no code implementations • 13 Feb 2024 • Xuexin Chen, Ruichu Cai, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, Jose Miguel Hernandez-Lobato
In order to enhance the ability of FAMs to distinguish different features' contributions in this challenging setting, we propose to utilize the Probability of Necessity and Sufficiency (PNS) that perturbing a feature is a necessary and sufficient cause for the prediction to change as a measure of feature importance.
no code implementations • 26 Jun 2023 • Wenlin Chen, Julien Horwood, Juyeon Heo, José Miguel Hernández-Lobato
This work extends the theory of identifiability in supervised learning by considering the consequences of having access to a distribution of tasks.
no code implementations • 29 Apr 2020 • Julien Horwood, Emmanuel Noutahi
The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria.
no code implementations • 25 Sep 2019 • Emmanuel Noutahi, Dominique Beani, Julien Horwood, Prudencio Tossou
Recent work in graph neural networks (GNNs) has led to improvements in molecular activity and property prediction tasks.
no code implementations • 28 May 2019 • Emmanuel Noutahi, Dominique Beaini, Julien Horwood, Sébastien Giguère, Prudencio Tossou
We benchmark LaPool on molecular graph prediction and understanding tasks and show that it outperforms recent GNNs.