Typically, in the pharmaceutical industry, data analysis or the application of computational approaches occurs at the request of users (possibly run by the user themselves). A user will either open an application or webpage and click a button to obtain the information or data that they are after or they will request a moretechnically experienced co-worker to perform the analysis for them. We feel that with the virtual assistant concept, such as has been developed in mainstream technology companies (e.g. Alexa, Siri, Google Assistant), this can happen in a far more proactive and automated manner. Responding to the generation of new data and automatically running predefined analyses. This brings standardization, ease of application across projects, and delivery of information to users they may not have been exposed to otherwise.<br/>Here, we introduce Kernel, a virtual assistant created to enhance medicinal chemistry at Lilly, along with the wider virtual assistant concept which we feel is applicable to medicinal chemistry teams across the industry. Kernel interacts with users (medicinal chemistry teams), delivering automated analysis and results of computational work directly to their email inbox. A number of implemented POC scenarios will be described including a detailed summary and assessment of primary biochemical assay data, as well as de novo design on newly tested compounds, which are then ranked and then sent back to the original submitter.<br/>The talk will conclude with a description of the first example of a fully automated medicinal chemistry design cycle at Lilly. Kernel identified newly tested molecules, designed derivatives, ranked them using predictive models, identified synthetic routes, and then a number were made via automated synthesis. Achieving this in the absence of human intellectual input was a landmark moment bringing together numerous technologies at Lilly and highlighting how automated approaches can complement human scientists.

Developing Kernel, a virtual assistant for medicinal chemistry discovery teams at Eli Lilly

2019 Fall National Meeting

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