AIMC Topic: Molecular Dynamics Simulation

Clear Filters Showing 101 to 110 of 710 articles

Consensus structure prediction of A. thaliana's MCTP4 structure using prediction tools and coarse grained simulations of transmembrane domain dynamics.

PloS one
Multiple C2 Domains and Transmembrane region Proteins (MCTPs) in plants have been identified as important functional and structural components of plasmodesmata cytoplasmic bridges, which are vital for cell-cell communication. MCTPs are endoplasmic re...

NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks.

Journal of chemical information and modeling
Unsupervised machine learning has recently gained much attention in the field of molecular dynamics (MD). Particularly, dimensionality reduction techniques have been regularly employed to analyze large volumes of high-dimensional MD data to gain insi...

Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identification.

Journal of chemical theory and computation
In this study, we propose a Kernel-PCA model designed to capture structure-function relationships in a protein. This model also enables the ranking of reaction coordinates according to their impact on protein properties. By leveraging machine learnin...

Harnessing AlphaFold to reveal hERG channel conformational state secrets.

eLife
To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been the resolution of discrete conformational states of tran...

Tuning antibody stability and function by rational designs of framework mutations.

mAbs
Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens. These approaches, however, often focus on the antibody complementarity-determining region (CDR) whilst ignoring the i...

Machine-Learning-Driven Discovery of -Phenylbenzenesulfonamides as a Novel Chemotype for Lactate Dehydrogenase A Inhibition with Anti-Pancreatic Cancer Activity.

Journal of medicinal chemistry
Lactate dehydrogenase A (LDHA) is a promising target for cancer therapy due to its crucial role in aerobic glycolysis. Despite extensive efforts, the structural diversity of LDHA inhibitors remains limited. Here, we utilized machine learning techniqu...

Integrating machine learning with in silico studies and Quantum Chemistry: Exploring novel compounds through multiscale screening targeting the CDK2 enzyme.

Computers in biology and medicine
Cyclin-dependent kinase 2 (CDK2) modulates the progression of the cell cycle, and its dysregulation results in unchecked cellular proliferation, establishing it as a pivotal target in oncological therapies. We implemented a comprehensive screening pi...

AI based natural inhibitor targeting RPS20 for colorectal cancer treatment using integrated computational approaches.

Scientific reports
The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying novel anticancer targets and developing new drugs. Computational approaches can significantly quicken r...

Permanent magnetic droplet-derived microrobots.

Science advances
Microrobots hold substantial potential for precision medicine. However, challenges remain in balancing multifunctional cargo loading with efficient locomotion and in predicting behavior in complex biological environments. Here, we present permanent m...

Predicting the Effects of Charge Mutations on the Second Osmotic Virial Coefficient for Therapeutic Antibodies via Coarse-Grained Molecular Simulations and Deep Learning Methods.

Molecular pharmaceutics
The impact of various charge mutations on the second osmotic virial coefficient was examined for three model therapeutic monoclonal antibodies (MAbs) at representative formulation pH values by using coarse-grained (CG) molecular modeling. The wild-ty...