AIMC Topic: Molecular Docking Simulation

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Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.

Molecular informatics
There is a growing body of evidence showing that machine learning regression results in more accurate structure-based prediction of protein-ligand binding affinity. Docking methods that aim at optimizing the affinity of ligands for a target rely on h...

Interplay of PRMTs and Identification of Biomarkers Through Machine Learning Algorithms in Pan-Cancer, Highlighting PRMT3 as a Biomarker in Pancreatic Cancer.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Protein arginine methylation was a common post-translational modification, playing a key role in many biological processes and disease. But the regulatory mechanisms of protein arginine methyltransferases (PRMTs) in cancer were not well understood. T...

BOLA3 as a key protein for the treatment of diabetic skeletal muscle atrophy.

International immunopharmacology
OBJECTIVE: Skeletal muscle is crucial for glucose metabolism, but diabetes impairs this function, leading to muscle atrophy. Although the mutation of BOLA Family Member 3 (BOLA3) resulted in disease Multiple Mitochondrial Dysfunctions Syndrome, the r...

Assessing the toxicological impact of DEGDB plasticizer exposure on glioblastoma multiforme via network toxicology, machine learning and in vitro methods.

Environmental pollution (Barking, Essex : 1987)
Diethylene glycol dibenzoate (DEGDB) is a novel environmentally friendly plasticizer. However, toxicological studies on DEGDB remain limited, and its potential harmful effects on the malignant progression of glioblastoma are still unclear. Further sy...

DPP-IV inhibitory peptides from highland barley via machine learning and multi-scale validation.

Food chemistry
Highland barley has shown potential in regulating blood glucose and may serve as a natural source of dipeptidyl peptidase-IV (DPP-IV) inhibitors. In this study, machine learning (Gradient Boosting Decision Trees) and virtual screening were employed t...

Artificial Intelligence, Molecular Dynamics, and Beyond: Computational Insights In Cosmetics Research and Formulation Design.

ChemPlusChem
The vast field of cosmetics is mainly explored through experimental methods, while computational tools find broader application in structuralbiology. The world of formulations remains relatively untouched or nondisclosed due to commercial interests. ...

Machine learning-driven discovery of antimicrobial peptides targeting the GAPDH-TPI protein-protein interaction in Schistosoma mansoni for novel antischistosomal therapeutics.

Computational biology and chemistry
Schistosomiasis, caused by Schistosoma mansoni, remains a significant public health burden, particularly in endemic regions with limited access to effective treatment. The emergence of resistance to praziquantel necessitates the urgent discovery of n...

Macrophage histone lactylation in atherosclerosis progression: mechanisms, predictive models, and therapeutic potential of Ruan Jian Qing Mai formula.

Life sciences
AIMS: This study investigates the role of macrophage histone lactylation-a protein modification-in atherosclerosis progression, particularly in peripheral artery disease (PAD), and evaluates the therapeutic potential of the herbal formula Ruan Jian Q...

Virtual screening of umami peptides during sufu ripening based on machine learning and molecular docking to umami receptor T1R1/T1R3.

Food chemistry
Umami peptides might significantly contribute to the taste of sufu. However, the inefficiencies of traditional identification methods had great limitations. This study explored a new approach for umami peptides characterization in sufu. Combining pep...