Cyclica Leverages GOSTAR Database to Advance AI-Driven Drug Discovery Platform

Hyderabad, India and Toronto, October 21, 2020 /PRNewswire/ -- A global leader in data and analyticsExcelraAnnounced today, licensing leading biotechnology company Cyclica Inc. to use its Global Online Structure-Activity Relationship database (GOSTAR). Cyclica’s AI-enhanced integrated platform enables multi-target, polypharmacology-based drug molecule design.

GOSTARGOSTAR is the largest online structure-activity relationship database, containing over 5.5 million small molecules along with their associated chemical, biological, and pharmacological properties. Curated manually by our scientific team, the database extracts and enriches datasets from functional assays, in vitro, and in vivo studies. Various small-molecule activities—including SAR, physicochemical, metabolic, ADME, and toxicological profiles—are documented and structured to form this relational database. GOSTAR provides researchers with ultimate insights, enabling them to generate novel ideas during the early stages of drug design and throughout the optimization phase of drug discovery.

Excelra Chemical Protective SuitChief Financial OfficerRaveendra DayamThe doctor stated:“GOSTAR provides over 28 million experimentally determined quantitative interactions between small molecules and the vast druggable target space. The insights derived from these interactions complement Cyclica’s polypharmacology approach employed in novel compound discovery. GOSTAR is a rich qualitative and quantitative dataset that has been adopted by many AI/ML companies, and we are pleased that these data will support Cyclica’s predictive analytics.”

The breadth of data provided by GOSTAR will broaden the applicability of Cyclica’s models, asCyclicaResearch and DevelopmentVice President, Development Stephen MacKinnonPh.D.As stated: “The collaboration with GOSTAR has enabled Cyclica to annotate proteomic screening data, thereby enhancing the training data for Cyclica’s platform models and improving our ability to predict molecular interactions. This will directly facilitate the development of more precise and effective therapies for patients in need.”