drug repurposing. Researchers face a significant challenge: staying up-to-date is nearly impossible due to the sheer volume of publications, databases, and tools available. This situation creates an urgent need for more efficient ways to access and integrate information from these articles. To tackle this problem, we developed "DrugRepoChatter", a tool designed to help researchers navigate the flood of recent literature on data sources, methods, and tools relevant for mechanism-based drug repurposing. DrugRepoChatter uses a vector database containing 285 open-access articles carefully selected by experts from the REPO4EU project to cover the latest developments in drug repurposing. The chatbot works by finding semantic similarities between a user’s question and the information stored in the database, effectively answering questions by drawing directly from the content of these articles. DrugRepoChatter is available as a web tool at https://apps.cosy.bio/drugrepochatter/ This tool makes the review process much more efficient and ensures researchers can quickly find relevant information. By facilitating faster access to scientific literature and enabling researchers to easily find the tools and information they need, DrugRepoChatter accelerates knowledge discovery. This tool not only streamlines the process of reviewing literature but also helps integrate scattered information into a cohesive, accessible format.

Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. The diagnosis of ASD is currently based on behavior criteria, which overlooks the diversity of genetic, neurophysiological, and clinical manifestations. Failure to acknowledge such heterogeneity has hindered the development of efficient drug treatments for ASD and other NDDs. DEPI® (Databased Endophenotyping Patient Identification) is a systems biology, multi-omics, and machine learning-driven platform enabling the identification of subgroups of patients with NDDs and the development of patient-tailored treatments. In this study, we provide evidence for the validation of a first clinically and biologically defined subgroup of patients with ASD identified by DEPI, ASD Phenotype 1 (ASD-Phen1). Among 313 screened patients with idiopathic ASD, the prevalence of ASD-Phen1 was observed to be ~24% in 84 patients who qualified to be enrolled in the study. Metabolic and transcriptomic alterations differentiating patients with ASD-Phen1 were consistent with an over-activation of NF-κB and NRF2 transcription factors, as predicted by DEPI. Finally, the suitability of STP1 combination treatment to revert such observed molecular alterations in patients with ASD-Phen1 was determined. Overall, our results support the development of precision medicine-based treatments for patients diagnosed with ASD.

Copy-number variants (CNVs) are genome-wide structural variations involving the duplication or deletion of large nucleotide sequences. While these types of variations can be commonly found in humans, large and rare CNVs are known to contribute to the development of various neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). Nevertheless, given that these NDD-risk CNVs cover broad regions of the genome, it is particularly challenging to pinpoint the critical gene(s) responsible for the manifestation of the phenotype. In this study, we performed a meta-analysis of CNV data from 11,614 affected individuals with NDDs and 4,031 control individuals from SFARI database to identify 41 NDD-risk CNV loci, including 24 novel regions. We also found evidence for dosage-sensitive genes within these regions being significantly enriched for known NDD-risk genes and pathways. In addition, a significant proportion of these genes was found to (1) converge in protein-protein interaction networks, (2) be among most expressed genes in the brain across all developmental stages, and (3) be hit by deletions that are significantly over-transmitted to individuals with ASD within multiplex ASD families from the iHART cohort. Finally, we conducted a burden analysis using 4,281 NDD cases from Decipher and iHART cohorts, and 2,504 neurotypical control individuals from 1000 Genomes and iHART, which resulted in the validation of the association of 162 dosage-sensitive genes driving risk for NDDs, including 22 novel NDD-risk genes. Importantly, most NDD-risk CNV loci entail multiple NDD-risk genes in agreement with a polygenic model associated with the majority of NDD cases.

Noncommunicable diseases (NCDs) have become globally abundant, yet the therapeutics we use for them are imprecise. In parallel, identifying new treatments has become more costly than ever due to the ever-aggravating efficacy crisis drug discovery faces. What unites these failures is our ontological classification of diseases, primarily based on descriptive terms. To achieve precision diagnosis and precision therapy in clinical practice, NCDs need to be redefined and subdivided based on their causal molecular mechanisms. However, the inconsistency and incompatibility of the current disease classification systems hinder data integration and analysis towards the characterization of such mechanisms. Here, we explain flaws in the current disease definitions and the dispersion among existing ontologies with the aim of establishing a mechanism-based classification of diseases hence, precision medicine.

Autism spectrum disorder (ASD) is a heterogenous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. Currently, ASD is diagnosed according to behavior-based criteria that overlook clinical and genomic heterogeneity, thus repeatedly resulting in failed clinical trials. Here, we summarize the scientific evidence pointing to the pressing need to create a precision medicine framework for ASD and other NDDs. We discuss the role of omics and systems biology to characterize more homogeneous disease subtypes with different underlying pathophysiological mechanisms and to determine corresponding tailored treatments. Finally, we provide recent initiatives towards tackling the complexity in NDDs for precision medicine and cost-effective drug discovery.

Alzheimer's disease (AD) and other forms of dementia are together a leading cause of disability and death in the aging global population, imposing a high personal, societal, and economic burden. They are also among the most prominent examples of failed drug developments. Indeed, after more than 40 AD trials of anti-amyloid interventions, reduction of amyloid-β (Aβ) has never translated into clinically relevant benefits, and in several cases yielded harm. The fundamental problem is the century-old, brain-centric phenotype-based definitions of diseases that ignore causal mechanisms and comorbidities. In this hypothesis article, we discuss how such current outdated nosology of dementia is a key roadblock to precision medicine and articulate how Network Medicine enables the substitution of clinicopathologic phenotypes with molecular endotypes and propose a new framework to achieve precision and curative medicine for patients with neurodegenerative disorders.

Neurodevelopmental disorders (NDDs) are a group of highly heterogenous and prevalent disorders characterized by abnormal brain development. These disorders remain an area of high unmet medical need due to the lack of specific pharmacological treatments addressing the core symptoms, especially social communication deficits. Most clinical trials in NDDs over the past decades have failed to show efficacy across patients recruited using behavior-based diagnosis despite bearing unrelated molecular pathophysiology. Here, we present the DEPI platform, STALICLA’s data-driven solution to meet the biological stratification needs in complex NDD populations. DEPI is the first systems biology and multi-omics based, AI module driven platform in the NDD space for the development of precision medicine-based treatments. It uses curated NDD-risk catalogs to identify pathway-level perturbations associated to clinical observations. Furthermore, it combines supervised and unsupervised machine learning methods to characterize molecular signatures (genetic, transcriptomic and metabolomic) across individuals through mechanistic endophenotyping. Importantly, the platform facilitates matching of the right treatments to the right patients with NDDs. The platform has been proven clinically successful by: i) identifying a first clinically actionable subgroup of patients with Autism Spectrum Disorder (ASD Phenotype 1), and the corresponding tailored treatment, STP1, with outstanding and unprecedented target engagement results in a Phase 1b clinical trial; and ii) blindly recalling known drug responder patients with sensitivity and specificity values ~80% for patients with fragile X syndrome.