Prediction of drug-target binding affinity based on multi-scale feature fusion.

Accurate prediction of drug-target binding affinity (DTA) plays a pivotal role in drug discovery and repositioning. Although deep learning methods are widely used in DTA prediction, two significant challenges persist: (i) how to effectively represent the complex structural information of proteins and drugs; (ii) how to precisely model the mutual interactions between protein binding sites and key drug substructures. To address these challenges, we propose a MSFFDTA (Multi-scale feature fusion for predicting drug target affinity) model, in which multi-scale encoders effectively capture multi-level structural information of drugs and proteins are designed.

NTCdb: Single-cell transcriptome database of human inflammatory-associated diseases.

With both the advancement of technology and the decline in costs, single-cell transcriptomics sequencing has become widespread in the biomedical area in recent years. It can facilitate the pathogenic characteristics at the single-cell level, which will assist clinical researchers in exploring the mechanism of diseases. As a result, single-cell transcriptome data based on clinical samples grew exponentially.

Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study.

Background: Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients.

ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection.

It is a critical step in lead optimization to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. Classical single-task learning (STL) has effectively predicted individual ADMET endpoints with abundant labels. Conversely, multi-task learning (MTL) can predict multiple ADMET endpoints with fewer labels, but ensuring task synergy and highlighting key molecular substructures remain challenges.

CMMS-GCL: cross-modality metabolic stability prediction with graph contrastive learning.

Motivation: Metabolic stability plays a crucial role in the early stages of drug discovery and development. Accurately modeling and predicting molecular metabolic stability has great potential for the efficient screening of drug candidates as well as the optimization of lead compounds. Considering wet-lab experiment is time-consuming, laborious, and expensive, in silico prediction of metabolic stability is an alternative choice.

Spatial Omics Sequencing Based on Microfluidic Array Chips.

Spatial profiling technologies fill the gap left by the loss of spatial information in traditional single-cell sequencing, showing great application prospects. After just a few years of quick development, spatial profiling technologies have made great progress in resolution and simplicity. This review introduces the development of spatial omics sequencing based on microfluidic array chips and describes barcoding strategies using various microfluidic designs with simplicity and efficiency.

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction.

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process.

CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties.

Motivation: Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties.

Results: To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder.

HnRNP A2/B1 as a potential anti-tumor target for triptolide based on a simplified thermal proteome profiling method using XGBoost.

Background: Triptolide (TP) is a highly active natural medicinal ingredient with significant potential in anticancer. The strong cytotoxicity of this compound suggests that it may have a wide range of targets within cells. However, further target screening is required at this stage.

Attention-based cross domain graph neural network for prediction of drug-drug interactions.

Drug-drug interactions (DDI) may lead to adverse reactions in human body and accurate prediction of DDI can mitigate the medical risk. Currently, most of computer-aided DDI prediction methods construct models based on drug-associated features or DDI network, ignoring the potential information contained in drug-related biological entities such as targets and genes. Besides, existing DDI network-based models could not make effective predictions for drugs without any known DDI records.

Few-Shot Drug Synergy Prediction With a Prior-Guided Hypernetwork Architecture.

Predicting drug synergy is critical to tailoring feasible drug combination treatment regimens for cancer patients. However, most of the existing computational methods only focus on data-rich cell lines, and hardly work on data-poor cell lines. To this end, here we proposed a novel few-shot drug synergy prediction method (called HyperSynergy) for data-poor cell lines by designing a prior-guided Hypernetwork architecture, in which the meta-generative network based on the task embedding of each cell line generates cell line dependent parameters for the drug synergy prediction network.

A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions.

Current machine learning-based methods have achieved inspiring predictions in the scenarios of mono-type and multi-type drug-drug interactions (DDIs), but they all ignore enhancive and depressive pharmacological changes triggered by DDIs. In addition, these pharmacological changes are asymmetric since the roles of two drugs in an interaction are different. More importantly, these pharmacological changes imply significant topological patterns among DDIs.

DGANDDI: Double Generative Adversarial Networks for Drug-Drug Interaction Prediction.

Co-administration of multiple drugs may cause adverse drug interactions and side effects that damage the body. Therefore, accurate prediction of drug-drug interaction (DDI) events is of great importance. Recently, many computational methods have been proposed for predicting DDI associated events.

MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction.

Motivation: During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other.

Corrigendum: NNAN: Nearest Neighbor Attention Network to Predict Drug-Microbe Associations.

[This corrects the article DOI: 10.3389/fmicb.2022.

STNN-DDI: a Substructure-aware Tensor Neural Network to predict Drug-Drug Interactions.

Computational prediction of multiple-type drug-drug interaction (DDI) helps reduce unexpected side effects in poly-drug treatments. Although existing computational approaches achieve inspiring results, they ignore to study which local structures of drugs cause DDIs, and their interpretability is still weak. In this paper, by supposing that the interactions between two given drugs are caused by their local chemical structures (substructures) and their DDI types are determined by the linkages between different substructure sets, we design a novel Substructure-aware Tensor Neural Network model for DDI prediction (STNN-DDI).

NNAN: Nearest Neighbor Attention Network to Predict Drug-Microbe Associations.

Many drugs can be metabolized by human microbes; the drug metabolites would significantly alter pharmacological effects and result in low therapeutic efficacy for patients. Hence, it is crucial to identify potential drug-microbe associations (DMAs) before the drug administrations. Nevertheless, traditional DMA determination cannot be applied in a wide range due to the tremendous number of microbe species, high costs, and the fact that it is time-consuming.

Directed graph attention networks for predicting asymmetric drug-drug interactions.

It is tough to detect unexpected drug-drug interactions (DDIs) in poly-drug treatments because of high costs and clinical limitations. Computational approaches, such as deep learning-based approaches, are promising to screen potential DDIs among numerous drug pairs. Nevertheless, existing approaches neglect the asymmetric roles of two drugs in interaction.

Compteromesa Sther (Diptera, Chironomidae, Prodiamesinae) newly recorded in China with the description of a new species.

Compteromesa haradensis Niitsuma et Makarchenko, 1997 is redescribed based on the male adults, pupae and larvae collected from China. Supplemental data are provided here for emending the previous inaccurate descriptions. In addition, a new species, Compteromesa biramosa Tang et Niitsuma, sp.

The Role of Autophagy in Tumor Immune Infiltration in Colorectal Cancer.

Objective: This study is aimed at exploring the association between autophagy and tumor immune infiltration (TII) in colorectal cancer (CRC).

Methods And Materials: We downloaded the transcriptome profiling and clinical data for CRC from The Cancer Genome Atlas (TCGA) database and obtained the normal colon transcriptome profiling data from Genotype-Tissue Expression Project (GTEx) database. The list of autophagy-related signatures was obtained from the Human Autophagy Database.

Compound-protein interaction prediction by deep learning: Databases, descriptors and models.

The screening of compound-protein interactions (CPIs) is one of the most crucial steps in finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address intrinsic limitations of traditional HTS and virtual screening with the advantage of low cost and high efficiency. This review provides a comprehensive survey of DL-based CPI prediction.