Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains
use AI, including molecular simulation for drug discovery. In this review, we provide an …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

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 …

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 …

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 …

The goal of structure-based drug discovery is to find small molecules that bind to a given
target protein. Deep learning has been used to generate drug-like molecules with certain …

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 …

A fundamental problem in drug discovery is to design molecules that bind to specific
proteins. To tackle this problem using machine learning methods, here we propose a novel …

De novo drug design is a stationary way to build novel ligands in the confined pocket of
receptor by assembling the atoms or fragments, while molecular dynamics (MD) simulation …

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with
high affinity and specificity to pre-determined protein targets. In this paper, we formulate …