miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA.

MicroRNAs (miRNAs) are critical regulators in various biological processes to cleave or repress translation of messenger RNAs (mRNAs). Accurately predicting miRNA targets is essential for developing miRNA-based therapies for diseases such as cancer and cardiovascular disease. Traditional miRNA target prediction methods often struggle due to incomplete knowledge of miRNA-target interactions and lack interpretability.

Fine-tuning large language models for chemical text mining.

Extracting knowledge from complex and diverse chemical texts is a pivotal task for both experimental and computational chemists. The task is still considered to be extremely challenging due to the complexity of the chemical language and scientific literature. This study explored the power of fine-tuned large language models (LLMs) on five intricate chemical text mining tasks: compound entity recognition, reaction role labelling, metal-organic framework (MOF) synthesis information extraction, nuclear magnetic resonance spectroscopy (NMR) data extraction, and the conversion of reaction paragraphs to action sequences.

Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.

Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning.

TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy.

Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy.

Computing the relative binding affinity of ligands based on a pairwise binding comparison network.

Structure-based lead optimization is an open challenge in drug discovery, which is still largely driven by hypotheses and depends on the experience of medicinal chemists. Here we propose a pairwise binding comparison network (PBCNet) based on a physics-informed graph attention mechanism, specifically tailored for ranking the relative binding affinity among congeneric ligands. Benchmarking on two held-out sets (provided by Schrödinger and Merck) containing over 460 ligands and 16 targets, PBCNet demonstrated substantial advantages in terms of both prediction accuracy and computational efficiency.

KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.

Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases.

Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation.

Three-dimensional (3D) conformations of a small molecule profoundly affect its binding to the target of interest, the resulting biological effects, and its disposition in living organisms, but it is challenging to accurately characterize the conformational ensemble experimentally. Here, we proposed an autoregressive torsion angle prediction model Tora3D for molecular 3D conformer generation. Rather than directly predicting the conformations in an end-to-end way, Tora3D predicts a set of torsion angles of rotatable bonds by an interpretable autoregressive method and reconstructs the 3D conformations from them, which keeps structural validity during reconstruction.

Structural Optimization of Fibroblast Growth Factor Receptor Inhibitors for Treating Solid Tumors.

Small-molecule fibroblast growth factor receptor (FGFR) inhibitors have emerged as a promising antitumor therapy. Herein, by further optimizing the lead compound under the guidance of molecular docking, we obtained a series of novel covalent FGFR inhibitors. After careful structure-activity relationship analysis, several compounds were identified to exhibit strong FGFR inhibitory activity and relatively better physicochemical and pharmacokinetic properties compared with those of .

143D, a novel selective KRAS inhibitor exhibits potent antitumor activity in preclinical models.

The KRAS mutant has emerged as an important therapeutic target in recent years. Covalent inhibitors have shown promising antitumor activity against KRAS-mutant cancers in the clinic. In this study, a structure-based and focused chemical library analysis was performed, which led to the identification of 143D as a novel, highly potent and selective KRAS inhibitor.

[Study on prevalence and correlation factors of bronchial asthma in Zaozhuang area, Shandong province].

Objective: To study the prevalence of asthma and its correlated factors in Zaozhuang area in 2003, to provide a basic consideration for prevention/treatment and control policy.

Methods: 6 points were selected by stratified-clusterd-random sampling with a total of 16,725 persons expected, but only 10,610 subjects investigated.

Results: In this survey, 128 asthma cases were identified with a overall prevalence of 1.