Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Ligand docking is one of the core technologies in structure-based virtual screening for drug
discovery. However, conventional docking tools and existing deep learning tools may suffer …

AlphaFold2 is a promising new tool for researchers to predict protein structures and
generate high‐quality models, with low backbone and global root‐mean‐square deviation …

Molecular simulations are an essential asset in the first steps of drug design campaigns.
However, the requirement of high-throughput limits applications mainly to qualitative …

Flexible modeling of the protein–ligand complex structure is a fundamental challenge for in
silico drug development. Recent studies have improved commonly used docking tools by …

Understanding the mechanism of action of a protein or designing better ligands for it, often
requires access to a bound (holo) and an unbound (apo) state of the protein. Resources for …

Accurate prediction of protein-ligand binding structures, a task known as molecular docking
is crucial for drug design but remains challenging. While deep learning has shown promise …

This research introduces new machine learning and deep learning approaches, collectively
referred to as Big Data analytics techniques that are unique to address the protein …

Drug discovery involves the process of designing molecules that interact well with a given
target protein structure in order to modulate to its function. With large scale availability of 3D …

Docking simulation, a key technique in virtual screening, typically treats proteins as rigid
bodies. However, proteins are inherently flexible, and ligand binding can induce significant …