Durian: A Comprehensive Benchmark for Structure-Based 3D Molecular Generation.

Three-dimensional (3D) molecular generation models employ deep neural networks to simultaneously generate both topological representation and molecular conformations. Due to their advantages in utilizing the structural and interaction information on targets, as well as their reduced reliance on existing bioactivity data, these models have attracted widespread attention. However, limited training and testing data sets and the unexpected biases inherent in single evaluation metrics pose a significant challenge in comparing these models in practical settings.

Unlocking comprehensive molecular design across all scenarios with large language model and unordered chemical language.

Molecular generation stands at the forefront of AI-driven technologies, playing a crucial role in accelerating the development of small molecule drugs. The intricate nature of practical drug discovery necessitates the development of a versatile molecular generation framework that can tackle diverse drug design challenges. However, existing methodologies often struggle to encompass all aspects of small molecule drug design, particularly those rooted in language models, especially in tasks like linker design, due to the autoregressive nature of large language model-based approaches.

Immature platelet fraction levels predict the development of prolonged thrombocytopenia after haematopoietic stem cell transplantation.

Prolonged thrombocytopenia (PT) is a serious complication after haematopoietic stem cell transplantation (HSCT). PT has been suggested to be associated with an increased platelet transfusion requirement and poor outcomes after transplantation. Due to the complex mechanism of PT development, it is difficult to diagnose in the early post-transplant period.

Exploring the role of NAA40 in immune infiltrates and prognostic prediction in hepatocellular carcinoma.

NAA40 belongs to the N-terminal acetyltransferase (NATs) family, responsible for protein N-terminal modification, and it exerts crucial roles across various cancers. However, its impact on patient prognosis and immune infiltration in hepatocellular carcinoma (HCC) remains elusive. To address this, our study delved into the comprehensive analysis of NAA40 in the context of cancer.

IL18R1-Related Molecules as Biomarkers for Asthma Severity and Prognostic Markers for Idiopathic Pulmonary Fibrosis.

To determine the role of inflammation-related proteins in predicting asthma severity and outcome, 92 inflammation-related proteins were measured in the asthmatic serum using Olink analysis. Different bioinformatics algorithms were developed to cross analyze with the single-cell or transcriptome data sets from the Gene Expression Omnibus database to explore the role of IL18R1 and related genes in asthma and idiopathic pulmonary fibrosis (IPF). Olink identified 52 differentially expressed proteins in asthma.

FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization.

Recently, deep generative models have been regarded as promising tools in fragment-based drug design (FBDD). Despite the growing interest in these models, they still face challenges in generating molecules with desired properties in low data regimes. In this study, we propose a novel flow-based autoregressive model named FFLOM for linker and R-group design.

Comprehensive analysis of immune-related genes for classification and immune microenvironment of asthma.

Objectives: To determine the effects of immune-related genes (IRGs) and immune landscape of induced sputum, and develop novel, non-invasive diagnostic molecular therapeutic targets for asthma.

Methods: GSE76262 datasets were used to identify differentially expressed IRGs in asthma. Key IRGs were detected using a protein-protein interaction network.

The Effect of Elevated Alanine Transaminase on Non-invasive Prenatal Screening Failures.

Objective: To determine the effects of alanine transaminase (ALT) levels on the screening failure rates or "no calls" due to low fetal fraction (FF) to obtain a result in non-invasive prenatal screening (NIPS).

Methods: NIPS by sequencing and liver enzyme measurements were performed in 7,910 pregnancies at 12-26 weeks of gestation. Univariate and multivariable regression models were used to evaluate the significant predictors of screening failure rates among maternal characteristics and relevant laboratory parameters.

Lipid Metabolism Affects Fetal Fraction and Screen Failures in Non-invasive Prenatal Testing.

To assess the association between lipid metabolism and fetal fraction, which is a critical factor in ensuring a highly accurate non-invasive prenatal testing (NIPT), and on the rate of screen failures or "no calls" in NIPT. A total of 4,514 pregnant women at 12-26 weeks of gestation underwent NIPT sequencing and serum lipid measurements. Univariate analysis and multivariate regression models were used to evaluate the associations of serum lipid concentrations with the fetal fraction and the rate of screen failures.