Molecular Docking Simulation

Integrative machine learning approach for identification of new molecular scaffold and prediction of inhibition responses in cancer cells using multi-omics data.

Briefings in functional genomics 40251828

MDM2 (Mouse Double Minute 2), a fundamental governor of the p53 tumor suppressor pathway, has garnered significant attention as a favorable target for cancer therapy. Recent years have witnessed the development and synthesis of potent MDM2 inhibitors...

Antineoplastic Agents Cell Line, Tumor Gene Expression Regulation, Neoplastic Genomics Humans Machine Learning Molecular Docking Simulation Multiomics Neoplasms Proto-Oncogene Proteins c-mdm2

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

Frontiers in immunology 40207230

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...

Animals Computational Biology Gene Expression Profiling Gene Regulatory Networks Humans Ischemic Stroke Machine Learning Mice Molecular Docking Simulation Multiomics Nucleotides Transcriptome

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

International journal of molecular sciences 40243651

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 ...

Antiviral Agents Catalytic Domain Coronavirus 3C Proteases Drug Discovery Humans Machine Learning Middle East Respiratory Syndrome Coronavirus Molecular Docking Simulation Molecular Dynamics Simulation Protease Inhibitors

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Study on the mechanism of action of the active ingredient of Calculus Bovis in the treatment of sepsis by integrating single-cell sequencing and machine learning.

Medicine 40258762

BACKGROUND: Sepsis, a complex inflammatory condition with high mortality rates, lacks effective treatments. This study explores the therapeutic mechanisms of Calculus Bovis in sepsis using network pharmacology and RNA sequencing.

China Drugs, Chinese Herbal Humans Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Network Pharmacology Protein Interaction Maps Sepsis Sequence Analysis, RNA Single-Cell Analysis

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Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.

Scientific reports 40253524

As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of ...

Biomarkers, Tumor Breast Neoplasms Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Lactation Machine Learning Molecular Docking Simulation Prognosis Transcriptome Tumor Microenvironment

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Bioactive structures for inhibitors of polymerase enzyme by artificial intelligence.

Future medicinal chemistry 40247646

AIMS: Present new bioactive compounds, created by De novo Drug Design and artificial intelligence (AI), as possible inhibitors of polymerase.

Antifungal Agents Artificial Intelligence Candida Drug Design Enzyme Inhibitors Humans Molecular Docking Simulation Molecular Dynamics Simulation Molecular Structure

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Identification of key therapeutic targets in nicotine-induced intracranial aneurysm through integrated bioinformatics and machine learning approaches.

BMC pharmacology & toxicology 40247428

BACKGROUND: Intracranial aneurysm (IA) is a critical cerebrovascular condition, and nicotine exposure is a known risk factor. This study delves into the toxicological mechanisms of nicotine in IA, aiming to identify key biomarkers and therapeutic tar...

Animals Computational Biology Gene Regulatory Networks Humans Intracranial Aneurysm Machine Learning Molecular Docking Simulation Nicotine

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Computational screening of natural products as tryptophan 2,3-dioxygenase inhibitors: Insights from CNN-based QSAR, molecular docking, ADMET, and molecular dynamics simulations.

Computers in biology and medicine 40233673

Parkinson's disease (PD) is characterised by a complex array of motor, psychiatric, and gastrointestinal symptoms, many of which are linked to disruptions in neuroactive metabolites. Dysregulated activity of tryptophan 2,3-dioxygenase (TDO), a key en...

Biological Products Enzyme Inhibitors Humans Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Neural Networks, Computer Parkinson Disease Quantitative Structure-Activity Relationship Tryptophan Oxygenase

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Integrating machine learning driven virtual screening and molecular dynamics simulations to identify potential inhibitors targeting PARP1 against prostate cancer.

Scientific reports 40229418

Prostate cancer (PC) is one of the most common types of malignancies in men, with a noteworthy increase in newly diagnosed cases in recent years. PARP1 is a ubiquitous nuclear enzyme involved in DNA repair, nuclear transport, ribosome synthesis, and ...

Humans Machine Learning Male Molecular Docking Simulation Molecular Dynamics Simulation Phytochemicals Poly (ADP-Ribose) Polymerase-1 Poly(ADP-ribose) Polymerase Inhibitors Prostatic Neoplasms

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

ACS chemical neuroscience 40181215

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...

Deep Learning Drug Discovery Drug Evaluation, Preclinical HT29 Cells Humans Molecular Docking Simulation Necroptosis Protein Kinase Inhibitors Receptor-Interacting Protein Serine-Threonine Kinases

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Integrative machine learning approach for identification of new molecular scaffold and prediction of inhibition responses in cancer cells using multi-omics data.

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

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

Study on the mechanism of action of the active ingredient of Calculus Bovis in the treatment of sepsis by integrating single-cell sequencing and machine learning.

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning.

Bioactive structures for inhibitors of polymerase enzyme by artificial intelligence.

Identification of key therapeutic targets in nicotine-induced intracranial aneurysm through integrated bioinformatics and machine learning approaches.

Computational screening of natural products as tryptophan 2,3-dioxygenase inhibitors: Insights from CNN-based QSAR, molecular docking, ADMET, and molecular dynamics simulations.

Integrating machine learning driven virtual screening and molecular dynamics simulations to identify potential inhibitors targeting PARP1 against prostate cancer.

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

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