Predicting the activity of chemical compounds against
cancer is a crucial task. Active chemical compounds against cancer
help pharmaceutical drugs producers in the conception of anticancer
medicines. Still the innate way of representing chemical compounds is
by graphs, the machine learning algorithms can not handle directly the
anticancer activity prediction problems. In this presentation, we investigate the representation power of Node2vec for embedding learning over
graphs, by comparing it to the theoretical framework Graph Isomorphism Network (GIN). We prove that GIN is a deep generalization of
Node2vec. We then exert the two models Node2vec and GIN to extract
regular representations from chemical compounds and make predictions
about their activity against lung and ovarian cancer

Graph Neural Networks to Advance Anticancer Drug Design

technical paper

AIAI 2020 (16th International Conference on Artificial Intelligence Applications and Innovations) should be held in Halkidiki, Greece. Due to current Covid-19 situation the conference will be held virtually. Underline will host a repository of video talks and preserve them for the future. Videos will be released on Underline one day after the conference.

Asmaa Rassil

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Graph Neural Networks to Advance Anticancer Drug Design

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