AIMC Topic: Molecular Docking Simulation

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Mycobacterium tuberculosis FAS-II pathway targeted integrative deep learning based identification of potential anti-tubercular agents.

Journal of computer-aided molecular design
Mycobacterium tuberculosis (Mtb) continues to be one of the major contributors to the global burden of infectious diseases. Many drugs used in the current treatment regime have fallen prey to the puzzling phenomenon of antimicrobial resistance. Despi...

Functionally Improved Urease Inhibitors (NBPT): Developed Approaches for Obtaining Environmentally Friendly Derivatives.

Journal of agricultural and food chemistry
Traditional agricultural urease inhibitors encounter a low inhibition efficiency and a short duration of action. Therefore, the typical traditional urease inhibitor -butylthiophosphoric acid triamide (NBPT) was modified by molecular docking and molec...

Learning Binding Affinities via Fine-Tuning of Protein and Ligand Language Models.

Journal of chemical information and modeling
Accurate in silico prediction of protein-ligand binding affinity is essential for efficient hit identification in large molecular libraries. Commonly used structure-based methods such as docking often fail to rank compounds effectively, and free ener...

Multidimensional strategy for discovering saltiness-enhancing peptides in shrimp heads integrating ultra-high pressure hydrolysis and machine learning.

Food chemistry
This study aims to develop a comprehensive strategy to investigate whether the integration of ultra-high pressure (UHP)-assisted enzymatic hydrolysis with machine learning and molecular docking can effectively identify salty peptides (SPs) from Litop...

Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.

RNA biology
Cancer diagnosis at an early stage is crucial for improving overall health outcomes. However, existing cancer diagnostic techniques are mostly invasive and tend to identify the disease only in its advanced stages. MicroRNAs (miRNAs), which are small ...

Coral-Derived Antimicrobial Peptides Identified In Silico from Acropora digitifera Transcriptomes: Potential Candidates Against Resistant Pathogens.

Marine biotechnology (New York, N.Y.)
Antimicrobial resistance is a serious threat to global public health and requires new therapeutic approaches. Antimicrobial peptides (AMP) are recognized as promising candidates to address antimicrobial resistance. AMP can disrupt cell membranes by i...

Multi-omics and machine learning identify FN1 and ALDH2 as diagnostic biomarkers and therapeutic targets in early and late diabetic kidney disease.

Renal failure
Diabetic kidney disease (DKD), the leading cause of end-stage kidney disease worldwide, demands deeper molecular characterization to improve clinical management. This study employed an integrated multi-omics approach to identify stage-specific biomar...

In silico Techniques for the Investigation of Bioactive Compounds in Quinoa (Chenopodium quinoa Willd.): Recent Advances in Molecular Modeling and Identification of Therapeutic Targets.

Plant foods for human nutrition (Dordrecht, Netherlands)
Quinoa (Chenopodium quinoa Willd.) is a valuable source of bioactive compounds with therapeutic potential, including peptides, saponins, and polyphenols. In recent years, in silico tools have emerged as key strategies for predicting, characterizing, ...

Identification of hub necroptosis-related targets and discovery of potential natural inhibitors in ulcerative colitis based on bioinformatics and computer-aided drug design.

Journal of computer-aided molecular design
Ulcerative colitis (UC) is a chronic inflammatory bowel disease with a complex pathogenesis and limited treatment options. Recently, necroptosis has been found to play a significant role in UC. This study aimed to investigate necroptosis-related mech...

Machine learning identifies exosome related gene signatures for early prediction of non-small cell lung cancer.

Scientific reports
Non-small cell lung cancer (NSCLC) remains a major health challenge worldwide, mainly due to the lack of effective early diagnostic biomarkers. Exosome-related genes have recently emerged as potential diagnostic markers due to their roles in tumor pr...