London, United Kingdom and Hyderabad, India: February 9, 2022 – A new collaboration between data science and analytics leader, Excelra, and artificial intelligence pioneer X-Chem will accelerate preclinical drug discovery and aid scientists to find new drug candidates for currently hard-to-drug targets.

Machine learning and artificial intelligence are reshaping discovery and optimization of drug candidates. This synergistic new partnership between Excelra’s GOSTAR and X-Chem’s RosalindAI  will enable unique and powerful tools to predict small molecules, chemical, biological, and physical properties, accelerating time and resource-intensive stages of drug discovery from  hit identification to preclinical candidate selection.

“This is a perfect match between two of the best solutions for some hard challenges in drug discovery,” said Norman Azoulay, Director, Scientific Products. “We’re convinced this partnership will immediately help drug developers fuel their pipelines better.”

GOSTAR’s proprietary data set underwent rigorous analysis and large-scale ML model building to predict drug solubility in a recent joint study. X-Chem’s RosalindAI delivered superior and actionable results than other similar analyses using  well-known publically available datasets. The results confirmed that RosalindAI’s proprietary models are designed specifically to address challenges in chemical datasets, and when trained on the larger, more diverse GOSTAR data, yielded models twice as better than models trained on other datasets.

X-Chem SVP Noor Shaker says: “AI is revolutionizing drug discovery in ways never thought possible before, and RosalindAI is leading the way with AI tools that are accurate, scalable, and robust, enabling a transformation in preclinical drug discovery. Our collaboration with Excelra will enable us to leverage GOSTAR data and enable AI in ways never possible before.”

GOSTAR provides a unique 360⁰ view of over 8 million small molecules. The content in GOSTAR is meticulously curated with a proprietary QMS-ISO certified process. It captures the most up-to-date view of the chemical space with information on chemical structures and their biological properties, including binding, in-vitro, in-vivo, ADME, Tox, and physicochemical properties.

X-Chem’s RosalindAI is a leading AI platform for preclinical drug discovery. It provides a seamless interface to build best-in-class AI models for chemical design and optimization. It has also been successfully applied to the design of a novel chemotype for challenging targets and for the accurate prediction of chemical activities and properties. 

Hyderabad, India/ Shenzhen, China: January 19, 2022 – Excelra, a leading global Data & Analytics organization, today announced the partnership for its Global Online Structure Activity Relationship Database (GOSTAR) with XtalPi Inc., an AI-based pharmaceutical biotechnology company reinventing the industry’s approach to drug research and development with its Intelligent Digital Drug Discovery and Development platform.

Excelra will provide ADMET datasets in the GOSTAR database to XtalPi Inc. as part of the partnership. GOSTAR’s ADMET data will power XtalPi’s predictive models. The data helps XtalPi with high precision and predictability to confidently tackle clinical failures of new chemical entities. The well-annotated, high-quality ADMET datasets of GOSTAR are built with a proprietary QMS-ISO certified curation process powered by NLP and human intelligence.

GOSTAR provides comprehensive information encompassing over 8 million compounds, manually curated from a variety of sources including patents and journal articles. The database contains over 29 million SAR associated data points. The well-structured relational database can be utilized for diverse applications across various stages of drug discovery and development lifecycle and aids in target validation, hit identification, early lead identification, and optimization.

Min Xu, Senior Scientist, Research Manager, XtalPi Inc., said, “In XtalPi Inc., we develop advanced AI-based algorithms to tackle the challenges in the drug design process. The size and quality of datasets are always a big concern for us to build high-accuracy predictive models. That is why we consider GOSTAR as a unique and precious resource. It has millions of data points covering different compounds’ ADMET properties and is also trustful, structured, and updated. We highly recommend GOSTAR to whoever is involved in the innovation of drug design methodologies.”

Norman Azoulay, Product Leader, Excelra, said, “Artificial Intelligence and Machine Learning is bringing a paradigm shift to drug discovery and development. This partnership will help train XtalPi’s models to accurately predict efficacy and safety parameters and to ultimately increase the success rate of drug design.”

Hyderabad, India and Nanjing, China: December 1, 2021 – 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 compoundsand 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.”

HYDERABAD, August 23, 2021 – Today, Excelra, a global data science and data analytics company, unveiled an improved version of GOSTAR, its flagship chemistry insights engine. The enhancements offer advanced search, analysis, and visualization capabilities enabling drug discovery scientists to accelerate the hit identification and lead optimization process.

“Drug discovery researchers need to ensure that they are getting the broadest possible view of structure-activity data while being able to readily drill down to the most meaningful data points. The new GOSTAR version has been re-envisioned based on extensive user feedback to provide researchers with an intuitive interface that reduces time to market for new drugs. It incorporates analytical capabilities, eliminating the need to use multiple tools to assess compounds while also providing insights directly within the application. The result is a rapid and more confident decision-making process.” said Norman Azoulay, Director, Scientific Products.