Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

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 …

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 …

This review paper comprehensively examines the dynamic landscape of 3D printing and
Machine Learning utilizing biodegradable polymers and their composites, presenting a …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

AI-based molecule generation provides a promising approach to a large area of biomedical
sciences and engineering, such as antibody design, hydrolase engineering, or vaccine …

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 …

Generating molecules that bind to specific proteins is an important but challenging task in
drug discovery. Most previous works typically generate atoms autoregressively, with element …

Molecular representation pretraining is critical in various applications for drug and material
discovery due to the limited number of labeled molecules, and most existing work focuses …

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 …