Max Pereira

TL;DR: This study evaluates the use of several Machine Learning algorithms to find useful rules to the elucidation and prediction of toxicity using 1D and 2D molecular descriptors and indicates that machine learning algorithms can effectively use 1D Molecular descriptors to construct accurate and simple models.

TL;DR: The coupling of an ILP system with a molecular visualisation software that allows a chemist to graphically control the search for interesting patterns in chemical fragments is described and it is shown how structural information, such as rings, functional groups, and esters are integrated and exploited in the search.

TL;DR: A software tool that allows the use of Relational Learners in the task of identifying molecules or molecular fragments with potential to produce toxic effects, and thus help in stream-lining drug design in silico, and proposes ways of providing background knowledge for RelationalLearners using the results of Graph Mining algorithms.

TL;DR: The integration with a molecule visualisation software that allows the chemist to graphically control the search for interesting patterns in chemical fragments and how structured information, such as rings, functional groups, are integrated and exploited in LogCHEM.

TL;DR: In this article , multiwalled carbon nanotubes (MWCNTs) and poly (3,4-ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS) have been applied to cotton textile substrates by straightforward scalable dyeing and coating processes, respectively.