Biology subjects

Gadiya, Y.

Publications and source records attributed to Gadiya, Y..

16 recordsLinked to original sources

bioRxiv2026

TIMS-Bench: Towards community standards for benchmarking untargeted trapped ion mobility metabolomics tools and datasets

Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics is an important technology for unbiased discovery of small molecules in biomedical (e.g., drug discovery to diagnostics), animal, plant, environmental, and microbial research. Over the past decade, ion mobility has added an additional dimension to the triplet of MS1, MS2, and retention time, helping resolve co-eluting or isomeric features in an LC-MS/MS that aid in compound identification. Here, we focused on evaluating the current trapped ion mobility spectrometry (TIMS)-amenable feature-finding tools (MZmine 4.9, MS-DIAL 5.5, and MetaboScape 2025 14.0.3) for pre-processing of metabolomics data generated using a popular ion mobility mass spectrometry (IM-MS) technique, TIMS. We leveraged ten public and three benchmark TIMS datasets to evaluate these tools for their strengths and weaknesses. Our results show that MZmine consistently identified the highest number of features and confidently annotated features; however, this performance was accompanied by an increased number of false positives, due to peak splitting, as well as reduced accuracy in collision cross section (CCS) measurements. In contrast, MetaboScape achieved the highest fraction of high-quality MS2 spectra, reflecting a more conservative feature detection strategy. MS-DIAL demonstrated balanced performance, identifying features that other tools missed. Finally, we publicly release the ground-truth datasets and code to support future developments in improving IMS data analysis.

bioinformatics↗

bioRxiv2025

From Library to Landscape: Integrative Annotation Workflows for Compound Libraries in Drug Repurposing

In the rapidly advancing landscape of drug discovery and repurposing, efficient access and integration of chemical and bioactivity data from public repositories has become essential. We implemented two complementary annotation pipelines (KNIME- and Python-based) designed to automate the extraction and integration of curated chemical and bioactivity data from public repositories. These pipelines are adaptable to any user-provided compound library, allowing reproducible workflows to integrate data from heterogeneous sources (e.g., ChEMBL and PubChem). As part of the REMEDi4ALL project, which aims to establish a European platform for drug repurposing, we validated our framework on a harmonized subset of the Specs repurposing collection (over 5000 compounds, available in-house). Additionally, we developed two interactive dashboards that support multilayered analyses and visualization by integrating chemical properties, bioactivity profiles, and relational data. We show how this framework streamlines the collection of harmonized data and facilitates analyses that are critical in drug repurposing efforts, while remaining versatile for broader applications in drug discovery. Both pipeline protocols are publicly available online, and the dashboards are open access.

bioinformatics↗

bioRxiv2025

Interdisciplinary Study on Drug-Induced-Phospholipidosis of Repurposing Libraries through Machine Learning and Experimental Evaluation in Different Cell Lines

Bigger PicturePhospholipidosis is a cellular condition characterized by the excessive accumulation of phospholipids within cells, that also can be induced by medications--especially those known as cationic amphiphilic drugs. While this phenomenon is not always harmful in itself, it can interfere with how cells respond in laboratory experiments and complicate the interpretation of drug screening results, leading to potential delays or failures in the development of new therapies. As the pharmaceutical industry increasingly relies on high-throughput screening and machine learning to accelerate early-stage drug discovery, there is growing interest in identifying compounds that may cause phospholipidosis before they move too far through the development pipeline. In response to this challenge, we at the EU-Horizon HLTH Remedi4ALL project compiled and analyzed one of the most comprehensive datasets to date--over 5,000 repurposed drugs tested across multiple cell lines--to better understand which compounds are likely to induce phospholipidosis. Using this dataset, we developed a machine learning model capable of predicting the risk of phospholipidosis based on a drugs chemical structure. By validating our approach across diverse experimental settings, our goal is to provide the broader scientific and pharmaceutical communities with a practical tool to flag problematic compounds early, ultimately helping bring safer, more effective treatments to patients faster. Highlights- A comprehensive dataset for a repurposing collection, comprising 5,228 compounds, for induction of phospholipidosis in A549-ACE2 and Vero-E6 cells -A machine learning model developed using in-house experimental phospholipidosis data across multiple cell lines -Exploration of chemical features that induce phospholipidosis and investigation of the relationships underlying different cell line susceptibilities -A case study: correlation of drug-induced phospholipidosis with antiviral compound effects against SARS-CoV-2 using phenotypic screening data Phospholipidosis (PLD), a cellular adverse effect that is, among others, caused by numerous cationic amphiphilic drugs. Interest is raised within pharma discovery to predict this phenomenon, as it can impact the outcome of phenotypic cellular screens and significantly delay drug development processes. The development of accurate and validated machine learning models for predicting drug-induced PLD across different cell lines and research centers could provide a valuable early application tool for the pharmaceutical industry, potentially accelerating drug discovery and reducing the risk of late-stage failures. We report here the assembly, curation, testing and modeling of one of the largest datasets of repurposed drugs (5K+) tested for PLD induction on different cell lines. A machine-learning classification method was developed and validated to predict whether molecules are prone to induce PLD effects when applied in cell-based screens. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/663638v1_ufig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@1c4760org.highwire.dtl.DTLVardef@92084dorg.highwire.dtl.DTLVardef@15effbaorg.highwire.dtl.DTLVardef@1e73f4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

molecular biology↗

bioRxiv2025

Defining the limits of plant chemical space: challenges and estimations

The plant kingdom, encompassing nearly 400,000 known species, produces an immense diversity of metabolites, including primary compounds essential for survival and secondary metabolites specialized for ecological interactions. These metabolites constitute a vast and complex phytochemical space with significant potential applications in medicine, agriculture, and biotechnology. However, much of this chemical diversity remains unexplored, as only a fraction of plant species have been studied comprehensively. In this work, we estimate the size of the plant chemical space by leveraging large-scale metabolomics and literature datasets. We begin by examining the known chemical space, which, while containing at most several hundred thousand unique compounds, remains sparsely covered. Using data from over 1,000 plant species, we apply various mass spectrometry-based approaches--a formula prediction model, a de novo prediction model, a combination of library search and de novo prediction, and MS2 clustering--to estimate the number of unique structures. Our methods suggest that the number of unique compounds in the metabolomics dataset alone may already surpass existing estimates of plant chemical diversity. Finally, we project these findings across the entire plant kingdom, conservatively estimating that the total plant chemical space likely spans millions, if not more, with the vast majority still unexplored.

bioinformatics↗

bioRxiv2024

Predicting antimicrobial class specificity of small molecules using machine learning

Whilst the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine learning (ML) could be one potential solution to help reduce the high costs and complexity of antibiotic drug discovery and attract collaboration across organizations. In our work, we developed a large antimicrobial knowledge graph (AntiMicrobial-KG) as a repository for collecting and visualizing public in-vitro antibacterial assay. Utilizing this data, we build ML models to efficiently scan compound libraries to identify compounds with the potential to exhibit antimicrobial activity. Our strategy involved training seven classic ML models across six compound fingerprint representations, of which the Random Forest trained on the MHFP6 fingerprint outperformed, demonstrating an accuracy of 75.9% and Cohens Kappa score of 0.68. Finally, we illustrated the models applicability for predicting the antimicrobial properties of two small molecule screening libraries. Firstly, the EU-OpenScreen library was tested against a panel of Gram-positive, Gram-negative, and Fungal pathogens. Here, we unveiled that the model was able to correctly predict more than 30% of active compounds for Gram-positive, Gram-negative, and Fungal pathogens. Secondly, with the Enamine library, a commercially available HTS compound collection with claimed antibacterial properties, we predicted its antimicrobial activity and pathogen class specificity. These results may provide a means for accelerating research in AMR drug discovery efforts by carefully filtering out compounds from commercial libraries with lower chances of being active.

bioinformatics↗

bioRxiv2024

From spreadsheet lab data templates to knowledge graphs: A FAIR data journey in the domain of AMR research

While awareness of FAIR (Findable, Accessible, Interoperable, and Reusable) principles has expanded across diverse domains, there remains a notable absence of impactful narratives regarding the practical application of FAIR data. This gap is particularly evident in the context of in-vitro and in-vivo experimental studies associated with the drug discovery and development process. Despite the structured nature of these data, reliance on classic methods such as spreadsheet-based visualization and analysis has limited the long-term reuse opportunities for such datasets. In response to this challenge, our work presents a representative journey towards FAIR data, characterized by structured, conventional spreadsheet-based lab data templates and the adoption of a knowledge graph framework for breaking data silos in the field of early antimicrobial resistance research. Here, we illustrate a tailored application of a "FAIRification framework" facilitating the practical implementation of FAIR principles. By showcasing the feasibility and benefits of transitioning to FAIR data practices, our work aims to encourage broader adoption and integration of FAIR principles within a research lab setting.

bioinformatics↗

bioRxiv2023

Medicinal chemistry lens on patent data in SureChEMBL

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

bioinformatics↗

bioRxiv2023

Pharmacophoric-based ML model to filter candidate E3 ligands and predict E3 Ligase binding probabilities

Among the plethora of E3 Ligases, only a few have been utilized for the novel PROTAC technology. However, extensive knowledge of the preparation of E3 ligands and their utilization for PROTACs is already present in several databases. Here we provide, together with an analysis of functionalized E3 ligands, a comprehensive list of trained ML models to predict the probability to be an E3 ligase binder. We compared the different algorithms based on the different description schemes used and identified that the pharmacophoric-based ML approach was the best. Due to the peculiar pharmacophores present in E3 ligase binders and the presence of an explainable model, we were able to show the capability of our ErG model to filter compound libraries for fast virtual screening or focused library design. A particular focus was also given to target E3 ligase prediction and to find a subset of candidate E3 ligase binders within known public and commercial compound collections.

bioinformatics↗

bioRxiv2023

Pharmaceutical patent landscaping: A novel approach to understand patents from the drug discovery perspective

Patents play a crucial role in the drug discovery process by providing legal protection for discoveries and incentivising investments in research and development. By identifying patterns within patent data resources, researchers can gain insight into the market trends and priorities of the pharmaceutical and biotechnology industries, as well as provide additional perspectives on more fundamental aspects such as the emergence of potential new drug targets. In this paper, we used the patent enrichment tool, PEMT, to extract, integrate, and analyse patent literature for rare diseases (RD) and Alzheimers disease (AD). This is followed by a systematic review of the underlying patent landscape to decipher trends and applications in patents for these diseases. To do so, we discuss prominent organisations involved in drug discovery research in AD and RD. This allows us to gain an understanding of the importance of AD and RD from specific organisational (pharmaceutical or university) perspectives. Next, we analyse the historical focus of patents in relation to individual therapeutic targets and correlate them with market scenarios allowing the identification of prominent targets for a disease. Lastly, we identified drug repurposing activities within the two diseases with the help of patents. This resulted in identifying existing repurposed drugs and novel potential therapeutic approaches applicable to the indication areas. The study demonstrates the expanded applicability of patent documents from legal to drug discovery, design, and research, thus, providing a valuable resource for future drug discovery efforts. Moreover, this study is an attempt towards understanding the importance of data underlying patent documents and raising the need for preparing the data for machine learning-based applications.

scientific communication and education↗

bioRxiv2022

Monkeypox Knowledge Graph: A comprehensive representation embedding chemical entities and associated biology of Monkeypox

SummaryThe outbreak of Monkeypox virus (MPXV) infection in May 2022 is declared a global health emergency by WHO. As of 28th July, 21067 cases have been confirmed and the numbers are on the rise. Unfortunately, MPXV pathophysiology and its underlying mechanisms are not yet understood. Likewise, the knowledge of biochemicals and drugs used against MPXV and their downstream effects is sparse. In this work, using Knowledge Graph (KG) representations we have depicted chemical and biological aspects of MPXV. To achieve this, we have collected and rationally assembled several biological study results, assays, drug candidates, and preclinical evidence to form a dynamic and comprehensive network. The KG is compliant with FAIR annotations allowing seamless transformation and integration to/with other formats and infrastructures. Availability and implementationThe Monkeypox knowledge graph is publicly available at https://github.com/Fraunhofer-ITMP/mpox-kg Supplementary informationSupplementary data are available at Bioinformatics online. ContactReagon.Karki@itmp.fraunhofer.de

systems biology↗

bioRxiv2022

PEMT: A patent enrichment tool for drug discovery

MotivationDrug discovery practitioners in industry and academia use semantic tools to extract information from online scientific literature to generate new insights into targets, therapeutics and diseases. However, due to complexities in access and analysis, patent-based literature is often overlooked as a source of information. As drug discovery is a highly competitive field, naturally, tools that tap into patent literature can provide any actor in the field an advantage in terms of better informed decision making. Hence, we aim to facilitate access to patent literature through the creation of an automatic tool for extracting information from patents described in existing public resources. ResultsHere, we present PEMT, a novel patent enrichment tool, that takes advantage of public databases like ChEMBL and SureChEMBL to extract relevant patent information linked to chemical structures and/or gene names described through FAIR principles and metadata annotations. PEMT aims at supporting drug discovery and research by establishing a patent landscape around genes of interest. The pharmaceutical focus of the tool is mainly due to the subselection of International Patent Classification (IPC) codes, but in principle, it can be used for other patent fields, provided that a link between a concept and chemical structure is investigated. Finally, we demonstrate a use-case in rare diseases by generating a gene-patent list based on the epidemiological prevalence of these diseases and exploring their underlying patent landscapes. Availability and implementationPEMT is an open-source Python tool and its source code and PyPi package are available at https://github.com/Fraunhofer-ITMP/PEMT and https://pvpi.org/project/PEMT/ respectively. Contactyojana.gadiya@itmp.fraunhofer.de Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two applications showing how one can deconvolute the predictions made by RPath as well as predict novel targets. Finally, we have made the source code and data publicly available at https://github.com/enveda/RPath.

bioinformatics↗

bioRxiv2021

DecoPath: A web application for decoding pathway enrichment analysis

The past two decades have brought a steady growth of pathway databases and pathway enrichment methods. However, the advent of pathway data has not been accompanied by an improvement with regards to interoperability across databases, thus, hampering the use of pathway knowledge from multiple databases for pathway enrichment analyses. While integrative databases have attempted to address this issue by collating pathway knowledge from multiple resources, these approaches do not account for redundant information across them. On the other hand, the majority of studies that employ pathway enrichment analyses still rely upon a single database, though the use of another resource could yield differing results, which is similarly the case when different pathway enrichment methods are employed. These shortcomings call for approaches that investigate the differences and agreements across databases and enrichment methods as their selection in the experimental design of a pathway analysis can be a crucial first step in ensuring the results of such an analysis are meaningful. Here we present DecoPath, a web application to assist in the interpretation of the results of pathway enrichment analysis. DecoPath provides an ecosystem to run pathway enrichment analysis or directly upload results and facilitate the interpretation of these results with custom visualizations that highlight the consensus and/or discrepancies at the pathway- and gene-levels. DecoPath is available at https://decopath.scai.fraunhofer.de and its source code and documentation can be found on GitHub at https://github.com/DecoPath/DecoPath.

bioinformatics↗

bioRxiv2020

The COVID-19 PHARMACOME: Rational Selection of Drug Repurposing Candidates from Multimodal Knowledge Harmonization

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific communitys massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2 / COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

bioinformatics↗

bioRxiv2020

Unravelling the debate on heme effects in COVID-19 infections

The SARS-CoV-2 outbreak was recently declared a worldwide pandemic. Infection triggers the respiratory tract disease COVID-19, which is accompanied by serious changes of clinical biomarkers such as hemoglobin and interleukins. The same parameters are altered during hemolysis, which is characterized by an increase in labile heme. We present two approaches that aim at analyzing a potential link between available heme and COVID-19 pathogenesis. Four COVID-19 related proteins, i.e. the host cell proteins ACE2 and TMPRSS2 as well as the viral protein 7a and S protein, were identified as potential heme binders. We also performed a detailed analysis of the common pathways induced by heme and SARS-CoV-2 by superimposition of knowledge graphs covering heme biology and COVID-19 pathophysiology. Herein, focus was laid on inflammatory pathways, and distinct biomarkers as the linking elements. Finally, the results substantially improve our understanding of COVID-19 infections and disease progression of patients with different clinical backgrounds and expand the diagnostic and treatment options.

bioinformatics↗

bioRxiv2020

COVID-19 Knowledge Graph: a computable, multi-modal, cause-and-effect knowledge model of COVID-19 pathophysiology

SummaryThe past few weeks have witnessed a worldwide mobilization of the research community in response to the novel coronavirus (COVID-19). This global response has led to a burst of publications on the pathophysiology of the virus, yet without coordinated efforts to organize this knowledge, it can remain hidden away from individual research groups. By extracting and formalizing this knowledge in a structured and computable form, as in the form of a knowledge graph, researchers can readily reason and analyze this information on a much larger scale. Here, we present the COVID-19 Knowledge Graph, an expansive cause-and-effect network constructed from scientific literature on the new coronavirus that aims to provide a comprehensive view of its pathophysiology. To make this resource available to the research community and facilitate its exploration and analysis, we also implemented a web application and released the KG in multiple standard formats. AvailabilityThe COVID-19 Knowledge Graph is publicly available under CC-0 license at https://github.com/covid19kg and https://bikmi.covid19-knowledgespace.de. Contactalpha.tom.kodamullil@scai.fraunhofer.de Supplementary informationSupplementary data are available online.

bioinformatics↗