%0 Conference Proceedings
%T Graph Neural Networks to Advance Anticancer Drug Design
%+ Faculté des Sciences [El Jadida. Maroc]
%+ Université Sidi Mohamed Ben Abdellah (USMBA)
%A Rassil, Asmaa
%A Chougrad, Hiba
%A Zouaki, Hamid
%Z Part 4: Learning Algorithms
%< avec comité de lecture
%@ 978-3-030-49160-4
%( Artificial Intelligence Applications and Innovations
%B 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Neos Marmaras, Greece
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 IFIP Advances in Information and Communication Technology
%V AICT-583
%N Part I
%P 216-226
%8 2020-06-05
%D 2020
%R 10.1007/978-3-030-49161-1_19
%K Graph neural networks
%K Transductive learning
%K Inductive learning
%K Representation vectors
%K Anticancer activity prediction
%Z Computer Science [cs]Conference papers
%X 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. Dealing with data defined on a non-Euclidean domain gave rise to a new field of research on graphs. There has been many proposals over the years, that tried to tackle the problem of representation learning on graphs. In this work, 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.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-04050573/document
%2 https://inria.hal.science/hal-04050573/file/497040_1_En_19_Chapter.pdf
%L hal-04050573
%U https://inria.hal.science/hal-04050573
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-583