Modern therapeutic development often involves several stages that are interconnected, and
multiple iterations are usually required to bring a new drug to the market. Computational …

The past few years have witnessed enormous progress toward applying machine learning
approaches to the development of protein–ligand scoring functions. However, the robust …

Although there has been considerable progress in molecular property prediction in
computer-aided drug design, there is a critical need to have fast and accurate models. Many …

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …

Both phytohormone signaling and epigenetic mechanisms have long been known to play
crucial roles in plant development and plasticity in response to ambient stimuli. Indeed …

Applying machine learning algorithms to protein–ligand scoring functions has aroused
widespread attention in recent years due to the high predictive accuracy and affordable …

Despite the success of pretrained natural language processing (NLP) models in various
fields, their application in computational biology has been hindered by their reliance on …

Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …

The recently reported machine learning-or deep learning-based scoring functions (SFs)
have shown exciting performance in predicting protein–ligand binding affinities with fruitful …

Taste peptides, as an important component of protein-rich foodstuffs, potentiate the nutrition
and taste of food. Thereinto, umami-and bitter-taste peptides have been ex tensively …