Learning Substructure Invariance for Out-of-Distribution Molecular Representations.pdf

1、DataFunSummitDataFunSummit#20232023Learning Substructure Invariance for Out-of-Distribution Molecular Representations Presented by Nianzu Yang,PhD candidate SJTU-ReThinkLabFormulation:denotes the support of environments,is the prediction model and represents a loss function.The risk function under a

2、 given environment e:Background-OoD1MoleOODNianzu Yang Out-of-Distribution Generalization:Assume that there is a potential environment variable accounting for the distribution shift between the training and testing data.In general cases the goal is to predict the target label given the associated in

3、put .Background-Invariant Learning2MoleOODNianzu Yang Invariant Learning is an emerging line for solving the OOD generalization problem.These methods propose to find an invariant predictor that could uncover invariant relationships between inputs and targets across all environments.The invariant pre

4、dictor aims to learn an invariant representation satisfying such a invariance principle.Invariance Principle:1)sufficiency:shows sufficient predictive power for the target2)invariance:contributes to equal performance for the downstream tasks across all environmentsA molecular graph can be represente

5、d as ,where is the graphs node set corresponding to atoms constituting the molecule and denotes the graphs edge sets corresponding to chemical bonds.Background-MRL3MoleOODNianzu Yang Molecular Representation Learning(MRL)aims at embedding a molecule into a vector in latent space as a foundation mode

6、l,on top of which the learned representations could be used for a variety of downstream tasks.SMILES-based methodsStructure-based methodsOoD Molecular Represention Learning4MoleOODNianzu Yang OOD General Formulation:OoD on MRL:Motivating Examples5MoleOODNianzu YangKey Observation:the(bio)chemical pr