The prediction of drug-target interactions (DTIs) is a vital step in drug discovery. The success
of machine learning and deep learning methods in accurately predicting DTIs plays a huge …

Abstract Background The Drug–Target Interaction (DTI) prediction uses a drug molecule and
a protein sequence as inputs to predict the binding affinity value. In recent years, deep …

The core of large-scale drug virtual screening is to select the binders accurately and
efficiently with high affinity from large libraries of small molecules in which non-binders are …

The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process
of pharmaceutical research and development, is progressing. By using the ability to process …

While research into drug–target interaction (DTI) prediction is fairly mature, generalizability
and interpretability are not always addressed in the existing works in this field. In this paper …

Receptor-mediated molecular initiating events (MIEs) and their relevance in endocrine
activity (EA) have been highlighted in literature. More than 15 receptors have been …

Introduction Drug-target interaction prediction is one important step in drug research and
development. Experimental methods are time consuming and laborious. Methods In this …

The exploration of non-covalent interactions between drugs and proteins (NCIs) has
significantly improved the performance of drug–target interactions (DTIs) prediction models …

The persistent challenge of bias in machine learning models necessitates robust solutions to
ensure parity and equal treatment across diverse groups, particularly in classification tasks …

The purpose of drug repurposing is to leverage previously approved drugs for a particular
disease indication and apply them to another disease. It can be seen as a faster and more …