Comprehensive SAR Access
GOSTAR® streamlines academic research by offering a single, expansive SAR database, eliminating the need to consult multiple sources. Access over 9.7 million chemical structures and a wealth of SAR data, enabling a seamless transition from data gathering to breakthrough discovery. This database, designed by and for medicinal chemists, provides unparalleled coverage of the medicinal chemical space, fostering a deeper understanding and enabling forward-thinking research planning.
GOSTAR® Database by the Numbers
10
Chemical structures
120
Abstracts & Conferences
33
SAR Data
78
Companies & Institutes
77
Biological Targets
4
Patents Screened
9.7
Chemical structures
33
SAR Data
120
Abstracts & Conferences
78
Companies & Institutes
77
Biological Targets
4
Patents Screened
Advanced Research Tools
With GOSTAR®, academic researchers gain access to precision tools designed for in-depth drug design analysis. From molecular pair analysis to efficiency plots, these tools offer advanced capabilities, including detailed compound-target affinity data and comprehensive profiles spanning pharmacokinetics, efficacy, metabolism, and toxicity. Such a suite of tools facilitates the design and testing of novel compounds, guiding researchers through complex datasets and enhancing their discovery process.
Data Visualization & Analysis
Empower your research with GOSTAR®’s data visualization capabilities, such as intuitive heatmaps that provide immediate insights into potential off-target activities of drug candidates. This visual approach simplifies complex data interpretation, allowing researchers to swiftly draw meaningful conclusions and advance towards new therapeutic discoveries. GOSTAR® presents data in a user-friendly format, expediting the transition from concept to peer-reviewed journal publications.
Data Visualization & Analysis
Empower your research with GOSTAR®’s data visualization capabilities, such as intuitive heatmaps that provide immediate insights into potential off-target activities of drug candidates. This visual approach simplifies complex data interpretation, allowing researchers to swiftly draw meaningful conclusions and advance towards new therapeutic discoveries. GOSTAR® presents data in a user-friendly format, expediting the transition from concept to peer-reviewed journal publications.
Advance Academic Publishing
GOSTAR® significantly aids academic researchers in authoring distinguished publications. It offers a database replete with 9.7 million chemical structures and 33 million SAR points, providing robust support for high-quality research papers. This wealth of data enables a thorough validation of hypotheses and enhances the substantiation of scientific arguments, essential for journal articles, reviews, and systematic studies. GOSTAR®’s reliable and expansive dataset not only underpins detailed research findings but also facilitates the contribution of substantial advancements to scientific literature.
Publications Citing GOSTAR®
GOSTAR® is a trusted ally in academic research, consistently featured in key studies that shape the future of drug discovery. Its extensive data drives scientific innovation, as evidenced in the curated selection of publications below
CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules
- Authors: Xiaodan Yin, Xiaorui Wang, Yuquan Li, Jike Wang, Yuwei Wang, Yafeng Deng, Tingjun Hou, Huanxiang Liu, Pei Luo, and Xiaojun Yao
- Journal: Journal of Chemical Information and Modeling (JCIM)
- Date: Published on October 11, 2023
- DOI: https://doi.org/10.1021/acs.jcim.3c00685
Generative Molecular Design and Experimental Validation of Selective Histamine H1 Inhibitors
- Authors: Kevin S. McLoughlin*, Da Shi, Jeffrey E. Mast, John Bucci, John P. Williams, W. Derek Jones, Derrick Miyao, Luke Nam, Heather L. Osswald , Lev Zegelman, Jonathan Allen, Brian J. Bennion, Amanda K. Paulson, Ruben Abagyan, Martha S. Head, James M. Brase
- Journal: bioRxiv
- Date: Published on February 16, 2023
- DOI: https://www.biorxiv.org/content/10.1101/2023.02.14.528391v1
Paclitaxel binds and activates C5aR1: A new potential therapeutic target for the prevention of chemotherapy-induced peripheral neuropathy and hypersensitivity reactions
- Authors: Laura Brandolini, Michele d’Angelo, Rubina Novelli, Vanessa Castelli, Cristina Giorgio, Anna Sirico, Pasquale Cocchiaro, Francesco D’Egidio, Elisabetta Benedetti, Claudia Cristiano, Antonella Bugatti, Anna Ruocco, Pier Giorgio Amendola, Carmine Talarico, Candida Manelfi, Daniela Iaconis, Andrea Beccari, Andreza U. Quadros, Thiago M. Cunha, Arnaldo Caruso, Roberto Russo, Annamaria Cimini, Andrea Aramini, Marcello Allegretti
- Journal: Cell Death & Disease
- Date: May 25, 2022
- DOI: https://www.nature.com/articles/s41419-022-04964-w
A Blueprint for High Affinity SARS-CoV-2 Mpro Inhibitors from Activity-Based Compound Library Screening Guided by Analysis of Protein Dynamics
- Authors: Jonas Gossen, Simone Albani, Anton Hanke, Benjamin P. Joseph, Cathrine Bergh, Maria Kuzikov, Elisa Costanzi, Candida Manelfi, Paola Storici, Philip Gribbon, Andrea R. Beccari, Carmine Talarico, Francesca Spyrakis, Erik Lindahl, Andrea Zaliani, Paolo Carloni, Rebecca C. Wade, Francesco Musiani, Daria B. Kokh, and Giulia Rossetti*
- Journal: ACS Pharmacology & Translational Science
- Date: Published on March 16, 2021
- DOI: https://doi.org/10.1021/acsptsci.0c00215
The Tools to Provide Clarity
GOSTAR® enables early discovery in drug development by providing normalized compound–target affinity data as well as comprehensive pharmacokinetic, efficacy, metabolic, and toxicity profiles. It enables medicinal chemists to build powerful searches which provide detailed insight into the entire pharmaceutical space. Find a wealth of data in GOSTAR®.
The drug-target interaction heatmap facilitates decision-making about the potential off-target activity of drug candidates early in drug development and drug repurposing workflows. GOSTAR® presents these findings in a visually intuitive manner, allowing end-users to easily interpret the data and draw conclusions. Go from data to understanding faster with GOSTAR®.
Matched molecular pair analysis is a powerful tool for determining which compound to design and test next. Computer algorithms analyse data in an unbiased manner to create design guidelines that will allow medicinal chemists to propose better compounds that reduce the number of design cycles. GOSTAR® provides tools for determining the matched molecular pairs and analysing activity landscapes across compound datasets. Design more effective compounds with GOSTAR®.
Lipophilicity is an important factor in determining the binding affinity of a drug to protein targets, as well as influencing ADMET properties. As a result, the combination of high target potency and high lipophilicity may increase the likelihood of ADMET-related attrition. Therefore, medicinal chemistry optimization needs to be balanced and multidimensional. GOSTAR® empowers medicinal chemists to efficiently explore the property space of compounds against a variety of bioactivity endpoints. Discover your next breakthrough with GOSTAR®.
What our customers say
Excelra responds quickly to provide the most recent patent and journal data to the GOSTAR® database.
Hanjo Kim
Deputy Head of Research, Standigm
Knowledge Hub
GOSTAR® Updates: The Latest Feature Enhancements
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GOSTAR®: the largest online medicinal chemistry intelligence database
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Analyzer in GOSTAR®: Matched Molecular Pair Analysis
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More use cases
Pharma & Biotech
GOSTAR® is pivotal for Pharma & Biotech, offering a SAR database that goes beyond chemistry to include activity, affinity, ADME, toxicology, physicochemical and other data relevant to drug design and discovery. The ontologies, format and interface are well designed to suit your needs.
AI / ML
Boost AI-driven drug discovery with GOSTAR®. It provides a highly normalized, QMS-ISO certified quality data set that can be used to train ML algorithms and help drive the search for novel compounds.
Expand your academic research horizons with GOSTAR®
Tell us about your study areas, and let’s push the boundaries of scientific knowledge together.
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