Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Chemical engineers rely on models for design, research, and daily decision-making, often
with potentially large financial and safety implications. Previous efforts a few decades ago to …

Drug development is time‐consuming and expensive. Repurposing existing drugs for new
therapies is an attractive solution that accelerates drug development at reduced …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

With the rapid progress of AI in both academia and industry, Deep Learning has been widely
introduced into various areas in drug discovery to accelerate its pace and cut R&D costs …

Multitargeting compounds comprising activity on more than a single biological target have
gained remarkable relevance in drug discovery owing to the complexity of multifactorial …

We introduce a convolutional neural network that operates directly on graphs. These
networks allow end-to-end learning of prediction pipelines whose inputs are graphs of …

Despite the fact that deep learning has achieved remarkable success in various domains
over the past decade, its application in molecular informatics and drug discovery is still …

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural
networks such as deep neural networks or recurrent networks drive this area. Numerous …