Sign up to save your podcasts
Or
Share Do Machines Dream of Atoms? Crippen’s logP as a Quantitative Molecular Benchmark for Explainable AI Heatmaps | Jan Jensen
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Do Machines Dream of Atoms? Crippen’s logP as a Quantitative Molecular Benchmark for Explainable AI Heatmaps | Jan Jensen
[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.
If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live.
Also consider joining the M2D2 Slack
Abstract: While there is a great deal of interest in methods aimed at explaining machine learning predictions of chemical properties, it is difficult to quantitatively benchmark such methods, especially for regression tasks. We show that the Crippen logP model provides an excellent benchmark for atomic attribution/heatmap approaches, especially if the ground truth heatmaps can be adjusted to reflect the molecular representation. I give some examples of how this benchmark can be used to get a better understanding of ML models work and how it can be used to determine which techniques for generating XAI heatmaps works the best.
Slides from the talk: https://speakerdeck.com/jhjensen/jensen-xai
Speakers: Jan Jensen
Twitter Prudencio
Twitter Therence
Twitter Cas
Twitter Valence Discovery
View all episodes
By Valence Discovery[DISCLAIMER] - For the full visual experience, we recommend you tune in through our YouTube channel to see the presented slides.
If you enjoyed this talk, consider joining the Molecular Modeling and Drug Discovery (M2D2) talks live.
Also consider joining the M2D2 Slack
Abstract: While there is a great deal of interest in methods aimed at explaining machine learning predictions of chemical properties, it is difficult to quantitatively benchmark such methods, especially for regression tasks. We show that the Crippen logP model provides an excellent benchmark for atomic attribution/heatmap approaches, especially if the ground truth heatmaps can be adjusted to reflect the molecular representation. I give some examples of how this benchmark can be used to get a better understanding of ML models work and how it can be used to determine which techniques for generating XAI heatmaps works the best.
Slides from the talk: https://speakerdeck.com/jhjensen/jensen-xai
Speakers: Jan Jensen
Twitter Prudencio
Twitter Therence
Twitter Cas
Twitter Valence Discovery