In medicinal chemistry, the relationship between molecular structure of a compound and its biological activity is referred to as Structure Activity Relationship (SAR). GOSTAR™ is a comprehensive small molecules database designed to support medicinal chemists in understanding SAR and accelerating data-driven drug discovery. Medicinal chemists modify bioactive molecules by inserting new chemical groups into compounds and test those modifications for their biological effects. Determining and identifying SARs is central to many stages of the drug discovery pipeline, ranging from hit identification to lead optimization, and is increasingly supported by advanced cheminformatics approaches.
Although information on millions of compounds and their bioactivities—such as reactivity, solubility, and target engagement—is freely available in the public domain, extracting meaningful and novel SAR insights remains highly challenging. This difficulty largely stems from the unstructured and heterogeneous nature of datasets contributed by the scientific community through journals, patents, regulatory filings, and other secondary sources. Addressing this challenge requires robust drug discovery databases and expertly curated bioactivity repositories, as discussed in types of drug discovery databases.
Owing to the increasing structural diversity among hit compounds and the wide distribution of their potencies, systematic SAR analysis has become more complex. Advanced data integration and analytics methods, including AI and machine learning, are increasingly being used to uncover hidden SAR patterns across large-scale datasets. When these structure–activity relationships are accurately extracted, linked, and analyzed, they provide valuable insights that significantly accelerate data-driven drug discovery and development.
To this end, there is a growing need and interest in mining, curating, and structuring SAR information from publicly available bioactivity data. Data-driven platforms and curated knowledgebases—such as those highlighted in GOSTAR™ medicinal chemistry intelligence—enable researchers to transform disparate bioactivity data into actionable SAR intelligence that supports rational medicinal chemistry decisions.
Global Online Structure Activity Relationship Database (GOSTAR™)
Excelra, a global biopharma data and analytics company (About Excelra), has responded to this need by developing the Global Online Structure Activity Relationship Database (GOSTAR™)—a powerful small molecules database that provides a 360-degree view of millions of compounds linking chemical structure to biological, pharmacological, and therapeutic information.
GOSTAR™ contains high-quality, manually annotated, and well-structured SAR data captured from primary sources such as patents and leading medicinal chemistry journals, and secondary sources including conference abstracts, company drug development pipelines, clinical registries, and drug approval reports. This positions GOSTAR™ among the most comprehensive SAR databases available today.
Who can use GOSTAR™ and how?
The primary objective of GOSTAR™ is to assist medicinal chemists, computational chemists, and cheminformaticians in identifying potential small-molecule therapeutics with meaningful biological activity and therapeutic relevance.
GOSTAR™ enables users to quickly visualize, explore, analyze, and evaluate SAR data using multiple identifiers such as drug names, chemical structures, bibliography, compound development stages, and activity endpoints—supporting exploratory research and early discovery decision-making. Its utility is further demonstrated in comparisons such as GOSTAR™ vs ChEMBL.
Applications of GOSTAR™ small molecules database
A deeper understanding of Structure–Activity Relationship (SAR) data enables researchers to make informed decisions while exploring chemical space during drug design. Leveraging comprehensive SAR intelligence through GOSTAR™ significantly accelerates early drug discovery and development.
Target profiling
GOSTAR™ enables holistic exploration of chemical space around a target of interest and helps researchers understand biological pathways and disease indications associated with that target—supporting data-driven target intelligence as outlined in drug target dossier and target intelligence.
Structure-based drug design
GOSTAR™ supports structure-based drug design by enabling virtual screening and hit identification workflows, complementing insights described in cheminformatics-driven drug discovery.
Lead optimization
By providing detailed SAR insights, GOSTAR™ supports lead optimization by suggesting chemical modifications that improve potency, reduce off-target effects, and optimize physicochemical and metabolic properties—key concepts discussed in hit-to-lead optimization.
Assay validation
GOSTAR™ recommends appropriate functional assays for secondary validation during hit-to-lead and lead optimization stages, leveraging curated experimental insights from structured bioactivity data.
Drug repurposing and translational science
GOSTAR™ data can be mined to interrogate multiple targets for a compound of interest, enabling feasibility assessments for data-driven drug repurposing and translational research.
Competitive intelligence and novelty analysis
GOSTAR™ captures comprehensive drug lifecycle information—including indication, development phase, sponsor details, and clinical trial status—supporting competitive landscape analysis as demonstrated in competitive landscape case studies.
Why GOSTAR™?
With hundreds of thousands of chemical classes available today, identifying viable therapeutic candidates can be daunting. Knowledge repositories like GOSTAR™ small molecules database enable rapid characterization and encoding of critical SAR data, helping researchers navigate chemical complexity with confidence.
Key features of GOSTAR™
Reachability – Easy access to curated SAR content for a broad scientific community
Utility – Maximized content utilization to generate actionable insights and hypotheses
Applicability – Selective use across diverse early discovery programs targeting unmet medical needs
Reliability – Standardized, normalized, expert-curated content suitable for traditional research and AI/ML-driven drug discovery
