While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …

In the real world all events are connected. There is a hidden network of dependencies that
governs behavior of natural processes. Without much argument it can be said that, of all the …

Having a compact yet robust structurally based identifier or representation system is a key
enabling factor for efficient sharing and dissemination of research results within the …

In recent years the development of computational techniques that build models to correctly
assign chemical compounds to various classes or to retrieve potential drug-like compounds …

Mining frequent trees is very useful in domains like bioinformatics, web mining, mining
semistructured data, and so on. We formulate the problem of mining (embedded) subtrees in …

Computational techniques that build models to correctly assign chemical compounds to
various classes of interest have many applications in pharmaceutical research and are used …

Graph-based modeling has emerged as a powerful abstraction capable of capturing in a
single and unified framework many of the relational, spatial, topological, and other …

We present an algorithm to find fragments in a set of molecules that help to discriminate
between different classes of for instance, activity in a drug discovery context. Instead of …

How can we generate realistic graphs? In addition, how can we do so with a mathematically
tractable model that makes it feasible to analyze their properties rigorously? Real graphs …

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 …