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Molecular Dynamics Simulation

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Linking of single-molecule experiments with molecular dynamics simulations by machine learning.

eLife
Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide data, such as donor-acceptor distances, whereas the latter give atomistic information, ...

TopP-S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility.

Journal of computational chemistry
Aqueous solubility and partition coefficient are important physical properties of small molecules. Accurate theoretical prediction of aqueous solubility and partition coefficient plays an important role in drug design and discovery. The prediction ac...

Recognition of protein allosteric states and residues: Machine learning approaches.

Journal of computational chemistry
Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with...

Machine Learning of Partial Charges Derived from High-Quality Quantum-Mechanical Calculations.

Journal of chemical information and modeling
Parametrization of small organic molecules for classical molecular dynamics simulations is not trivial. The vastness of the chemical space makes approaches using building blocks challenging. The most common approach is therefore an individual paramet...

Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning.

Journal of biomolecular structure & dynamics
Thrombin is a key component for chemotherapeutic and antithrombotic therapy development. As the physiologic and pathologic roles of the light chain still remain vague, here, we continue previous efforts to understand the impacts of the disease-associ...

Simulations meet machine learning in structural biology.

Current opinion in structural biology
Classical molecular dynamics (MD) simulations will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy. Notwithstanding this, MD will still be in the regime of lo...

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening.

PLoS computational biology
This work introduces a number of algebraic topology approaches, including multi-component persistent homology, multi-level persistent homology, and electrostatic persistence for the representation, characterization, and description of small molecules...

VAMPnets for deep learning of molecular kinetics.

Nature communications
There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transfor...

Protein structure modeling and refinement by global optimization in CASP12.

Proteins
For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequ...