Abstract Structure-based docking screens of large compound libraries have become
common in early drug and probe discovery. As computer efficiency has improved and …

With the recent explosion of chemical libraries beyond a billion molecules, more efficient
virtual screening approaches are needed. The Deep Docking (DD) platform enables up to …

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 …

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 …

Abstract Structure-based drug design (SBDD) aims to design small-molecule ligands that
bind with high affinity and specificity to pre-determined protein targets. Generative SBDD …

Deep generative models have achieved tremendous success in designing novel drug
molecules in recent years. A new thread of works have shown potential in advancing the …

We study a fundamental problem in structure-based drug design---generating molecules
that bind to specific protein binding sites. While we have witnessed the great success of …

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

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 …

Rich data and powerful machine learning models allow us to design drugs for a specific
protein target\textit {in silico}. Recently, the inclusion of 3D structures during targeted drug …