In medicinal chemistry, the relationship between molecular structure of a compound and its biological activity is referred to as Structure Activity Relationship (SAR). Medicinal chemists modify biomedical molecules by inserting new chemical groups into the compound and test those modifications for their biological effects. Determining and identifying SARs is key to many aspects of the drug discovery process, ranging from hit identification to lead optimization.
Although information on millions of compounds and their bio-activities e.g. reaction ability, solubility, target activity etc., is freely available to the public, it is very challenging to infer a meaningful and novel SAR from that information. The underlying problem in here is the un-structured and heterogeneous nature of these datasets contributed by the scientific & research community in journals, scientific articles, patents, regulatory documents and various secondary sources. Owing to the increasing structural diversity among hit compounds and their potency distribution, it is becoming a challenge to analyze the SAR information. If these relationships are properly extracted, associated and analyzed, they provide valuable information that would support drug discovery and development. To this end, there has been an increasing need and interest in mining and structuring SAR information from bioactivity data available in the public domain.
Global Online Structure Activity Relationship Database (GOSTAR)
Excelra, a leading global biopharma data and analytics company, has responded to this pertinent need by developing a knowledge repository, Global Online Structure Activity Relationship Database (GOSTAR), which provides a 360-degree view of millions of compounds linking their chemical structure to the biological, pharmacological and therapeutic information. GOSTAR contains high-quality, manually annotated and very well-structured SAR data captured from various primary sources (patents and top journals of medicinal chemistry) and secondary sources (conference meetings & abstracts, company drug development pipelines, company annual reports, clinical registries and drug approval reports).
Who can use GOSTAR and how?
The main objective for creating GOSTAR is to assist medicinal chemists, computational chemists and cheminformaticians in their quest for identifying potential small molecules that have decent biological effect and could be of a specific therapeutic use. GOSTAR enables users to quickly visualize, explore, analyze and evaluate SAR data based on their project requirements. The users can explore various SAR associations by searching various identifiers like drug names, chemical structures, bibliography, compound development stage and activity endpoints.
What are the applications of GOSTAR?
Better understanding of SAR data will enable the users to take correct decisions in exploring the chemical space while designing a drug.
Following are the applications of GOSTAR:
- Target profiling – GOSTAR enables a holistic exploration of the chemical space around a target of interest & enables the users to understand the pathways and indications in which a given target is implicated
- Structure based drug design – GOSTAR can be used as a compound library to perform virtual screening and hit identification in traditional structure-based drug design methodologies
- Lead optimization – GOSTAR enables lead optimization by suggesting the structure activity relationships with improved potency, reduced off-target activities, and physiochemical/metabolic properties
- Assay validation – GOSTAR suggests the right functional assays for secondary validation for the chemical modifications while involved in the tuning of the hit molecule
- Drug repurposing and Translational science – GOSTAR data can be mined to interrogate diverse targets with a compound of interest to understand the feasibility and viability for drug rescue or for label expansion
- Competitive intelligence and Novelty analysis – GOSTAR captures drug lifecycle information such as indication, phase of development, sponsor and recruitment/approval status including suspended trials along with the reason for discontinuation that can be used for building the competitive landscape around the drug/target/indication.
Currently, there are hundreds and thousands of chemical classes, and it often becomes daunting task to identify potential candidates for therapeutic use. In such cases, using knowledge repositories like GOSTAR, we can rapidly characterize data points that can help to efficiently capture and encode specific SAR. Below are the key features that showcase why GOSTAR is the ideal and simplistic solution for the complex task of gathering SAR data.
- Reachability – Easy content accessibility to a wide and diverse user community
- Utility – Maximize the utilization of content to create insights/concepts
- Applicability – Selective utilization of content in diverse early discovery programs targeting unmet medical needs
- Reliability – Standardized and normalized content to support traditional as well as AI/ML driven discovery programs