Asst. Prof. Andrea Mastropietro gave a lecture in February.<\/p>\n
The details are as follows.<\/p>\n
==================== <\/span><\/p>\n Asst. Prof. Andrea Mastropietro<\/p>\n TITLE: ABSTRACT\uff1a <\/p>\n <\/p>\n \u00a0<\/p>\n<\/div>\n<\/div>\n <\/p>\n","protected":false},"excerpt":{"rendered":" Asst. Prof. Andrea Mastropietro gave a lecture in February. The details are as follows. ==================== Asst. Prof. Andrea Mastropietro TITLE: Graph Learning and Explainable Artificial Intelligence in the Life Sciences ABSTRACT\uff1a Deep learning has become a widely used tool in chemoinformatics and bioinformatics. Graph-based models, such as graph neural networks, can be applied to molecular […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_locale":"en_US","_original_post":"http:\/\/www-dsc.naist.jp\/dsc_naist\/?p=6558","_links_to":"","_links_to_target":""},"categories":[3],"tags":[],"event_taxonomy":[15],"acf":[],"_links":{"self":[{"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/posts\/6561"}],"collection":[{"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/comments?post=6561"}],"version-history":[{"count":2,"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/posts\/6561\/revisions"}],"predecessor-version":[{"id":6563,"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/posts\/6561\/revisions\/6563"}],"wp:attachment":[{"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/media?parent=6561"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/categories?post=6561"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/tags?post=6561"},{"taxonomy":"event_taxonomy","embeddable":true,"href":"http:\/\/www-dsc.naist.jp\/dsc_naist\/wp-json\/wp\/v2\/event_taxonomy?post=6561"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nGraph Learning and Explainable Artificial Intelligence in the Life Sciences<\/p>\n
\nDeep learning has become a widely used tool in chemoinformatics and bioinformatics. Graph-based models, such as graph neural networks, can be applied to molecular graphs to predict chemical properties or biochemical activity with high accuracy. However, deep learning models often lack transparency, which is undesirable in applications such as drug design, where model outputs must be interpretable to be trusted. To address this limitation, explainable artificial intelligence strategies have been extensively developed and applied. This seminar will discuss how graph neural networks can be used and explained in the context of chemoinformatics and drug design, covering applications ranging from predictive to generative artificial intelligence. We will examine whether graph deep learning models are capable of extracting and learning meaningful chemical knowledge from data, or whether they mainly rely on memorizing statistical patterns. Finally, we will highlight current challenges and outline opportunities for future research in this field.<\/p>\n