Developing new drugs is too expensive and time-consuming. Accurately predicting the
interaction between drugs and targets will likely change how the drug is discovered …

Understanding protein sequence and structure is essential for understanding protein–
protein interactions (PPIs), which are essential for many biological processes and diseases …

We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug
discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed …

Artificial intelligence (AI) has gained significant traction in the field of drug discovery, with
deep learning (DL) algorithms playing a crucial role in predicting protein–ligand binding …

The protein–ligand binding free energy is a central quantity in structure-based
computational drug discovery efforts. Although popular alchemical methods provide sound …

Protein–ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery.
Recently, various deep learning-based models predict binding affinity by incorporating the …

Predicting the binding affinity of protein–ligand complexes is crucial for computer-aided drug
discovery (CADD) and the identification of potential drug candidates. The deep learning …

Accurate and rapid prediction of protein–ligand interactions (PLIs) is the fundamental
challenge of drug discovery. Deep learning methods have been harnessed for this purpose …

Abstract Structure‐based drug design is a widely applied approach in the discovery of new
lead compounds for known therapeutic targets. In most structure‐based drug design …

The enormous growth in the amount of data generated by the life sciences is continuously
shifting the field from model-driven science towards data-driven science. The need for …