This statistic shows the total number of drugs in the R&D pipeline worldwide from 2001 to 2025. In 2001, there were ***** drugs in the R&D pipeline, whereas there were ****** drugs in the pipeline in January 2025.

A database providing information about compounds under investigation for therapeutic use. There are currently 44 compounds on record with 100 compounds in the process of being uploaded to the database.

One of the key requirements for incorporating machine learning (ML) into the drug discovery process is complete traceability and reproducibility of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing ML models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of ML and molecular featurization tools. We have benchmarked AMPL on a large collection of pharmaceutical data sets covering a wide range of parameters. Our key findings indicate that traditional molecular fingerprints underperform other feature representation methods. We also find that data set size correlates directly with prediction performance, which points to the need to expand public data sets. Uncertainty quantification can help predict model error, but correlation with error varies considerably between data sets and model types. Our findings point to the need for an extensible pipeline that can be shared to make model building more widely accessible and reproducible. This software is open source and available at: https://github.com/ATOMconsortium/AMPL.

Success.ai’s Pharma Data for the Global Pharmaceutical Industry provides a robust dataset tailored for businesses looking to connect with pharmaceutical companies, decision-makers, and key stakeholders worldwide. Covering pharmaceutical manufacturers, research organizations, biotech firms, and distributors, this dataset offers verified SIC codes, firmographic details, and contact information for executives and operational leads.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and business development strategies are driven by reliable, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is essential for navigating the competitive global pharmaceutical landscape.

Why Choose Success.ai’s Pharma Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of pharmaceutical executives, R&D leads, compliance officers, and procurement managers.
   • AI-driven validation ensures 99% accuracy, optimizing your campaigns and improving communication efficiency.

2. Comprehensive Coverage of the Global Pharmaceutical Sector

   • Includes profiles of pharmaceutical companies, biotech firms, contract manufacturing organizations (CMOs), and distributors across North America, Europe, Asia, and other major markets.
   • Gain insights into regional pharmaceutical trends, product pipelines, and market dynamics unique to global markets.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, mergers, product launches, and regulatory compliance shifts.
   • Stay aligned with the fast-paced pharmaceutical industry to capitalize on emerging opportunities and maintain relevance.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers, R&D specialists, and operational leaders in the pharmaceutical industry worldwide.
• 30M Company Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Verified SIC Codes: Understand industry classifications and product specializations to refine your targeting strategies.
• Leadership Contact Details: Connect with CEOs, COOs, medical directors, and regulatory managers influencing pharmaceutical operations.

Key Features of the Dataset:

1. Decision-Maker Profiles in Pharmaceuticals

   • Identify and engage with professionals overseeing R&D, clinical trials, supply chains, and regulatory compliance.
   • Target leaders responsible for drug development, vendor selection, and market entry strategies.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (biotech, generic pharmaceuticals, vaccines), geographic location, or revenue size.
   • Tailor campaigns to align with specific needs such as drug pipeline acceleration, production scaling, or market expansion.

3. SIC Codes and Firmographic Insights

   • Access verified SIC codes and detailed company profiles to understand market focus, operational scale, and specialization areas.
   • Use firmographic data to prioritize high-value targets and align product offerings with market demands.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with pharmaceutical stakeholders.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer technology solutions, research tools, or contract services to pharmaceutical manufacturers, R&D facilities, and distribution networks.
   • Build relationships with procurement teams and compliance officers responsible for vendor approvals and operational excellence.

2. Market Research and Product Development

   • Analyze global pharmaceutical trends, drug approval patterns, and regulatory frameworks to guide product innovation and market entry strategies.
   • Identify high-growth regions and emerging therapeutic areas to focus your resources effectively.

3. Partnership and Supply Chain Development

   • Connect with pharmaceutical companies seeking contract manufacturing, raw material sourcing, or distribution partnerships.
   • Foster alliances that streamline production, ensure quality, and accelerate time-to-market.

4. Regulatory Compliance and Risk Mitigation

   • Engage with regulatory officers and compliance managers overseeing adherence to local and international pharmaceutical standards.
   • Present solutions for documentation, reporting, and risk management to ensure compliance and operational efficiency.

Why Choose Success.ai?

1. Best Price Guarantee
   ...

Drug Delivery Infusion Systems are used to administer medication to a patient in critical care, emergency care, home care and trauma centers. GlobalData's Medical Devices sector report, “Drug Delivery Infusion Systems – Medical Devices Pipeline Assessment, 2016' provides comprehensive information about the Drug Delivery Infusion Systems pipeline products with comparative analysis of the products at various stages of development and information about the clinical trials which are in progress. The Drug Delivery Infusion Systems Pipeline Assessment report provides key information and data related to: Extensive coverage of the Drug Delivery Infusion Systems under development Review details of major pipeline products which include product description, licensing and collaboration details and other developmental activities including pipeline territories, regulatory paths and estimated approval dates Reviews of major players involved in the pipeline product development. Provides key clinical trial data related to ongoing clinical trials such as trial phase, trial status, trial start and end dates, and, the number of trials of the major Drug Delivery Infusion Systems pipeline products. Review of Recent Developments in the segment / industry The Drug Delivery Infusion Systems Pipeline Assessment report enables you to: Access significant competitor information, analysis, and insights to improve your R&D strategies Identify emerging players with potentially strong product portfolio and create effective counter-strategies to gain competitive advantage Identify and understand important and diverse types of Drug Delivery Infusion Systems under development Formulate market-entry and market expansion strategies Plan mergers and acquisitions effectively by identifying major players with the most promising pipeline The major companies covered in the “Drug Delivery Infusion Systems – Medical Devices Pipeline Assessment, 2016” report: 410 Medical Innovation, LLC Acuros GmbH AktiVax Inc. Alnylam Pharmaceuticals, Inc. Anesthesia Safety Products, LLC AngioDynamics, Inc. Automedics Medical Systems Baxter International Inc. BioCardia, Inc. Debiotech S.A. Delpor, Inc. Edwards Lifesciences Corporation Eksigent Technologies, LLC Eli Lilly and Company Flowonix Medical, Inc. FluidSynchrony, LLC Fluonic, Inc. Fresenius Kidney Care Imagnus Biomedical Inc. Innovfusion Pte. Ltd. Intarcia Therapeutics, Inc. IRadimed Corporation Ivenix, Inc. LifeMedix, LLC Lynntech, Inc. Medallion Therapeutics, Inc. Medical Device Creations Ltd. MedPrime Technologies Pvt. Ltd. Mercator MedSystems, Inc. Nano Precision Medical NexGen Medical Systems, Inc. Nipro Corporation Owen Mumford Limited Pavmed Inc PRO-IV Medical Ltd. Ratio, Inc. Rice University SteadyMed Therapeutics, Inc. StnDrd Infusion Corporation Tel Aviv University Terumo Corporation ToucheMedical Ltd. Unilife Corporation University of Minnesota University of Southern California Note: Certain sections in the report may be removed or altered based on the availability and relevance of data in relation to the equipment type. The GlobalData Differentiation This report is prepared using data sourced from in-house databases, secondary and primary research by GlobalData's team of industry experts. The data and analysis within this report are driven by GlobalData Medical Equipment (GDME) databases. GlobalData Medical Equipment database gives you comprehensive information required to drive sales, investment and deal-making activity in your business. It includes the following: 15,000+ data tables showing market size across more than 780 medical equipment segments and 15 countries, from 2007 and forecast to 2021 10,000+ primary interviews, conducted annually to ensure data and report quality 1,100+ medical equipment conference reports 2,000+ industry-leading reports per annum, covering growing sectors, market trends, investment opportunities and competitive landscape 600+ analysis reports, covering market and pipeline product analysis, by indication; medical equipment trends and issues, and investment and M&A trends 56,500+ medical equipment company profiles 4,100+ company profiles of medical equipment manufacturers in China and India 2,000+ company profiles of medical equipment manufacturers in Japan 825+ companies’ revenue splits and market shares 1,750+ quarterly and annual medical equipment company financials 700+ medical equipment company SWOTs 19,000+ pipeline product profiles 27,000+ marketed product profiles 33,000+ clinical trials 25,000+ trial investigators 20,600+ product patents 3,700+ reports on companies with products under development 21,500+ reports on deals in the medical equipment industry 1,300+ surgical and diagnostic procedures by therapy area 50+ key healthcare indicators by country For more information or to receive a free demonstration of the service, please visit: GlobalData Medical Custom Requirements Contact us to discuss the areas of your business where you need external input, and we will work with you to identify the strongest way forward to meet your needs. Read More

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MetaDrugs workflow
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Data analysis pipeline for investigating drug-host-microbiome relationships in cardiometabolic disease (MetaCardis cohort).

For questions and requests, please contact:
Sofia K. Forslund (sofia.forslund@mdc-berlin.de)
and Till Birkner (till.birkner@mdc-berlin.de)

*******************************************************************
Contents:
-------------------------------------------------------------------
Data files:
metadata.tar.gz - archived cohort metadata files*
input_features.tar.gz - archived preprocessed serum and urine metabolome and gut microbiome features
output_complete.tar.gz - archived example analysis output files for each of the input feature file
output_rerun.tar.gz - archived empty directory for generating test output files as described in this document

*Please note: Due to conflicts with Danish Data Protection laws, metadata from the Danish subset of the cohort were removed in this repository. Please reach out for a potential case-by-case access request for access to the complete set of metadata.
-------------------------------------------------------------------
Text files:
archived in feature_names.tar.gz:
atcs_names - full names for atcs drug compounds
contrast_names - full names for disease comparison groups
file_names - brief description of the files in input_features folder
gmm_names - full names of GMM modules
kegg_names - full names of KEGG modules
ko_names - full names of KO modules
metadata_names - full names of metadata features
mOTU_names - species names for metagenomics data
taxon_names - taxon names for metagenomics data
-------------------------------------------------------------------
Scripts:
-------------------------------------------------------------------
runFrame.r - main wrapper script envoking the analysis pipeline
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runFrame_rel_comb.r - script calculating drug combination effects
runFrame_rel.r - script calculating dosage effects
testCombPresenceSeparate.r - testing of significant drug combination effects beyond single drug effects
testDosagePresenceSeparate.pl - testing of significant drug dosage effects beyond single drug effects
testDosagePresenceSeparateNegative.pl - testing of unique drug dosage effects beyond single drug effects
-------------------------------------------------------------------
prettifyResults_uncollapsed.pl - wrapper scripts to create and format a single analysis output file
makeTables.r - wrapper script to make excel tables with analysis results
-------------------------------------------------------------------
Example output file:
-------------------------------------------------------------------
output_all_formatted_noc_uncollapsed_complete.tsv - contains all disease-drug-host-microbiome feature analysis results in one place.
*******************************************************************

100 random FORESEE pipelines were chosen and then trained on all 266 available drugs in the GDSC database to predict the patient data sets. (XLSX)

Injectable Drug Delivery Devices are used for administration of a drug into patient’s blood through a delivery device. GlobalData's Medical Devices sector report, “Injectable Drug Delivery – Medical Devices Pipeline Assessment, 2017" provides comprehensive information about the Injectable Drug Delivery pipeline products with comparative analysis of the products at various stages of development and information about the clinical trials which are in progress. Read More

We developed a single-cell transcriptomics pipeline for high-throughput pharmacotranscriptomic screening. We explored the transcriptional landscape of three HGSOC models (JHOS2, a representative cell line; PDC2 and PDC3, two patient-derived samples) after treating their cells for 24 hours with 45 drugs representing 13 distinct classes of mechanism of action. Our work establishes a new precision oncology framework for the study of molecular mechanisms activated by a broad array of drug responses in cancer. . ├── 3D UMAPs/ → Interactive 3D UMAPs of cells treated with the 45 drugs used for multiplexed scRNA-seq. Related to Figure 4. Coordinates: x = UMAP 1; y = UMAP 2; z = UMAP 3. Legend: green = PDC1; blue = PDC2; red = JHOS2. │ ├── DMSO_3D_UMAP_Dini.et.al.html → 3D UMAP of untreated cells. │ └── drug_3D_UMAP_Dini.et.al.html → 3D UMAP of cells treated with (drug). ├── QC_plots/ → Diagnostic plots. Related to Figures 2–4. │ ├── model_QC_violin_plot_2023.pdf → Violin plots of the QC metrics used to filter the data. │ ├── model_col_HTO or model_row_HTO before and after filt → Heatmaps of the row or column HTO expression in each cell. │ └── model_counts_histogram_2023.pdf → Histogram of the distribution of the total counts per cell after filtering for high-quality cells. ├── scRNAseq/ → scRNA-seq data. Related to Figures 2–4. │ ├── AllData_subsampled_DGE_edgeR.csv.gz → Differential gene expression analyses results between treated and untreated cells via pseudobulk of aggregate subsamples, for each of the three models. Related to Figure 3. │ └── All_vs_all_RNAclusters_DEG_signif.txt → Differential gene expression analysis results (p.adj < 0.05) of FindAllMarkers for the Leiden/RNA clusters. ├── PDCs.transcript.counts.tsv → Bulk RNA-seq count data for PDCs 1–3 processed by Kallisto. Related to Figure S6. └── PDCs.transcript.TPM.tsv → Bulk RNA-seq TPM data for PDCs 1–3 processed by Kallisto. Related to Figure S6.

PHMSA regulations in 49 CFR Part 199 require operators of pipelines, liquefied natural gas plants, and underground natural gas storage facilities to submit data to the DOT DAMIS. Operators with less than 50 covered employees are required to submit once every three years, not annually.

Success.ai’s Prospect Data for Biotechnology & Pharmaceutical Innovators Globally provides a powerful dataset designed to connect businesses with key players driving innovation in the biotech and pharmaceutical industries worldwide. Covering companies engaged in drug development, biotechnology research, and life sciences innovation, this dataset offers verified profiles, professional histories, work emails, and phone numbers of decision-makers and industry leaders.

With access to over 700 million verified global profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and partnership efforts are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is indispensable for navigating the fast-evolving biotech and pharmaceutical landscape.

Why Choose Success.ai’s Prospect Data for Biotech and Pharmaceutical Innovators?

1. Verified Contact Data for Industry Professionals

   • Access verified work emails, phone numbers, and LinkedIn profiles of executives, R&D leads, compliance officers, and procurement managers in the biotech and pharmaceutical sectors.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and maximizing communication efficiency.

2. Comprehensive Coverage Across Global Markets

   • Includes profiles of professionals and companies from North America, Europe, Asia-Pacific, and other emerging biotech and pharmaceutical markets.
   • Gain insights into global industry trends, drug development pipelines, and regional innovations.

3. Continuously Updated Datasets

   • Real-time updates capture leadership changes, research breakthroughs, funding activities, and regulatory compliance updates.
   • Stay ahead of market developments and align your strategies with industry dynamics.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with decision-makers, researchers, and executives in biotech and pharmaceutical industries worldwide.
• 30M Company Profiles: Access detailed firmographic data, including revenue ranges, research capacities, and operational footprints.
• Professional Histories: Gain insights into the expertise, career progressions, and roles of professionals driving innovation.
• Leadership Contact Information: Connect directly with CEOs, R&D heads, regulatory managers, and other key stakeholders shaping the future of life sciences.

Key Features of the Dataset:

1. Decision-Maker Profiles in Biotech and Pharmaceuticals

   • Identify and engage with professionals leading research, clinical trials, supply chains, and compliance efforts.
   • Target individuals responsible for strategic decisions in drug development, technology integration, and regulatory adherence.

2. Advanced Filters for Precision Targeting

   • Filter professionals and companies by industry focus (biotech, generics, vaccines), geographic region, revenue size, or workforce composition.
   • Tailor campaigns to address specific needs, such as drug discovery, manufacturing scalability, or market entry.

3. Research and Innovation Insights

   • Access data on research priorities, product pipelines, and innovation trends across global biotech and pharmaceutical sectors.
   • Leverage these insights to position your offerings effectively and uncover new opportunities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes with industry professionals.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technologies that accelerate R&D, streamline production, or ensure compliance to biotech and pharmaceutical companies.
   • Build relationships with procurement teams, regulatory managers, and R&D heads managing budgets and resource allocation.

2. Market Research and Competitive Analysis

   • Analyze global trends in biotechnology and pharmaceuticals to guide product innovation and strategic planning.
   • Benchmark against competitors to identify market gaps, emerging niches, and high-growth opportunities.

3. Partnership Development and Licensing

   • Engage with organizations seeking strategic partnerships, co-development opportunities, or licensing agreements for drug development.
   • Foster alliances that drive mutual growth and innovation in life sciences.

4. Regulatory Compliance and Risk Mitigation

   • Connect with compliance officers and legal professionals overseeing regulatory adherence, clinical trials, and product approvals.
   • Offer solutions that simplify compliance reporting, risk management, and quality assurance processes.

Why Choose Success.ai?

1. Best Price...

The GLOBOCAN database revealed that there were 544,100 esophageal cancer deaths and 604,100 new cases worldwide in 2020, translating to age-standardized incidence and mortality rates of 6.3 and 5.6 per 100,000, respectively. Interestingly, men account for about 70% of instances, and there is a significant gender disparity in frequency worldwide, with men being impacted two to five times more frequently than women.

Injectable Drug Delivery is defined as administration of a drug into patient’s blood through a delivery device. GlobalData's Medical Devices sector report, “Injectable Drug Delivery – Medical Devices Pipeline Assessment, 2018" provides comprehensive information about the Injectable Drug Delivery pipeline products with comparative analysis of the products at various stages of development and information about the clinical trials which are in progress.
The Injectable Drug Delivery Pipeline Assessment report provides key information and data related to:
Extensive coverage of the Injectable Drug Delivery under development
Review details of major pipeline products which include product description, licensing and collaboration details and other developmental activities including pipeline territories, regulatory paths and estimated approval dates
Reviews of major players involved in the pipeline product development.
Provides key clinical trial data related to ongoing clinical trials such as trial phase, trial status, trial start and end dates, and, the number of trials of the major Injectable Drug Delivery pipeline products.
Review of Recent Developments in the segment / industry
The Injectable Drug Delivery Pipeline Assessment report enables you to:
Access significant competitor information, analysis, and insights to improve your R&D strategies
Identify emerging players with potentially strong product portfolio and create effective counter-strategies to gain competitive advantage
Identify and understand important and diverse types of Injectable Drug Delivery under development
Formulate market-entry and market expansion strategies
Plan mergers and acquisitions effectively by identifying major players with the most promising pipelineNote: Certain sections in the report may be removed or altered based on the availability and relevance of data in relation to the equipment type. Read More

The success of artificial intelligence (AI) models has been limited by the requirement of large amounts of high-quality training data, which is just the opposite of the situation in most drug discovery pipelines. Active learning (AL) is a subfield of AI that focuses on algorithms that select the data they need to improve their models. Here, we propose a two-phase AL pipeline and apply it to the prediction of drug oral plasma exposure. In phase I, the AL-based model demonstrated a remarkable capability to sample informative data from a noisy data set, which used only 30% of the training data to yield a prediction capability with an accuracy of 0.856 on an independent test set. In phase II, the AL-based model explored a large diverse chemical space (855K samples) for experimental testing and feedback. Improved accuracy and new highly confident predictions (50K samples) were observed, which suggest that the model’s applicability domain has been significantly expanded.

The Non-Alcoholic Steatohepatitis (NASH) Drug Pipeline market is increasingly recognized as a critical area of focus within the pharmaceutical industry. NASH, a progressive liver disease characterized by inflammation and fat accumulation in the liver, has seen a surge in prevalence, primarily linked to rising obesit

Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation of historical data into information-rich spectral databases. Meanwhile, molecular networking, a recent bioinformatic framework, provides global displays and system-level understanding of complex LC-MS/MS data sets. Herein we present meRgeION, a multifunctional, modular, and flexible R-based toolbox to streamline spectral database building, automated structural elucidation, and molecular networking. The toolbox offers diverse tuning parameters and the possibility to combine various algorithms in the same pipeline. As an open-source R package, meRgeION is ideally suited for building spectral databases and molecular networks from privacy-sensitive and preliminary data. Using meRgeION, we have created an integrated spectral database covering diverse pharmaceutical compounds that was successfully applied to annotate drug-related metabolites from a published nontargeted metabolomics data set as well as reveal the chemical space behind this complex data set through molecular networking. Moreover, the meRgeION-based processing workflow has demonstrated the usefulness of a spectral library search and molecular networking for pharmaceutical forced degradation studies. meRgeION is freely available at: https://github.com/daniellyz/meRgeION2.

The Cancer Vaccines Drug Pipeline market is rapidly evolving as a cornerstone of therapeutic innovation within the oncology landscape. With the global cancer burden continuing to rise, cancer vaccines have emerged as a promising solution to enhance the body's immune response against various cancer types. These vacci

The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.

Abstract:The current COVID-19 pandemic has highlighted the need for new and fast methods to identify novel or repurposed therapeutic drugs. Here we present a method for untargeted phenotypic drug screening of virus-infected cells, combining Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. We show that the methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with Human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state. The method can be used in drug discovery for morphological profiling of novel antiviral compounds on both infected and non-infected cells. Screen description: The images are of MRC-5 human lung fibroblasts infected with Human coronavirus 229E (CoV-229E) and treated with a panel of nine host- and virus-targeting antivirals. Cells are labelled with five labels that characterise seven cellular components (from the "Cell Painting" assay) as well as with a Coronavirus pan monoclonal antibody combined with a secondary antibody. This experiment consists of 5 plates. Each plate has 60 wells, and 9 fields of view per well. Each field was imaged in five channels (detection wavelengths), and each channel is stored as a separate, grayscale image file in TIFF format.The channel names (w1-w5) correspond to the following stains: w1 = Hoechst 33342 (HOECHST); w2= Coronavirus pan Monoclonal Antibody (FIPV3-70) + Goat Anti-Mouse IgG H&L secondary antibody (MITO); w3= Wheat Germ Agglutinin/Alexa Fluor 555 + Phalloidin/Alexa Fluor 568 (PHAandWGA); w4= SYTO 14 green (SYTO); w5= Concanavalin A/Alexa Fluor 488 (CONC)Organization of files:1) Raw image data:- MRC5_HCoV229_Plate1.tar.gz - MRC5_HCoV229_Plate2.tar.gz - MRC5_Plate3.tar.gz - MRC5_Plate4.tar.gz - MRC5_HCoV229_Plate5.tar.gz 2) Image analysis pipelines (CellProfiler 4.0.7):Cell Profiler project with a subset of images to try out the analysis pipeline:- Example_PipelineAndData.tar.gz Quality control, illumination correction and feature extraction pipelines:- AnalysisPipelines.tar.gz3) Extracted feature data:- features_MRC5_HCoV229_Plate1.tar.gz- features_MRC5_HCoV229_Plate2.tar.gz- features_MRC5_Plate3.tar.gz- features_MRC5_Plate4.tar.gz- features_MRC5_HCoV229_Plate5.tar.gzMetadata:The file “Metadata_MRC5_HCoV229E_plate1-5.csv“ contains the metadata in CSV format, with the following fields:- Plate_id: corresponds to the experimental plate- Well: well allocation in the 96-well plate- virus: "virus +" when cells are exposed to virus, and "virus -' for non-infected controls- Compound: name of compound- Dose [μM]: dose of compoundFor full information, see the manuscript to which this data is linked.

IntroductionIdentifying disease–target associations is a pivotal step in drug discovery, offering insights that guide the development and optimization of therapeutic interventions. Clinical trial data serves as a valuable source for inferring these associations. However, issues such as inconsistent data quality and limited interpretability pose significant challenges. To overcome these limitations, an integrated approach is required that consolidates evidence from diverse data sources to support the effective prioritization of biological targets for further research.MethodsWe developed a comprehensive data integration and visualization pipeline to infer and evaluate associations between diseases and known and potential drug targets. This pipeline integrates clinical trial data with standardized metadata, providing an analytical workflow that enables the exploration of diseases linked to specific drug targets as well as facilitating the discovery of drug targets associated with specific diseases. The pipeline employs robust aggregation techniques to consolidate multivariate evidence from multiple studies, leveraging harmonized datasets to ensure consistency and reliability. Disease–target associations are systematically ranked and filtered using a rational scoring framework that assigns confidence scores derived from aggregated statistical metrics.ResultsOur pipeline evaluates disease–target associations by linking protein-coding genes to diseases and incorporates a confidence assessment method based on aggregated evidence. Metrics such as meanRank scores are employed to prioritize associations, enabling researchers to focus on the most promising hypotheses. This systematic approach streamlines the identification and prioritization of biological targets, enhancing hypothesis generation and evidence-based decision-making.DiscussionThis innovative pipeline provides a scalable solution for hypothesis generation, scoring, and ranking in drug discovery. As an open-source tool, it is equipped with publicly available datasets and designed for ease of use by researchers. The platform empowers scientists to make data-driven decisions in the prioritization of biological targets, facilitating the discovery of novel therapeutic opportunities.