The number of 'small'molecules that may be of interest to chemical biologists—chemical
space—is enormous, but the fraction that have ever been made is tiny. Most strategies are …

Recent advances in the field of artificial intelligence, specifically regarding deep learning
methods, have invigorated research into novel ways for the exploration of chemical space …

The rise and fall of artificial neural networks is well documented in the scientific literature of
both computer science and computational chemistry. Yet almost two decades later, we are …

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 …

The success of deep machine learning in processing of large amounts of data, for example,
in image or voice recognition and generation, raises the possibilities that these tools can …

The question of molecular similarity is core in cheminformatics and is usually assessed via a
pairwise comparison based on vectors of properties or molecular fingerprints. We recently …

Recent advances in machine learning or artificial intelligence for vision and natural
language processing that have enabled the development of new technologies such as …

Machine learning (ML) has emerged as a general, problem-solving paradigm with many
applications in computer vision, natural language processing, digital safety, or medicine. By …

Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Deep learning is becoming a standard tool in chemistry and materials science. Although
there are learning materials available for deep learning, none cover the applications in …