Excelra, a leading global data & analytics provider, today announced its partnership with Silexon AI Technology, a next-generation biotech company with a scalable AI/ML (Artificial Intelligence and Machine Learning) platform for drug discovery and biopharmaceutical research.
Under the agreement, Excelra will provide ADMET and Binding Assays datasets from its GOSTAR database to Silexon. GOSTAR provides comprehensive information encompassing over 8 million compounds and contains more than 28 million SAR associated data points. The well-structured relational database can be utilized for diverse applications across different phases of drug discovery and development and aids in target validation, hit identification, early lead Identification, and optimization.
Silexon's AI/ML platform uses both high-quality, real-world biological data and outstanding machine learning capabilities to aid in de novo drug design, drug discovery, and biopharmaceutical research through its disruptive predictive tools that help streamline the discovery and design process, thus shortening the time to market for new life-saving drugs. Silexon focuses on innovative biomedicines with unique target product profiles for undruggable, difficult-to-drug, and novel targets in multiple therapeutic areas.
Anandbir Singh Brar, CEO of Excelra, said, "We are excited that GOSTAR has become the gold standard dataset for Biopharma and AI/ML companies. Our partnership with Silexon underscores GOSTAR's ability to provide large, normalized, quality-controlled datasets that empower a wide range of predictive models. We are delighted to partner with Silexon and to be part of their journey in developing the AI4D platform for drug discovery."
Hainian ZENG, CEO of Silexon, said, "We look forward to combining the well-annotated GOSTAR data with data accumulated and generated by Silexon's proprietary AI/ML models and wet-lab experiments. Using our AI/ML platform, we aim to address bottlenecks in drug discovery and biopharmaceutical research to ultimately deliver innovative therapeutic candidates with higher overall success rates. Our preliminary assessment gives us the confidence that GOSTAR's database would be an important addition to our current training dataset for next-generation modeling."
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( With inputs from ANI )
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