High throughput experimentation (HTE) has transformed the pharmaceutical industry in recent years, driven by a desire for increase productivity, profitability, and shorter discovery-to-commercialization timelines. Furthermore, HTE has significant implications for data science technologies such as machine learning and AI due to the generation of large amounts of data that can be used to feed the models. While there has been significant investment in robotics and automation for HTE, processing and review of related analytical data is still a largely manual process and as a result, scientists spend too much time on tedious, error-prone tasks using several different software packages. Being able to have direct association of analytical data with array design and plate location, automating the processing of analytical data, and visualization of analytical results and trends for fast, confident decisions is therefore of great interest in the HTE community. Additionally, the ability to export normalized experimental and analytical information is key for further utilization of HTE data. This presentation will examine some of the challenges faced with analytical data management in HTE and examine a solution to these challenges.

Addressing the complexity of analytical data management in high throughput experimentation

ACS Spring Meeting 2021

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