Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across …

The discovery of new drugs has important implications for human health. Traditional
methods for drug discovery rely on experiments to optimize the structure of lead molecules …

Diffusion models, as a novel generative paradigm, have achieved remarkable success in
various image generation tasks such as image inpainting, image-to-text translation, and …

Abstract Structure-based generative chemistry is essential in computer-aided drug discovery
by exploring a vast chemical space to design ligands with high binding affinity for targets …

This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …

Molecule generation is a very important practical problem, with uses in drug discovery and
material design, and AI methods promise to provide useful solutions. However, existing …

Diffusion models have become a new SOTA generative modeling method in various fields,
for which there are multiple survey works that provide an overall survey. With the number of …

Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the
noise issues from data cleaning perspective such as data resampling and reweighting, but …

Diffusion models have emerged as powerful tools for molecular generation, particularly in
the context of 3D molecular structures. Inspired by nonequilibrium statistical physics, these …

This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs
and their corresponding 3D atom arrangements. Unlike existing methods that rely on …