An explainable deep learning platform for molecular discovery.

Deep learning approaches have been increasingly applied to the discovery of novel chemical compounds. These predictive approaches can accurately model compounds and increase true discovery rates, but they are typically black box in nature and do not generate specific chemical insights. Explainable deep learning aims to 'open up' the black box by providing generalizable and human-understandable reasoning for model predictions.

Deep generative design of RNA aptamers using structural predictions.

RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis. Deep learning approaches have aided in exploring chemical spaces; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics.

Discovering small-molecule senolytics with deep neural networks.

The accumulation of senescent cells is associated with aging, inflammation and cellular dysfunction. Senolytic drugs can alleviate age-related comorbidities by selectively killing senescent cells. Here we screened 2,352 compounds for senolytic activity in a model of etoposide-induced senescence and trained graph neural networks to predict the senolytic activities of >800,000 molecules.

Transcriptomic characterization of Lonrf1 at the single-cell level under pathophysiological conditions.

The LONRF family of proteins consists of three isozymes, LONRF1-3, which harbors RING (really interesting new gene) domain and Lon substrate binding domain. We have recently identified LONRF2 as a protein quality control ubiquitin ligase that acts predominantly in neurons. LONRF2 selectively ubiquitylates misfolded or damaged proteins for degradation.

Blocking PD-L1-PD-1 improves senescence surveillance and ageing phenotypes.

The accumulation of senescent cells is a major cause of age-related inflammation and predisposes to a variety of age-related diseases. However, little is known about the molecular basis underlying this accumulation and its potential as a target to ameliorate the ageing process. Here we show that senescent cells heterogeneously express the immune checkpoint protein programmed death-ligand 1 (PD-L1) and that PD-L1 senescent cells accumulate with age in vivo.

Senolysis by glutaminolysis inhibition ameliorates various age-associated disorders.

Removal of senescent cells (senolysis) has been proposed to be beneficial for improving age-associated pathologies, but the molecular pathways for such senolytic activity have not yet emerged. Here, we identified glutaminase 1 () as an essential gene for the survival of human senescent cells. The intracellular pH in senescent cells was lowered by lysosomal membrane damage, and this lowered pH induced kidney-type glutaminase (KGA) expression.

A 3D tissue model-on-a-chip for studying the effects of human senescent fibroblasts on blood vessels.

All human tissues experience aging that eventually causes organ dysfunction and disease. Cellular senescence was discovered in fibroblasts cultured in vitro. In adults, it is a primary defense mechanism against cancer, but also a major contributor to lifespan limits and disorders associated with aging.

Generation of a p16 Reporter Mouse and Its Use to Characterize and Target p16 Cells In Vivo.

Cell senescence plays a key role in age-associated organ dysfunction, but the in vivo pathogenesis is largely unclear. Here, we generated a p16-Cre-tdTomato mouse model to analyze the in vivo characteristics of p16 cells at a single-cell level. We found tdTomato-positive p16 cells detectable in all organs, which were enriched with age.