We are working on maximizing efficiency through In Silico and Machine Learning tools to supplement our efforts in automation and data management. Making use of our high-volume in-house assay data (ADME and Bioanalysis), we are developing strategies to predict endpoints and increase probabilities of success in a high-throughput environment. The use of these tools saves valuable time either reducing the need for wet work or accelerating the pace of our workflows such as selecting the right Bioanalytical method for the right need. By incorporating Machine Learning into our systems and strategic plans, we can act on data-driven insights with greater speed and efficiency and ultimately accelerate pipeline delivery.
Learning Objectives:
Understand the role of In Silico and Machine Learning tools in maximizing efficiency.
Learn how to effectively utilize high-volume in-house assay data to develop strategies for predicting endpoints and increasing the probabilities of success in a high-throughput environment, specifically in the context of ADME and Bioanalysis.
Understand the benefits and implementation of incorporating Machine Learning into systems and strategic plans to act on data-driven insights with greater speed and efficiency, ultimately accelerating pipeline delivery.
