Inductive transfer of knowledge: application of multi-task learning and feature net approaches to model tissue-air partition coefficients.
Two inductive knowledge transfer approaches - multitask learning (MTL) and Feature Net (FN) - have been used to build predictive neural networks (ASNN) and PLS models for 11 types of tissue-air partition coefficients (TAPC). Unlike conventional single-task learning (STL) modeling focused only on a single target property without any relations to other properties, in the framework of inductive transfer approach, the individual models are viewed as nodes in the network of interrelated models built in parallel (MTL) or sequentially (FN). It has been demonstrated that MTL and FN techniques are extremely useful in structure-property modeling on small and structurally diverse data sets, when conventional STL modeling is unable to produce any predictive model. The predictive STL individual models were obtained for 4 out of 11 TAPC, whereas application of inductive knowledge transfer techniques resulted in models for 9 TAPC. Differences in prediction performances of the models as a function of the machine-learning method, and of the number of properties simultaneously involved in the learning, has been discussed.
Varnek
Alexandre
A
Laboratoire d'Infochimie, UMR 7177 CNRS, Universite Louis Pasteur, 4, rue B. Pascal, Strasbourg 67000, France. varnek@chimie.u-strasbg.fr
Gaudin
Cédric
C
Marcou
Gilles
G
Baskin
Igor
I
Pandey
Anil Kumar
AK
Tetko
Igor V
IV
D000388:Air; D000818:Animals; D001185:Artificial Intelligence; D016208:Databases, Factual; D006801:Humans; D048088:Informatics; D016018:Least-Squares Analysis; D016014:Linear Models; D008954:Models, Biological; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000737:chemistry / Q000493:pharmacokinetics; D021281:Quantitative Structure-Activity Relationship; D051381:Rats; D014018:Tissue Distribution
D009930:Organic Chemicals