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Molecular Dynamics Simulation

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Discovery of novel PARP-1 inhibitors using tandem studies: integrated docking, e-pharmacophore, deep learning based de novo and molecular dynamics simulation approach.

Journal of biomolecular structure & dynamics
Cancer accounts for the majority of deaths worldwide, and the increasing incidence of breast cancer is a matter of grave concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as an attractive target for the treatment of breast cancer as it has...

Enhanced sampling in explicit solvent by deep learning module in FSATOOL.

Journal of computational chemistry
FSATOOL is an integrated molecular simulation and data analysis program. Its old molecular dynamics engine only supports simulations in vacuum or implicit solvent. In this work, we implement the well-known smooth particle mesh Ewald method for simula...

PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces.

Nature communications
Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their  binding  interfaces remains a challenge. In this study, we present a ...

Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model.

Journal of chemical information and modeling
Allosteric modulators are important regulation elements that bind the allosteric site beyond the active site, leading to the changes in dynamic and/or thermodynamic properties of the protein. Allosteric modulators have been a considerable interest as...

A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database.

Journal of biomolecular structure & dynamics
Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in predicting pharmacological inhibitors of EGLN1 using a deep learning-based architecture, one of its ...

Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder.

International journal of molecular sciences
Intrinsically disordered proteins (IDPs) account for more than 50% of the human proteome and are closely associated with tumors, cardiovascular diseases, and neurodegeneration, which have no fixed three-dimensional structure under physiological condi...

Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study.

Molecular diversity
Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major ...

Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M) using molecular docking and deep learning methods.

Computers in biology and medicine
Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the acad...

Uncertainty-driven dynamics for active learning of interatomic potentials.

Nature computational science
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, th...

Protein Design Using Physics Informed Neural Networks.

Biomolecules
The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been su...