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 …

Virtual screening consists of using computational tools to predict potentially bioactive
compounds from files containing large libraries of small molecules. Virtual screening is …

In qualitative or quantitative studies of structure–activity relationships (SARs), machine
learning (ML) models are trained to recognize structural patterns that differentiate between …

Although extending the reactivity of a given class of molecules is relatively straightforward,
the discovery of genuinely new reactivity and the molecules that result is a wholly more …

Chemical space is impractically large, and conventional structure-based virtual screening
techniques cannot be used to simply search through the entire space to discover effective …

The discovery of bioactive molecules is an expensive and time-consuming process and new
strategies are continuously searched for in order to optimize this process. Virtual Screening …

The development of computational tools to support organic synthesis, including the
prediction of reaction pathways, optimization and selectivity, is a topic of intense current …

Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare
including research and chemical discoveries. Using large amounts of aggregated data, the …

Introduction: Docking and structure-based virtual screening (VS) have been standard
approaches in structure-based design for over two decades. However, our understanding of …

Compound activity prediction is a major application of machine learning (ML) in
pharmaceutical research. Conventional single-task (ST) learning aims to predict active …