AIMC Topic: Molecular Dynamics Simulation

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Integrated machine learning-based virtual screening and biological evaluation for identification of potential inhibitors against cathepsin K.

Molecular diversity Apr 25, 2024
Cathepsin K is a type of cysteine proteinase that is primarily expressed in osteoclasts and has a key role in the breakdown of bone matrix protein during bone resorption. Many studies suggest that the deficiency of cathepsin K is concomitant with a s...
Humans Machine Learning Drug Evaluation, Preclinical Cathepsin K Cysteine Proteinase Inhibitors Molecular Docking Simulation Molecular Dynamics Simulation
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Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints.

Scientific reports Apr 24, 2024
Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigatin...
Bayes Theorem Molecular Dynamics Simulation Binding Sites Molecular Docking Simulation Ligands Humans Protein Binding Machine Learning Receptors, G-Protein-Coupled
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HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES.

Scientific reports Apr 22, 2024
Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug des...
Viral Nonstructural Proteins Humans Hepatitis B Molecular Dynamics Simulation Hepatitis C Ligands Hepacivirus Neural Networks, Computer Molecular Docking Simulation Drug Design Antiviral Agents Hepatitis B virus
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Differentiating stable and unstable protein using convolution neural network and molecular dynamics simulations.

Computational biology and chemistry Apr 20, 2024
Protein stability is a critical aspect of molecular biology and biochemistry, hinges on an intricate balance of thermodynamic and structural factors. Determining protein stability is crucial for understanding and manipulating biological machineries, ...
Protein Stability Thermodynamics Proteins Molecular Dynamics Simulation Neural Networks, Computer Machine Learning
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Binding Mechanism of Inhibitors to BRD4 and BRD9 Decoded by Multiple Independent Molecular Dynamics Simulations and Deep Learning.

Molecules (Basel, Switzerland) Apr 19, 2024
Bromodomain 4 and 9 (BRD4 and BRD9) have been regarded as important targets of drug designs in regard to the treatment of multiple diseases. In our current study, molecular dynamics (MD) simulations, deep learning (DL) and binding free energy calcula...
Transcription Factors Molecular Dynamics Simulation Triazoles Protein Binding Nuclear Proteins Molecular Docking Simulation Cell Cycle Proteins Humans Azepines Deep Learning Binding Sites Bromodomain Containing Proteins Thermodynamics
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How exascale computing can shape drug design: A perspective from multiscale QM/MM molecular dynamics simulations and machine learning-aided enhanced sampling algorithms.

Current opinion in structural biology Apr 16, 2024
Molecular simulations are an essential asset in the first steps of drug design campaigns. However, the requirement of high-throughput limits applications mainly to qualitative approaches with low computational cost, but also low accuracy. Unlocking t...
Quantum Theory Algorithms Drug Design Molecular Dynamics Simulation Machine Learning
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Biomolecular Adsorption on Nanomaterials: Combining Molecular Simulations with Machine Learning.

Journal of chemical information and modeling Apr 16, 2024
Adsorption free energies of 32 small biomolecules (amino acids side chains, fragments of lipids, and sugar molecules) on 33 different nanomaterials, computed by the molecular dynamics - metadynamics methodology, have been analyzed using statistical m...
Nanostructures Thermodynamics Amino Acids Molecular Dynamics Simulation Neural Networks, Computer Algorithms Adsorption Machine Learning
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Enhancing Protein Solubility via Glycosylation: From Chemical Synthesis to Machine Learning Predictions.

Biomacromolecules Apr 10, 2024
Glycosylation is a valuable tool for modulating protein solubility; however, the lack of reliable research strategies has impeded efficient progress in understanding and applying this modification. This study aimed to bridge this gap by investigating...
Machine Learning Molecular Dynamics Simulation Solubility Glycosylation Glycoproteins
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Modeling Zinc Complexes Using Neural Networks.

Journal of chemical information and modeling Apr 8, 2024
Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build...
Neural Networks, Computer Coordination Complexes Thermodynamics Molecular Dynamics Simulation Quantum Theory Zinc Molecular Conformation Models, Molecular
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One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates.

The journal of physical chemistry. B Apr 3, 2024
Identifying optimal reaction coordinates for complex conformational changes and protein folding remains an outstanding challenge. This study combines collective variable (CV) discovery based on chemical intuition and machine learning with enhanced sa...
Protein Folding Molecular Dynamics Simulation Thermodynamics Peptides Discriminant Analysis Machine Learning
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