Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …

Molecular docking has been widely employed as a fast and inexpensive technique in the
past decades, both in academic and industrial settings. Although this discipline has now had …

Predicting the binding structure of a small molecule ligand to a protein--a task known as
molecular docking--is critical to drug design. Recent deep learning methods that treat …

Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …

AutoDock Vina is arguably one of the fastest and most widely used open-source programs
for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks …

Illuminating interactions between proteins and small drug molecules is a long-standing
challenge in the field of drug discovery. Despite the importance of understanding these …

Molecular docking computationally predicts the conformation of a small molecule when
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …

On average, an approved drug currently costs US $2–3 billion and takes more than 10 years
to develop. In part, this is due to expensive and time-consuming wet-laboratory experiments …

Deep generative models are attracting much attention in the field of de novo molecule
design. Compared to traditional methods, deep generative models can be trained in a fully …

Therapeutics machine learning is an emerging field with incredible opportunities for
innovatiaon and impact. However, advancement in this field requires formulation of …