Protein–ligand interactions are increasingly profiled at high throughput using affinity
selection and massively parallel sequencing. However, these assays do not provide the …

Accurate in silico prediction of protein–ligand binding affinity is important in the early stages
of drug discovery. Deep learning-based methods exist but have yet to overtake more …

Data driven computational approaches to predicting protein–ligand binding are currently
achieving unprecedented levels of accuracy on held-out test datasets. Up until now …

Protein-protein interaction networks and biology—what's the connection? | Nature Biotechnology
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New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …

The prediction of protein–ligand binding affinity has recently been improved remarkably by
machine-learning-based scoring functions. For example, using a set of simple descriptors …

Motivation Machine learning scoring functions for protein–ligand binding affinity prediction
have been found to consistently outperform classical scoring functions. Structure-based …

Predicting protein-ligand interactions using artificial intelligence (AI) models has attracted
great interest in recent years. However, data-driven AI models unequivocally suffer from a …

Motivation: Identifying protein–protein interactions is critical for understanding cellular
processes. Because protein domains represent binding modules and are responsible for the …

Protein–ligand interactions (PLIs) are essential for biochemical functionality and their
identification is crucial for estimating biophysical properties for rational therapeutic design …