Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

As graph neural networks are becoming more and more powerful and useful in the field of
drug discovery, many pharmaceutical companies are getting interested in utilizing these …

Deep learning models can accurately predict molecular properties and help making the
search for potential drug candidates faster and more efficient. Many existing methods are …

Recently, attention mechanism and derived models have gained significant traction in drug
development due to their outstanding performance and interpretability in handling complex …

Abstract Knowledge graph embedding has been an active research topic for knowledge
base completion, with progressive improvement from the initial TransE, TransH, DistMult et …

Drug discovery based on artificial intelligence has been in the spotlight recently as it
significantly reduces the time and cost required for developing novel drugs. With the …

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 …

Graph-structured data arise naturally in many different application domains. By representing
data as graphs, we can capture entities (ie, nodes) as well as their relationships (ie, edges) …

Constructing proper representations of molecules lies at the core of numerous tasks such as
molecular property prediction and drug design. Graph neural networks, especially message …

Reviews on online shopping websites affect the buying decisions of customers, meanwhile,
attract lots of spammers aiming at misleading buyers. Xianyu, the largest second-hand …