AIMC Topic: Molecular Dynamics Simulation

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High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation.

Journal of chemical theory and computation
The dissociation or off rate, , of a drug molecule has been shown to be more relevant to efficacy than affinity for selected systems, motivating the development of predictive computational methodologies. These are largely based on enhanced-sampling m...

Modeling Enzyme Reaction and Mutation by Direct Machine Learning/Molecular Mechanics Simulations.

Journal of chemical theory and computation
Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations remains challenging in describing the electrostatic coupling between the QM and MM subsystems. In this work, we proposed a reweighting ME (mechanic e...

AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.

Journal of chemical theory and computation
Elucidating collective variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) al...

Small Molecules Targeting the Structural Dynamics of AR-V7 Partially Disordered Proteins Using Deep Ensemble Docking.

Journal of chemical theory and computation
The extensive conformational dynamics of partially disordered proteins hinders the efficiency of traditional in-silico structure-based drug discovery approaches due to the challenge of screening large chemical spaces of compounds, albeit with an exce...

A deep learning and molecular modeling approach to repurposing Cangrelor as a potential inhibitor of Nipah virus.

Scientific reports
Deforestation, urbanization, and climate change have significantly increased the risk of zoonotic diseases. Nipah virus (NiV) of Paramyxoviridae family and Henipavirus genus is transmitted by Pteropus bats. Climate-induced changes in bat migration pa...

Discovery, Biological Evaluation and Binding Mode Investigation of Novel Butyrylcholinesterase Inhibitors Through Hybrid Virtual Screening.

Molecules (Basel, Switzerland)
Butyrylcholinesterase (BChE), plays a critical role in alleviating the symptoms of Alzheimer's disease (AD) by regulating acetylcholine levels, emerging as an attractive target for AD treatment. This study employed a quantitative structure-activity r...

Machine learning assisted in Silico discovery and optimization of small molecule inhibitors targeting the Nipah virus glycoprotein.

Scientific reports
The Nipah virus (NiV), a lethal pathogen from the Paramyxoviridae family, presents a significant global health threat as a result of its high mortality rate and inter-human transmission. This investigation employed in silico methods that were assiste...

Repurposing FDA-approved drugs as NLRP3 inhibitors against inflammatory diseases: machine learning and molecular simulation approaches.

Journal of biomolecular structure & dynamics
Activation of NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) has been associated with multiple chronic pathologies, including diabetes, atherosclerosis, and rheumatoid arthritis. Moreover, histone deacetylases (HDACs), specifical...

Bioactivity predictions and virtual screening using machine learning predictive model.

Journal of biomolecular structure & dynamics
Recently, there has been significant attention on machine learning algorithms for predictive modeling. Prediction models for enzyme inhibitors are limited, and it is essential to account for chemical biases while developing them. The lack of repeatab...

Discovery of hematopoietic progenitor kinase 1 inhibitors using machine learning-based screening and free energy perturbation.

Journal of biomolecular structure & dynamics
Hematopoietic progenitor kinase 1 (HPK1) is a key negative regulator of T-cell receptor (TCR) signaling and a promising target for cancer immunotherapy. The development of novel HPK1 inhibitors is challenging yet promising. In this study, we used a c...