Drug discovery and development pipelines are long, complex and depend on numerous
factors. Machine learning (ML) approaches provide a set of tools that can improve discovery …

Recent advances in computer vision and machine learning underpin a collection of
algorithms with an impressive ability to decipher the content of images. These deep learning …

Molecular machine learning bears promise for efficient molecular property prediction and
drug discovery. However, labelled molecule data can be expensive and time consuming to …

Molecular graph representation learning is a fundamental problem in modern drug and
material discovery. Molecular graphs are typically modeled by their 2D topological …

How to obtain informative representations of molecules is a crucial prerequisite in AI-driven
drug design and discovery. Recent researches abstract molecules as graphs and employ …

Advancing research in the emerging field of deep graph learning requires new tools to
support tensor computation over graphs. In this paper, we present the design principles and …

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides
opportunities for the discovery and development of innovative drugs. Various machine …

Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The
widespread adoption of machine learning, in particular deep learning, in multiple scientific …

Machine learning enables computers to address problems by learning from data. Deep
learning is a type of machine learning that uses a hierarchical recombination of features to …

We introduce our efforts towards building a universal neural machine translation (NMT)
system capable of translating between any language pair. We set a milestone towards this …