Drug–target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug–target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negativ...
Accounting for the interactions between drugs and proteins may give rise to more accurate predictions of drug–target interactions (DTIs). In this work, we propose a novel end-to-end deep-learning-based model called AMMVF-DTIs, which incorporates the attention mechanism and multi-view fusion to...
Researchers in biology and computational sciences have sought to use machine learning (ML) to efficiently predict drug–target interactions (DTIs). In recent years, according to the emerging usefulness of pretrained models in natural language process (NLPs), pretrained models are being developed for ...
Then, we analyzed the competitiveTnP–target interaction dynamics based on the affinity similarity between ITA4 and VCAM-1 interaction using a computational prediction of drug–target interactions (DTIs). The simulations were carried out using the Hdock Serve model, according to Gao and Skolnick [25...