Timezone: »

Invited Talk: Benjamin Sanchez-Lengeling - Evaluating Attribution of Molecules with Graph Neural Networks
Benjamin Sanchez-Lengeling

Sat Dec 12 11:51 AM -- 12:11 PM (PST) @


The interpretability of machine learning models for molecules is critical to scientific discovery, understanding, and debugging. Attribution is one approach to interpretability, which highlights parts of the input that are influential to a neural network’s prediction. With molecules, we can set up synthetic tasks such as the identification of subfragment logics to generate ground truth attributions and labels. This scenario serves as a testbed to quantitatively study attributions of molecular graphs with Graph Neural Networks (GNNs). We perform multiple experiments looking at the effect of GNN architectures, label noise, and spurious correlations in attributions. In the end, we make concrete recommendations for which attribution methods and models to use while also providing a framework for evaluating new attribution techniques.

Biography: I am a research scientist at Google Research. My research centers around using machine learning techniques to build data-driven models for the prediction of molecular properties and the generation of new molecules and materials via generative models. Applications include solar cells, solubility, drug-design, and particularly smelly molecules. I am part of a team that wants to do for olfaction, what machine learning has done for vision and speech.

I am also passionate about science education and divulgation, I am one of the founders and organizers for Clubes de Ciencia Mexico and a LatinX-centered AI conference RIIAA. In my free time, I like to run, eat ice cream and cook food.

Author Information

Benjamin Sanchez-Lengeling (Google Research)

More from the Same Authors

  • 2022 : A deep learning and data archaeology approach for mosquito repellent discovery »
    Jennifer Wei · Marnix Vlot · Benjamin Sanchez-Lengeling · Brian Lee · Luuk Berning · Martijn Vos · Rob Henderson · Wesley Qian · D. Michael Ando · Kurt Groetsch · Richard Gerkin · Alexander Wiltschko · Koen Dechering
  • 2022 Workshop: AI for Accelerated Materials Design (AI4Mat) »
    Santiago Miret · Marta Skreta · Zamyla Morgan-Chan · Benjamin Sanchez-Lengeling · Shyue Ping Ong · Alan Aspuru-Guzik
  • 2020 Poster: Evaluating Attribution for Graph Neural Networks »
    Benjamin Sanchez-Lengeling · Jennifer Wei · Brian Lee · Emily Reif · Peter Wang · Wesley Qian · Kevin McCloskey · Lucy Colwell · Alexander Wiltschko
  • 2019 : Morning Coffee Break & Poster Session »
    Eric Metodiev · Keming Zhang · Markus Stoye · Randy Churchill · Soumalya Sarkar · Miles Cranmer · Johann Brehmer · Danilo Jimenez Rezende · Peter Harrington · AkshatKumar Nigam · Nils Thuerey · Lukasz Maziarka · Alvaro Sanchez Gonzalez · Atakan Okan · James Ritchie · N. Benjamin Erichson · Harvey Cheng · Peihong Jiang · Seong Ho Pahng · Samson Koelle · Sami Khairy · Adrian Pol · Rushil Anirudh · Jannis Born · Benjamin Sanchez-Lengeling · Brian Timar · Rhys Goodall · Tamás Kriváchy · Lu Lu · Thomas Adler · Nathaniel Trask · Noëlie Cherrier · Tomohiko Konno · Muhammad Kasim · Tobias Golling · Zaccary Alperstein · Andrei Ustyuzhanin · James Stokes · Anna Golubeva · Ian Char · Ksenia Korovina · Youngwoo Cho · Chanchal Chatterjee · Tom Westerhout · Gorka Muñoz-Gil · Juan Zamudio-Fernandez · Jennifer Wei · Brian Lee · Johannes Kofler · Bruce Power · Nikita Kazeev · Andrey Ustyuzhanin · Artem Maevskiy · Pascal Friederich · Arash Tavakoli · Willie Neiswanger · Bohdan Kulchytskyy · sindhu hari · Paul Leu · Paul Atzberger