Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break.

The artificial intelligence-based prediction of the mechanical properties derived from the tensile test plays a key role in assessing the application profile of new polymeric materials, especially in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure-property relationship (QSPR) model for tensile strength at break is presented in this work.

Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RV Algorithm Using Synthetic Polydisperse Data Sets.

The feature selection (FS) process is a key step in the Quantitative Structure-Property Relationship (QSPR) modeling of physicochemical properties in cheminformatics. In particular, the inference of QSPR models for polymeric material properties constitutes a complex problem because of the uncertainty introduced by the polydispersity of these materials. The main challenge is how to capture the polydispersity information from the molecular weight distribution (MWD) curve to achieve a more effective computational representation of polymeric materials.

QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease.

Alzheimer's disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods.

Second ISCB Latin American Student Council Symposium (LA-SCS) 2016.

This report summarizes the scientific content and activities of the second edition of the Latin American Symposium (LA-SCS), organized by the Student Council (SC) of the International Society for Computational Biology (ISCB), held in conjunction with the Fourth Latin American conference from the International Society for Computational Biology (ISCB-LA 2016) in Buenos Aires, Argentina, on November 19, 2016.

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.

Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property plays a critical role. Two main general approaches can be used for this modeling procedure: feature selection and feature learning. In this paper, a performance comparative study of two state-of-art methods related to these two approaches is carried out.

Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design.

Several feature extraction approaches for QSPR modelling in Cheminformatics are discussed in this paper. In particular, this work is focused on the use of these strategies for predicting mechanical properties, which are relevant for the design of polymeric materials. The methodology analysed in this study employs a feature learning method that uses a quantification process of 2D structural characterization of materials with the autoencoder method.