WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking.

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery. Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, .

SuperWater: Predicting Water Molecule Positions on Protein Structures by Generative AI.

Water molecules play a significant role in maintaining protein structural stability and facilitating molecular interactions. Accurate prediction of water molecule positions around protein structures is essential for understanding their biological roles and has significant implications for protein engineering and drug discovery. Here, we introduce SuperWater, a novel generative AI framework that integrates a score-based diffusion model with equivariant graph neural networks to predict water molecule placements around proteins with high accuracy.

Oxidation and Damage Mechanisms of Second-Generation Highly Cross-Linked Polyethylene Tibial Inserts.

Background: After clinical introduction in 2005, sequentially annealed, highly cross-linked polyethylene (SA HXLPE) was studied for retrievals with short implantation times; however, long-term follow-ups are lacking. The objective of this study was to examine and compare the revision reasons, damage mechanisms, and oxidation indices of SA HXLPE and conventional gamma inert-sterilized (Gamma Inert) ultra-high-molecular-weight polyethylene tibial inserts implanted for >5 years.

Methods: There were 74 total knee arthroplasty tibial inserts (46 SA HXLPEs, 28 Gamma Inerts) implanted for >5 years (mean 7 ± 2 years) retrieved as part of a multicenter retrieval program.

Femoral Head Penetration Rates and Oxidation of Highly Cross-Linked Polyethylene Hip Liners Implanted More Than 10 Years.

Background: Highly cross-linked polyethylene (HXLPE) was introduced to improve wear in total hip arthroplasty, with manufacturers implementing different thermal treatments to reduce oxidation. It is important to understand how long-term time in vivo affects the wear of these materials. The purpose of this study was to investigate the wear and oxidative performance of first-generation HXLPE hip inserts implanted for greater than 10 years and compare annealed and remelted HXLPE formulations.

NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics.

Machine learning provides a valuable tool for analyzing high-dimensional functional neuroimaging data, and is proving effective in predicting various neurological conditions, psychiatric disorders, and cognitive patterns. In functional magnetic resonance imaging (MRI) research, interactions between brain regions are commonly modeled using graph-based representations. The potency of graph machine learning methods has been established across myriad domains, marking a transformative step in data interpretation and predictive modeling.