Prediction of the glass transition temperature of (meth)acrylic polymers containing phenyl groups by recursive neural network
ABSTRACT A recursive neural network QSPR model that can take directly molecular structures as input was applied to the prediction of the glass transition temperature of 277 poly(meth)acrylates. This model satisfactorily predicted the chemical–physical properties of high and low molecular weight acyclic compounds. However, side-chain benzene rings are present in about one half of the selected polymers. In order to render cyclic structures, the molecular representation through hierarchical structures was extended by two methods, named group and cycle breaking, respectively. The latter approach exploits standard unique molecular description systems, i.e. Unique SMILES and InChI. In all cases the prediction was very good, with 15–16 K mean absolute error and 19–21 K standard deviation. This result confirms the robustness of our method with respect to the inclusion of different structures. Moreover, the good performance of the cycle breaking representation paves the way for the investigation of data sets that contain a variety of poorly sampled cyclic structures.
Article: Prediction of Polymer Properties
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ABSTRACT: An application of recursive cascade correlation (CC) neural networks to quantitative structure-activity relationship (QSAR) studies is presented, with emphasis on the study of the internal representations developed by the neural networks. Recursive CC is a neural network model recently proposed for the processing of structured data. It allows the direct handling of chemical compounds as labeled ordered directed graphs, and constitutes a novel approach to QSAR. The adopted representation of molecular structure captures, in a quite general and flexible way, significant topological aspects and chemical functionalities for each specific class of molecules showing a particular chemical reactivity or biological activity. A class of 1,4-benzodiazepin-2-ones is analyzed by the proposed approach. It compares favorably versus the traditional QSAR treatment based on equations. To show the ability of the model in capturing most of the structural features that account for the biological activity, the internal representations developed by the networks are analyzed by principal component analysis. This analysis shows that the networks are able to discover relevant structural features just on the basis of the association between the molecular morphology and the target property (affinity).Journal of Chemical Information and Computer Sciences 01/2001; 41:202-218.
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ABSTRACT: Three azobenzene-containing side-chain liquid crystalline polymers (SCLCPs) were grafted onto a styrene−butadiene−styrene (SBS) triblock copolymer to yield photoactive thermoplastic elastomers. The SCLCPs used were a polymethacrylate and two polyacrylates having different glass and phase transition temperatures. We have investigated the stretching-induced orientation of azobenzene mesogens, the orientation erasure by UV irradiation, and the consequences on the formation of diffraction gratings on stretched films. The results show that a combination of high degree of orientation of trans-azobenzene in nonirradiated areas with an efficient photoisomerization leading to disordered cis-azobenzene in irradiated areas is necessary for an efficient diffraction grating. However, this is not the only mechanism responsible for the formation of grating. Changes in the anisotropic morphology of stretched SBS may occur in these elastomers as a result of the photoisomerization process, which contributes to the formation of grating in films under strain and accounts for the stable diffraction grating remained in the relaxed state. These azobenzene elastomers can be used to record gratings that display reversible changes in diffraction angle and efficiency and the diffraction efficiency of which also depends strongly on the polarization of the probe light as well as the alignment of the fringes with respect to the strain of the film.Macromolecules. 11/2002; 35(26).