Name: Exploiting artificial intelligence in synthesis planning
Start: 2024-09-12T15:00:00+00:00
End: 2024-09-12T16:30:00+00:00
Location: EDIT Analysen

AI for Science seminar with Samuel Genheden, AstraZeneca.

12 Sep, 15:0012 Sep, 16:30

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Overview

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12 Sep 2024, 15:0012 Sep 2024, 16:30

EDIT Analysen

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Seats available:40

Language:English

Dr. Samuel Genheden, AstraZeneca.

The on-site event will be followed by fika in the Analysen coffee area (fika from 16:00-16:30).

"Exploiting artificial intelligence in synthesis planning"

Talk abstract:

Tools for synthesis planning is changing rapidly with the emergence of artificial intelligence (AI) models. AI-assisted synthesis planning tools can now perform retrosynthesis tasks, evaluate reactivity, or suggest reaction conditions to mention a few examples. In this talk, I will present current research from AstraZeneca R&D with a focus on retrosynthesis. I will provide an overview of our open-source retrosynthesis platform, AiZynthFinder, show how transformers can complement rule-based approaches, and detail some recent developments on constrained retrosynthesis. The talk will be concluded with a discussion on outstanding challenges for AI-assisted synthesis planning, and a peak into future research directions.

About the speaker:

Samuel Genheden leads the Deep Chemistry team in Discovery Sciences, AstraZeneca R&D. He received his PhD in theoretical chemistry from Lund University in 2012, having studied computational methods to estimate ligand-binding affinities. He continued with postdocs at the Universities of Southampton and Gothenburg, where he simulated membrane phenomena using multiscale approaches. He joined the Molecular AI department at AstraZeneca in 2020 and became team leader in 2022. The team’s research focuses on the AiZynth platform for AI-assisted retrosynthesis planning. Samuel’s interests lie in studying chemical and biological systems with computers and using these approaches to impact drug development. He is a keen advocate for open-source software.

Structured learning

This theme focuses on how to make use of structure in data to build machine learning (ML) and artificial intelligence (AI) systems which are safer, more trustworthy and generalize better. Structure includes the relationship between data, in time and space, and how the predictions change when data is transformed in specific ways, for example rotated or scaled. These topics are abstract and general but have a direct impact on the use of AI and ML in the sciences and in applications such as drugs and materials design, or medical imaging.