Optimal design of polymers is a challenging task due to their enormous chemical and
configurational space. Recent advances in computations, machine learning, and increasing …

Introduction: Neural networks are becoming a very popular method for solving machine
learning and artificial intelligence problems. The variety of neural network types and their …

Shallow machine learning methods have been applied to chemoinformatics problems with
some success. As more data becomes available and more complex problems are tackled …

Many underlying relationships among data in several areas of science and engineering, eg,
computer vision, molecular chemistry, molecular biology, pattern recognition, and data …

Increased availability of large repositories of chemical compounds is creating new
challenges and opportunities for the application of machine learning methods to problems in …

Elucidating and accurately predicting the druggability and bioactivities of molecules plays a
pivotal role in drug design and discovery and remains an open challenge. Recently, graph …

This paper is focused on modern approaches to machine learning, most of which are as yet
used infrequently or not at all in chemoinformatics. Machine learning methods are …

We describe a general methodology for the design of large-scale recursive neural network
architectures (DAG-RNNs) which comprises three fundamental steps:(1) representation of a …

Motivation: Small molecules play a fundamental role in organic chemistry and biology. They
can be used to probe biological systems and to discover new drugs and other useful …

Recent developments in the area of neural networks produced models capable of dealing
with structured data. Here, we propose the first fully unsupervised model, namely an …