AIMC Topic: Molecular Docking Simulation

Clear Filters Showing 181 to 190 of 854 articles

From prediction to design: Revealing the mechanisms of umami peptides using interpretable deep learning, quantum chemical simulations, and module substitution.

Food chemistry
This study screened and designed umami peptides using deep learning model and module substitution strategies. The predictive model, which integrates pre-training, enhanced feature, and contrastive learning module, achieved an accuracy of 0.94, outper...

Virtual reality in drug design: Benefits, applications and industrial perspectives.

Current opinion in structural biology
Virtual reality (VR) is a tool which has transformative potential in domains which involve the visualization of complex 3D data such as structure-based drug design (SBDD), where it offers new ways to visualize and manipulate complex molecular structu...

Accelerating drug discovery targeting dihydroorotate dehydrogenase using machine learning and generative AI approaches.

Computational biology and chemistry
Dihydroorotate dehydrogenase (DHODH) is a key enzyme in pyrimidine biosynthesis, making it an attractive drug target for cancer, autoimmune diseases, and infections. Traditional DHODH inhibitor discovery is slow and costly. Our study integrated machi...

Discovery and Characterization of Novel Receptor-Interacting Protein Kinase 1 Inhibitors Using Deep Learning and Virtual Screening.

ACS chemical neuroscience
Receptor-interacting protein kinase 1 (RIPK1) serves as a critical mediator of cell necroptosis and represents a promising therapeutic target for various human neurodegenerative diseases and inflammatory diseases. Nonetheless, the RIPK1 inhibitors cu...

Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?

Journal of chemical information and modeling
Allosteric compounds offer an alternative mode of inhibition to orthosteric compounds with opportunities for selectivity and noncompetition. Structure-based drug design (SBDD) of allosteric compounds introduces complications compared to their orthost...

Integrating single-cell RNA sequencing, WGCNA, and machine learning to identify key biomarkers in hepatocellular carcinoma.

Scientific reports
The microarray and single-cell RNA-sequencing (scRNA-seq) datasets of hepatocellular carcinoma (HCC) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (W...

Molecular docking-QSAR-Kronecker-regularized least squares-based multiple machine learning for assessment and prediction of PFAS-protein binding interactions.

Journal of hazardous materials
Ubiquitous per- and poly-fluoroalkyl substances (PFAS) threaten human's health and attract worldwide attention. PFAS-mediated toxicity involves adverse effects of PFAS on proteins, and assessment of PFAS-protein binding interactions helps to explain ...

Machine Learning-Guided Screening and Molecular Docking for Proposing Naturally Derived Drug Candidates Against MERS-CoV 3CL Protease.

International journal of molecular sciences
In this study, we utilized machine learning techniques to identify potential inhibitors of the MERS-CoV 3CL protease. Among the models evaluated, the Random Forest (RF) algorithm exhibited the highest predictive performance, achieving an accuracy of ...

Integrating machine learning and multi-omics analysis to reveal nucleotide metabolism-related immune genes and their functional validation in ischemic stroke.

Frontiers in immunology
BACKGROUND: Ischemic stroke (IS) is a major global cause of death and disability, linked to nucleotide metabolism imbalances. This study aimed to identify nucleotide metabolism-related genes associated with IS and explore their roles in disease mecha...