Can you routinely identify potential off-target effects? Combine sophisticated machine learning-driven predictions with mass spectrometry and gene expression data to screen potential drug candidates for likely off-target effects. Quickly evaluate potential candidates for likely side effects or toxicity. Informed pipeline decision making at an early stage saves time, money and attrition, and improves the likelihood that your drugs will make it to market. Can you adequately scale-up your analysis? The Aigenpulse Platform’s high-performance back end allows you to run advanced machine learning algorithms, including the Risk Profiling module, without slowing platform speed or functionality. Running as a scalable microservice that crunches the data in the background while you continue your work, the platform notifies you when the analysis has been completed and is ready for review. Run and re-run 100,000s of analyses with no loss of performance with the assurance of all parameters and results saved and accessible. Interactive focus on key data Potential drug targets can be evaluated in the context of large amounts of expression data and visualised as interactive plots. Intuitive visualisation can help you to hone in on key data points, and inform on whether a candidate target should be progressed. Built around in-house data, augmented with external sources The Aigenpulse Platform is designed to help you to build a powerful and tailored analysis pipeline by combining multiple, disparate datasets from internal and external sources. The Aigenpulse Platform takes care of standardising and integrating these datasets using our Gene Matching technology. This allows you to leverage advanced machine learning algorithms that maximize the benefit you can derive from valuable data and resources.