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 …

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 …

Determining the properties of chemical molecules is essential for screening candidates
similar to a specific drug. These candidate molecules are further evaluated for their target …

Coordinate denoising is a promising 3D molecular pre-training method, which has achieved
remarkable performance in various downstream drug discovery tasks. Theoretically, the …

Molecular representation learning plays an essential role in cheminformatics. Recently,
language model-based approaches have gained popularity as an alternative to traditional …

Inspired by the evolution of biological systems, genetic algorithms have been applied to
generate solutions for optimization problems in a variety of scientific and engineering …

Molecular representation learning is fundamental for many drug related applications. Most
existing molecular pre-training models are limited in using single molecular modality, either …

Protein language models have demonstrated significant potential in the field of protein
engineering. However, current protein language models primarily operate at the residue …

Predicting drug-target interactions (DTIs) is crucial for drug discovery, and heavily relies on
supervised learning techniques. Supervised learning algorithms for DTI prediction use …

Protein language models have demonstrated significant potential in the field of protein
engineering. However, current protein language models primarily operate at the residue …