Computational Biology and Chemistry

Stercularin is a coumarin, isolated from the ethyl acetate fraction of stem bark and leaves of S. diversifolia. Pharmacologically it is active against cancer, diabetes, and inflammation etc. The molecule is further screened for in vitro ...

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• Natural products possess favorable drug like properties with lower risk of toxicity.
• Stercularin modified protein glycation in vitro, and presented itself as a drug candidate for metabolic complications.
• ADMET and DFT studies are ...

Extensive research has accumulated which suggests that phosphatidylinositol 3-kinase delta (PI3Kδ) is closely related to the occurrence and development of various human diseases, making PI3Kδ a highly promising drug target. However, PI3Kδ ...

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• Thirteen crystal human PI3Kδ-inhibitor complexes were collected to establish models.
• Virtual screening integrating multiple PI3Kδ has higher prediction accuracy.
• Virtual screening integrating pharmacophore and molecular docking has ...

The healthy as well as dysbiotic state of an ecosystem like human body is known to be influenced not only by the presence of the bacterial groups in it, but also with respect to the associations within themselves. Evidence reported in ...

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• Inter-bacterial associations constitute the building blocks of bacterial community.
• Automated and accurate methods are essential to extract such information from text.
• Transfer learning using information from similar domains can ...

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or ...

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• The study addresses a gap in existing approaches by introducing a novel mutually exclusive spectral biclustering (MESBC) algorithm, offering a distinct perspective in identifying disease or molecular subtypes with survival significance.

1,4-dihydropyridine derivatives (1,4-DHPs) are a class of drugs used to treat cardiovascular diseases, but these drugs can cause liver injury. To reveal the toxicity characteristics of these compounds, we used a series of assays, including cell ...

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• The model of 1,4-DHPs toxicity prediction is established by cytotoxicity test.
• The method is used to predict the toxicity of 1,4-DHPs.
• The model is built by the CoMSIA with good stability and predictive ability.

Unlike simple molecular screening, a combined hybrid computational methodology has been applied which includes quantum chemical methods, molecular docking, and molecular dynamics simulations to design some novel ketonic derivatives. The current ...

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• A combined hybrid computational methodology is used to design new compounds.
• The newly designed compounds showed good inhibition efficiency again ERα receptor.
• The FMOs and other geometry parameters were studied by quantum ...

Functional peptides are easy to absorb and have low side effects, which has attracted increasing interest from pharmaceutical scientists. However, due to the limitations in the laboratory funding and human resources, it is difficult to screen the ...

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• Weight focal loss function based on imbalanced dataset was used.
• Deep2Pep based on BiLSTM and BERT encoder was constructed.
• BiLSTM plays a primary role, while BERT encoder is auxiliary in Deep2Pep.
• Deep2Pep contributes to the ...

Cytokines are small protein molecules that exhibit potent immunoregulatory properties, which are known as the essential components of the tumor immune microenvironment (TIME). While some cytokines are known to be universally upregulated in TIME, ...

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• Cytokine expression patterns were revealed in TIME for precise cancer classification using scRNA-seq dataset.
• A most comprehensive and updated TIME scRNA-seq dataset was constructed for TIME studies.
• Identified the key cytokines ...

Channel-regulated peptides (CRPs) derived from animal venom hold great promise as potential drug candidates for numerous diseases associated with channel proteins. However, discovering and identifying CRPs using traditional bio-experimental ...

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• Sequence semantics and structural information were fused in graph-based models to improve the performance of CRPs prediction.
• The pre-trained language model extracted the semantic features of protein sequences as feature vectors of ...

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising ...

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• A new Boolean Network Model of Breast Cancer validated with cell-line data.
• A new method to optimize the number of potential therapeutic targets on Boolean network models.
• A set of potential therapeutic targets for breast cancer.

High throughput RNA sequencing brings new perspective to the elucidation of molecular mechanisms of diseases. Normalization is the first and most important step for RNA-Seq data, and it can differ based on the purpose of the analysis. Within-...

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• RNA-Seq normalization methods are benchmarked via PPI networks for the first time.
• Covariate adjustment leads to better representation of dysregulated AD mechanisms.
• Covariate-adjusted TMM performed the best for two AD datasets ...

The exploration encompassed the synthesis and characterization of two innovative complexes, namely FePHNS and CuPHNS, employing a diverse array of analytical techniques such as elemental analysis, infrared and ultraviolet-visible spectroscopy, ...

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• Novel Fe(III) and Cu(II) ions in molar ratios of 1:1 and 1:2 with PHNS ligand complexes have been synthesized.
• Their in vitro antibacterial, antifungal, and antioxidant efficacy of the PHNS ligand and its complexes has been ...

Tripterygium wilfordii Hook. f. (TW) shows anticancer activity, and no study has comprehensively investigated the effects of TW in treating cholangiocarcinoma (CHOL). This study was designed to identify the therapeutic role and the ...

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• WGCNA and Limma were first combined to obtain genes for cholangiocarcinoma (CHOL), with the diagnostic markers predicted.
• Network pharmacology was used to elucidate the therapeutic potential and mechanism of Tripterygium wilfodii (TW) ...

Cancer is a global public health problem characterized by deviations in the mechanisms that control cell proliferation, resulting in mutations and variations in the structure of DNA. The mechanisms of action of chemotherapeutic drugs are related ...

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• Acridine intercalates with DNA by non-covalent interactions on DFT studies.
• The Binding energy showed that acridine interacts with DNA effectively.
• The interactions with nitrogenous bases C-G and G-C were more effective than the ...

As a promising alternative to conventional antibiotic drugs in the biomedical field, functional peptide has been widely used in disease treatment owing to its low toxicity, high absorption rate, and biological activity. Recently, several machine ...

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• Multiangle initialization embedding enrich discriminant information.
• Deep spatiotemporal neural network optimization feature extraction.
• Adjusting for sample loss address the problem of sample class imbalance.

The conventional computational approaches to investigating a disease confront inherent constraints as they often need to improve in delving beyond protein functional associations and grasping their deeper contextual significance within the ...

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• Integrate co-expression with directional PPI network to study gene expression data.
• Identify source node in a directional network in context of disease biology.
• Explored possible causal linkage of regulators with the hallmark of ...

Transcription profiling is a key process that can reveal those biological mechanisms driving the response to various exposure conditions or gene perturbations. In this work, we investigate the prediction of differentially expressed genes (DEGs) ...

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• Integrated approach to feature engineering from diverse set of genomic data.
• Machine learning and artificial intelligence-based approach to gene expression prediction.
• Model trained with features engineered from factors driving ...

AI-enhanced bioinformatics and cheminformatics pivots on generating increasingly descriptive and generalized molecular representation. Accurate prediction of molecular properties needs a comprehensive description of molecular geometry. We design ...

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• Introduce a molecular representation model, combining a spatial information based three-level network and self-supervised learning.
• Comparisons with extensive baseline models reveal the superior accuracy in several tasks.
• ...

Metabolites represent the underlying information of biological systems. Revealing the links between metabolites and diseases can facilitate the development of targeted drugs. Traditional biological experiments can be used to validate the ...

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• We construct a metabolite-gene-disease network by integrating gene and diverse sources.
• Using GraphSAGE, we generate feature representations by sampling and aggregating features.
• MGDHGS employs self-attention mechanisms to allocate ...