Enhanced High Throughput Analysis and Property Prediction for Pharmaceutical Development

Description:
High throughput analysis (HTA) is an increasingly important component of automated workflows in the pharmaceutical industry and in other areas of research where high throughput experimentation is carried out. In recent years there has been a growing interest in applying artificial intelligence and machine learning to enhance and enable HTA workflows. In this presentation we describe several recent projects that provide a glimpse into the new opportunities coming from this union of automation, analytical chemistry and data science, including development of new approaches for improved chiral chromatographic method development and chemical structure elucidation via mass spectrometry. Recent work in this area coming from the Center for Bioanalytic Metrology (CBM), an NSF-funded Industry-University Cooperative Research Center, will be highlighted,

Speaker: Christopher Welch - ICASE
Christopher J. Welch is a principal with Welch Innovation, LLC. Since 2017, Chris has served as the Executive Director of the Indiana Consortium for Analytical Sciences and Engineering, a joint venture between Purdue, Notre Dame, and Indiana Universities focusing on measurement science and analytical instrumentation. Chris is a veteran of the pharmaceutical industry, with expertise in process research, stereochemistry, separation technologies, green chemistry and high throughput experimentation and analysis. Chris created and administered Merck's Postdoctoral program and for 15 years managed a multi-million-dollar fund and research network that identified, evaluated and acquired more than 300 new enabling technologies for pharmaceutical discovery, development and manufacturing. He has served as a science advisor with the FDA, is a cofounder of The Enabling Technologies Consortium, has been involved in several life science startups and has published more than 275 scientific articles.