More than just a scientific software solution, Discngine Assay offers a robust data management system that seamlessly manages, stores, and integrates large volumes of data from diverse sources such as liquid handlers and, plate readers... It ensures high data integrity, tracking, security, and accessibility. The platform enables labs to maintain data compliance confidently and make well-informed decisions while adhering to FAIR data principles.

With Discngine Assay, High-Throughput Screening labs benefit from sophisticated data analysis and visualization capabilities. The software's ability to perform statistical analysis, run automated data analysis, and generate high-quality visual representations helps researchers rapidly identify hits and invalidate outliers.
With advanced visualization, scientists can also compare multiple experiments to identify patterns and analyze trends over time. This reduces the time to insights and speeds up the discovery projects overall.

Discngine Assay minimizes manual errors and maximizes throughput by automating critical aspects of the data analysis. This automation ensures that lab operations are consistent, efficient, and scalable, freeing staff to focus on more strategic tasks.

The cloud-based platform fosters collaboration among teams, regardless of their physical locations. It can be easily implemented in multiple labs and across various sites within the same company, enabling collaboration at the global level.

Real-time data sharing is crucial for coordinated efforts in large-scale, multi-site HTS operations.

Discngine's ISO 27001 certification demonstrates its total commitment to ensuring the confidentiality, availability, and integrity of customers’ research data and its protection from security vulnerabilities.

Discngine Assay is developed in accordance to FAIR (Findable, Accessible, Interoperable, and Reusable) principles, allowing companies to rest assured their data meets these standards.

By adhering to FAIR principles, the platform enhances data management practices, ensuring that data is well-organized, easily accessible, and usable across various systems and research projects. This approach also enables effective reuse of historic knowledge, which creates a huge increase in trust and data quality overall.