AIMC Topic: Molecular Dynamics Simulation

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Generative adversarial network (GAN) model-based design of potent SARS-CoV-2 M inhibitors using the electron density of ligands and 3D binding pockets: insights from molecular docking, dynamics simulation, and MM-GBSA analysis.

Molecular diversity Nov 30, 2024
Deep learning-based generative adversarial network (GAN) frameworks have recently been developed to expedite the drug discovery process. These models generate novel molecules from scratch and validate them through molecular docking simulation to iden...
Humans COVID-19 SARS-CoV-2 Generative Adversarial Networks Molecular Dynamics Simulation Ligands COVID-19 Drug Treatment Binding Sites Protease Inhibitors Coronavirus 3C Proteases Deep Learning Antiviral Agents Protein Binding Molecular Docking Simulation Drug Design
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Rational design of potent phosphopeptide binders to endocrine Snk PBD domain by integrating machine learning optimization, molecular dynamics simulation, binding energetics rescoring, and in vitro affinity assay.

European biophysics journal : EBJ Nov 29, 2024
Human Snk is an evolutionarily conserved serine/threonine kinase essential for the maintenance of endocrine stability. The protein consists of a N-terminal catalytic domain and a C-terminal polo-box domain (PBD) that determines subcellular localizati...
Protein Domains Drug Design Amino Acid Sequence Molecular Dynamics Simulation Protein Binding Humans Thermodynamics Protein Serine-Threonine Kinases Machine Learning Phosphopeptides
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Machine Learning Models for Predicting Monoclonal Antibody Biophysical Properties from Molecular Dynamics Simulations and Deep Learning-Based Surface Descriptors.

Molecular pharmaceutics Nov 28, 2024
Monoclonal antibodies (mAbs) have found extensive applications and development in treating various diseases. From the pharmaceutical industry's perspective, the journey from the design and development of mAbs to clinical testing and large-scale produ...
Deep Learning Antibodies, Monoclonal Molecular Dynamics Simulation Humans Solubility Machine Learning Viscosity
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Triple-Action Therapy: Combining Machine Learning, Docking, and Dynamics to Combat BRCA1-Mutated Breast Cancer.

Molecular biotechnology Nov 26, 2024
Breast cancer dominates women's mortality, and among other factors, mutations in the BRCA1 gene are significant risk factors. Several approaches are followed to treat the BRCA1 affected cancer patients. However, specific BRCA1 inhibitors are not avai...
Machine Learning Female Breast Neoplasms Humans Molecular Dynamics Simulation Ligands Molecular Docking Simulation BRCA1 Protein Antineoplastic Agents Mutation
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Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck.

Journal of chemical theory and computation Nov 26, 2024
The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional colle...
Deep Learning Humans Oligopeptides Molecular Dynamics Simulation Dipeptides
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ConfRank: Improving GFN-FF Conformer Ranking with Pairwise Training.

Journal of chemical information and modeling Nov 20, 2024
Conformer ranking is a crucial task for drug discovery, with methods for generating conformers often based on molecular (meta)dynamics or sophisticated sampling techniques. These methods are constrained by the underlying force computation regarding r...
Drug Discovery Machine Learning Molecular Dynamics Simulation Molecular Conformation
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Deciphering Cas9 specificity: Role of domain dynamics and RNA:DNA hybrid interactions revealed through machine learning and accelerated molecular simulations.

International journal of biological macromolecules Nov 19, 2024
CRISPR/Cas9 technology is widely used for gene editing, but off-targeting still remains a major concern in therapeutic applications. Although Cas9 variants with better mismatch discrimination have been developed, they have significantly lower rates o...
Protein Domains Molecular Dynamics Simulation RNA Substrate Specificity CRISPR-Associated Protein 9 Francisella DNA Machine Learning CRISPR-Cas Systems Bacterial Proteins
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Emerging Frontiers in Conformational Exploration of Disordered Proteins: Integrating Autoencoder and Molecular Simulations.

ACS chemical neuroscience Nov 18, 2024
Intrinsically disordered proteins (IDPs) are closely associated with a number of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease. Due to the highly dynamic nature of IDPs, their structural determination and conformatio...
Machine Learning Molecular Dynamics Simulation Intrinsically Disordered Proteins Protein Conformation Humans
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Estimating AChE inhibitors from MCE database by machine learning and atomistic calculations.

Journal of molecular graphics & modelling Nov 14, 2024
Acetylcholinesterase (AChE) is one of the most successful targets for the treatment of Alzheimer's disease (AD). Inhibition of AChE can result in preventing AD. In this context, the machine-learning (ML) model, molecular docking, and molecular dynami...
Databases, Chemical Acetylcholinesterase Binding Sites Cholinesterase Inhibitors Humans Protein Binding Machine Learning Molecular Dynamics Simulation Molecular Docking Simulation Ligands
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Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

Journal of chemical information and modeling Nov 11, 2024
MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in t...
Molecular Dynamics Simulation Ligands Peptides, Cyclic Machine Learning MicroRNAs Nucleic Acid Conformation RNA Precursors Ribonuclease III Humans
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