Relative molecule self-attention transformer.

The prediction of molecular properties is a crucial aspect in drug discovery that can save a lot of money and time during the drug design process. The use of machine learning methods to predict molecular properties has become increasingly popular in recent years. Despite advancements in the field, several challenges remain that need to be addressed, like finding an optimal pre-training procedure to improve performance on small datasets, which are common in drug discovery.

PRG4 deficiency in mice alters skeletal structure, mechanics, and calvarial osteoclastogenesis, and rhPRG4 inhibits in vitro osteoclastogenesis.

Osteoporosis is a chronic disease characterized by reduced bone mass and increased fracture risk, estimated to affect over 10 million people in the United States alone. Drugs used to treat bone loss often come with significant limitations and/or long-term safety concerns. Proteoglycan-4 (PRG4, also known as lubricin) is a mucin-like glycoprotein best known for its boundary lubricating function of articular cartilage.

Antibody-mediated NK cell activation as a correlate of immunity against influenza infection.

Antibodies play a critical role in protection against influenza; yet titers and viral neutralization represent incomplete correlates of immunity. Instead, the ability of antibodies to leverage the antiviral power of the innate immune system has been implicated in protection from and clearance of influenza infection. Here, post-hoc analysis of the humoral immune response to influenza is comprehensively profiled in a cohort of vaccinated older adults (65 + ) monitored for influenza infection during the 2012/2013 season in the United States (NCT: 01427309).

An Efficient Deep Neural Network to Classify Large 3D Images With Small Objects.

3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than their 2D counterparts. To be trained with high-resolution 3D images, convolutional neural networks resort to downsampling them or projecting them to 2D.

Generative Models Should at Least Be Able to Design Molecules That Dock Well: A New Benchmark.

Designing compounds with desired properties is a key element of the drug discovery process. However, measuring progress in the field has been challenging due to the lack of realistic retrospective benchmarks, and the large cost of prospective validation. To close this gap, we propose a benchmark based on docking, a widely used computational method for assessing molecule binding to a protein.