Dr Hugo MacDermott-Opeskin

A machine learning first approach to ADMET (and drug discovery)

MedChem Australia

Online attendance requires registration.
 In-person attendance is available without registration at:
WEHI Davis Auditorium
 1G Royal Parade

<h3>Machine learning for drug discovery attracts enormous hype, but how useful is it actually?</h3><p>In this talk I argue that we're not there yet, and that the bottleneck isn't model architecture but data. I'll introduce OpenADMET, an open-science effort built on a key insight: the high-value protein targets driving absorption, distribution, metabolism, excretion, and toxicity,  the "Avoid-ome", are few in number, which makes designing against them tractable for modern drug discovery techniques. By combining high-throughput assays, structural biology, and active learning to generate consistent, mechanistically grounded data, alongside blind community challenges to keep models honest, OpenADMET aims to move the field from avoiding ADMET liabilities late to designing against them from the start. OpenADMET also gives us a platform to ask fundamental questions about machine learning for molecules, questions whose answers, we hope, can drive the practical, efficacious application of ML to drug discovery, so we can bring more medicines to patients.</p>

A machine learning first approach to ADMET (and drug discovery)

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A machine learning first approach to ADMET (and drug discovery)

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