This seminar forms part of the AI3SD Online Seminar Series that will run across the autumn (from October 2021 to December 2021). This seminar will be run via zoom, when you register on Eventbrite you will receive a zoom registration email alongside your standard Eventbrite registration email. Where speakers have given permission to be recorded, their talks will be made available on our AI3SD YouTube Channel. The theme for this seminar is Explainable AI & ML.
Abstract: The value of amassing and standardizing chemical data for improving the efficiency of chemical discovery is becoming increasingly clear. Machine learning analyses of these data are focused on finding correlations, trends and patterns to uncover needles of knowledge in the haystack of chemical reactions. However, in many cases, especially in academic settings, we do not have the means to produce large data sets, so by necessity we remain in the Small Data regime. In this talk, I will present our work in the field of organocatalysis focused on applying machine learning strategies to small data sets as a means to uncover underlying mechanisms. We aim to show that whereas Big Data serves to identify hidden correlations, Small Data encourages the discovery of causation. In this sense, Small Data is not just a necessity, but is key to bridging the gap between human intuition and machine learning.
Bio: Anat Milo received her BSc/BA in Chemistry and Humanities from the Hebrew University of Jerusalem in 2001, her MSc from UPMC Paris in 2004 with Berhold Hasenknopf, and her PhD from the Weizmann Institute of Science in 2011 with Ronny Neumann. Her postdoctoral studies at the University of Utah with Matthew Sigman focused on developing physical organic descriptors and data analysis approaches for chemical reactions. At the end of 2015 she returned to Israel to join the Department of Chemistry at Ben-Gurion University of the Negev, where her research group develops experimental, statistical, and computational strategies for identifying molecular design principles in catalysis with a particular focus on stabilizing and intercepting reactive intermediates by second sphere interactions.
Abstract: High-throughput experimentation (HTE) is an enabling technology that has had major effects on efficiency in small-molecule industrial chemistry, particularly pharma and agorchemicals. Data management and curation can be – perhaps should be – a guiding strategy for building up advantageous HTE capabilities, with futureproofing for goals including machine learning for reaction optimization. Beyond HTE, rapid access to analytical and project data enables chemists in any industrial role to make not only faster, but better decisions.
Bio: Nessa Carson was born in Warrington, England. She received her MChem degree from Oxford University, before completing postgraduate studies in catalysis and organic methodology at the University of Illinois at Urbana-Champaign. She started in industry as a synthetic chemist for AMRI, then moved within the company to run the high-throughput automation facility on behalf of Eli Lilly in Windlesham, working across both the discovery and process chemistry arenas. She then worked in process development using automation at Pfizer. Nessa started at Syngenta in 2020, working in automation, reaction optimization, and data management. She maintains a website of useful chemistry resources, https://supersciencegrl.co.uk.
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