Anti-cancer drug design has been acknowledged as a complicated, expensive, time-
consuming, and challenging task. How to reduce the research costs and speed up the …

Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …

Virtual screening, which identifies potential drugs from vast compound databases to bind
with a particular protein pocket, is a critical step in AI-assisted drug discovery. Traditional …

Deep learning methods that predict protein–ligand binding have recently been used for
structure-based virtual screening. Many such models have been trained using protein …

Combinational therapies with synergistic effects provide a powerful treatment strategy for
tackling complex diseases, particularly malignancies. Discovering these synergistic …

Multi-task learning (MTL) is a machine learning paradigm that aims to enhance the
generalization of predictive models by leveraging shared information across multiple tasks …

The integration of Artificial Intelligence (AI) into the field of drug discovery has been a
growing area of interdisciplinary scientific research. However, conventional AI models are …

Abstract Structure‐based drug design is a widely applied approach in the discovery of new
lead compounds for known therapeutic targets. In most structure‐based drug design …

We propose a linear time graph transformation that enables the Weisfeiler-Leman (WL) test
and message passing graph neural networks (MPNNs) to be maximally expressive on …

High-performance and high-throughput computing play an important role in drug
development and, in particular, in solving the computationally intensive problem of virtual …