MolBiC: the cell-based landscape illustrating molecular bioactivities.

The measurement of cell-based molecular bioactivity (CMB) is critical for almost every step of drug development. With the booming application of AI in biomedicine, it is essential to have the CMB data to promote the learning of cell-based patterns for guiding modern drug discovery, but no database providing such information has been constructed yet. In this study, we introduce MolBiC, a knowledge base designed to describe valuable data on molecular bioactivity measured within a cellular context.

PDCdb: the biological activity and pharmaceutical information of peptide-drug conjugate (PDC).

Peptide-drug conjugates (PDCs) have emerged as a promising class of targeted therapeutics with substantial pharmaceutical advantages and market potentials, which is a combination of a peptide (selective to the disease-relevant target), a linker (stable in circulation but cleavable at target site) and a cytotoxic/radioactive drug (efficacious/traceable for disease). Among existing PDCs, those based on radiopharmaceuticals (a.k.

Optimizing Skin Surface Metabolomics: A Comprehensive Evaluation of Sampling Methods, Extraction Solvents, and Analytical Techniques.

Characterizing the metabolite fingerprint from the skin surface provides invaluable insights into skin biology and microbe-host interactions. To ensure data accuracy and reproducibility, it is essential to develop standard operating procedures for skin surface metabolomics. However, there is a notable lack of studies in this area.

CRUSE -An innovative mobile application for patient monitoring and management in chronic spontaneous urticaria.

Background: Chronic spontaneous urticaria (CSU) is unpredictable and can severely impair patients' quality of life. Patients with CSU need a convenient, user-friendly platform to complete patient-reported outcome measures (PROMs) on their mobile devices. CRUSE , the Chronic Urticaria Self Evaluation app, aims to address this unmet need.

RVvictor: Virus RNA-directed molecular interactions for RNA virus infection.

RNA viruses are major human pathogens that cause seasonal epidemics and occasional pandemic outbreaks. Due to the nature of their RNA genomes, it is anticipated that virus's RNA interacts with host protein (INTPRO), messenger RNA (INTmRNA), and non-coding RNA (INTncRNA) to perform their particular functions during their transcription and replication. In other words, thus, it is urgently needed to have such valuable data on virus RNA-directed molecular interactions (especially INTPROs), which are highly anticipated to attract broad research interests in the fields of RNA virus translation and replication.

Tripartite motif containing 26 prevents steatohepatitis progression by suppressing C/EBPδ signalling activation.

Currently potential preclinical drugs for the treatment of nonalcoholic steatohepatitis (NASH) and NASH-related pathopoiesis have failed to achieve expected therapeutic efficacy due to the complexity of the pathogenic mechanisms. Here we show Tripartite motif containing 26 (TRIM26) as a critical endogenous suppressor of CCAAT/enhancer binding protein delta (C/EBPδ), and we also confirm that TRIM26 is an C/EBPδ-interacting partner protein that catalyses the ubiquitination degradation of C/EBPδ in hepatocytes. Hepatocyte-specific loss of Trim26 disrupts liver metabolic homeostasis, followed by glucose metabolic disorder, lipid accumulation, increased hepatic inflammation, and fibrosis, and dramatically facilitates NASH-related phenotype progression.

SingPro: a knowledge base providing single-cell proteomic data.

Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet.

Virtual reality for neurorehabilitation: A bibliometric analysis of knowledge structure and theme trends.

Background: As an emerging technology, virtual reality (VR) has been broadly applied in the medical field, especially in neurorehabilitation. The growing application of VR therapy promotes an increasing amount of clinical studies. In this paper, we present a bibliometric analysis of the existing studies to reveal the current research hotspots and guide future research directions.

CovInter: interaction data between coronavirus RNAs and host proteins.

Coronavirus has brought about three massive outbreaks in the past two decades. Each step of its life cycle invariably depends on the interactions among virus and host molecules. The interaction between virus RNA and host protein (IVRHP) is unique compared to other virus-host molecular interactions and represents not only an attempt by viruses to promote their translation/replication, but also the host's endeavor to combat viral pathogenicity.

Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images.

Instance segmentation is of great importance for many biological applications, such as study of neural cell interactions, plant phenotyping, and quantitatively measuring how cells react to drug treatment. In this paper, we propose a novel box-based instance segmentation method. Box-based instance segmentation methods capture objects via bounding boxes and then perform individual segmentation within each bounding box region.

CDD/SPARCLE: functional classification of proteins via subfamily domain architectures.

NCBI's Conserved Domain Database (CDD) aims at annotating biomolecular sequences with the location of evolutionarily conserved protein domain footprints, and functional sites inferred from such footprints. An archive of pre-computed domain annotation is maintained for proteins tracked by NCBI's Entrez database, and live search services are offered as well. CDD curation staff supplements a comprehensive collection of protein domain and protein family models, which have been imported from external providers, with representations of selected domain families that are curated in-house and organized into hierarchical classifications of functionally distinct families and sub-families.

Target enhanced 2D similarity search by using explicit biological activity annotations and profiles.

Background: The enriched biological activity information of compounds in large and freely-accessible chemical databases like the PubChem Bioassay Database has become a powerful research resource for the scientific research community. Currently, 2D fingerprint based conventional similarity search (CSS) is the most common widely used approach for database screening, but it does not typically incorporate the relative importance of fingerprint bits to biological activity.

Results: In this study, a large-scale similarity search investigation has been carried out on 208 well-defined compound activity classes extracted from PubChem Bioassay Database.

PubChem Substance and Compound databases.

PubChem (https://pubchem.ncbi.nlm.

PubChem structure-activity relationship (SAR) clusters.

Background: Developing structure-activity relationships (SARs) of molecules is an important approach in facilitating hit exploration in the early stage of drug discovery. Although information on millions of compounds and their bioactivities is freely available to the public, it is very challenging to infer a meaningful and novel SAR from that information.

Results: Research discussed in the present paper employed a bioactivity-centered clustering approach to group 843,845 non-inactive compounds stored in PubChem according to both structural similarity and bioactivity similarity, with the aim of mining bioactivity data in PubChem for useful SAR information.

PubChem's BioAssay Database.

PubChem (http://pubchem.ncbi.nlm.

PubChem3D: a new resource for scientists.

Background: PubChem is an open repository for small molecules and their experimental biological activity. PubChem integrates and provides search, retrieval, visualization, analysis, and programmatic access tools in an effort to maximize the utility of contributed information. There are many diverse chemical structures with similar biological efficacies against targets available in PubChem that are difficult to interrelate using traditional 2-D similarity methods.

The Text-mining based PubChem Bioassay neighboring analysis.

Background: In recent years, the number of High Throughput Screening (HTS) assays deposited in PubChem has grown quickly. As a result, the volume of both the structured information (i.e.

Update of TTD: Therapeutic Target Database.

Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets, hundreds of which are targets of approved and clinical trial drugs. Knowledge of these targets and corresponding drugs, particularly those in clinical uses and trials, is highly useful for facilitating drug discovery. Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs.

A survey of across-target bioactivity results of small molecules in PubChem.

This work provides an analysis of across-target bioactivity results in the screening data deposited in PubChem. Two alternative approaches for grouping-related targets are used to examine a compound's across-target bioactivity. This analysis identifies compounds that are selectively active against groups of protein targets that are identical or similar in sequence.

What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical, and systems profiles of successful targets.

Low target discovery rate has been linked to inadequate consideration of multiple factors that collectively contribute to druggability. These factors include sequence, structural, physicochemical, and systems profiles. Methods individually exploring each of these profiles for target identification have been developed, but they have not been collectively used.

Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChem.

Background: Recent advances in high-throughput screening (HTS) techniques and readily available compound libraries generated using combinatorial chemistry or derived from natural products enable the testing of millions of compounds in a matter of days. Due to the amount of information produced by HTS assays, it is a very challenging task to mine the HTS data for potential interest in drug development research. Computational approaches for the analysis of HTS results face great challenges due to the large quantity of information and significant amounts of erroneous data produced.

Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity.

Protein sequence contains clues to its function. Functional prediction from sequence presents a challenge particularly for proteins that have low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from sequence-derived properties independent of sequence similarity, which showed promising potential for low- and non-homologous proteins.