Biotech and pharma companies are increasingly utilising artificial intelligence (AI) to expedite the discovery of new therapeutics. With this comes the need for large amounts of robust, high-quality data to feed into data hungry algorithms and drive design-make-test-analyse cycles.

BioAscent’s centralised compound management system streamlines the process of storing, handling, and distributing chemical compounds, resulting in rapid turnaround times for our clients. State-of-the-art automation plays a crucial role in this system, enabling precise and efficient management of compound libraries. Fully automated processes ensure that compounds are accurately tracked, retrieved, and dispensed, thereby eliminating the risk of human error and increasing the robustness and reliability of experimental data.

AI-driven drug discovery relies heavily on large datasets to identify potential drug candidates. Automation ensures that data regarding each compound’s properties and availability is consistently updated and easily accessible. At BioAscent, we implement the FAIR principles for scientific data management (i.e., Findable, Accessible, Interoperable and Reusable). This data integrity is vital for training AI models and for performing high-throughput screening and virtual screening experiments. Our robust processes ensure that data is fit for purpose to be used by machine learning and artificial intelligence technologies, giving our clients the reassurance that they are driving their drug discovery projects using high quality data inputs.

Our poster, Advancing Drug Discovery: Facilitating AI-driven Initiatives with Automation in BioAscent’s Centralised Compound Management, describes some examples of how BioAscent’s centralised compound management approach supports our client’s AI-driven drug discovery projects.