The transformer-based language models, including vanilla transformer, BERT and GPT-3,
have achieved revolutionary breakthroughs in the field of natural language processing …

In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our
approach to complex data. This review explores the attention mechanism and its extended …

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

Elucidating and accurately predicting the druggability and bioactivities of molecules plays a
pivotal role in drug design and discovery and remains an open challenge. Recently, graph …

Molecular property prediction (MPP) is vital in drug discovery and drug reposition. Deep
learning-based MPP models capture molecular property-related features from various …

The application of Explainable Artificial Intelligence (XAI) in the field of chemistry has
garnered growing interest for its potential to justify the prediction of black-box machine …

A transformer is the foundational architecture behind large language models designed to
handle sequential data by using mechanisms of self‐attention to weigh the importance of …

Artificial intelligence-based molecular property prediction plays a key role in molecular
design such as bioactive molecules and functional materials. In this study, we propose a self …

Molecular Property Prediction (MPP) is vital for drug discovery, crop protection, and
environmental science. Over the last decades, diverse computational techniques have been …

Drug discovery plays a critical role in advancing human health by developing new
medications and treatments to combat diseases. How to accelerate the pace and reduce the …