Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

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 …

Transformer models coupled with a simplified molecular line entry system (SMILES) have
recently proven to be a powerful combination for solving challenges in cheminformatics …

The clinical efficacy and safety of a drug is determined by its molecular properties and
targets in humans. However, proteome-wide evaluation of all compounds in humans, or …

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

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

Human prescription drug labeling contains a summary of the essential scientific information
needed for the safe and effective use of the drug and includes the Prescribing Information …

Molecule representation learning (MRL) methods aim to embed molecules into a real vector
space. However, existing SMILES-based (Simplified Molecular-Input Line-Entry System) or …

Retrosynthesis prediction is one of the fundamental challenges in organic synthesis. The
task is to predict the reactants given a core product. With the advancement of machine …

Predicting the biological properties of molecules is crucial in computer-aided drug
development, yet it's often impeded by data scarcity and imbalance in many practical …