AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics

• URL: http://arxiv.org/abs/2409.17852v3
• Date: Mon, 11 Nov 2024 16:41:16 GMT
• Title: AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
• Authors: Antonio Mirarchi, Raul P. Pelaez, Guillem Simeon, Gianni De Fabritiis,
• Abstract summary: All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Omni-force Representation-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing network architecture, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities.
• Score: 2.874893537471256
• License:
• Abstract: All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-force-field (AMARO), a new neural network potential (NNP) that combines an O(3)-equivariant message-passing neural network architecture, TensorNet, with a coarse-graining map that excludes hydrogen atoms. AMARO demonstrates the feasibility of training coarser NNP, without prior energy terms, to run stable protein dynamics with scalability and generalization capabilities.

Related papers

• Universal neural network potentials as descriptors: Towards scalable chemical property prediction using quantum and classical computers [0.0]
  We present a versatile approach that uses the intermediate information of a universal neural network potential as a general-purpose descriptor for chemical property prediction. We show that transfer learning with graph neural network potentials such as M3GNet and MACE achieves accuracy comparable to state-of-the-art methods for predicting the NMR chemical shifts.
  arXiv  Detail & Related papers  (2024-02-28T15:57:22Z)
• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning [57.670845619155195]
  We introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA) ASBA addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information. Our method offers a more comprehensive way to learn representations for molecular property prediction and has broad potential in drug and material discovery applications.
  arXiv  Detail & Related papers  (2023-05-22T00:56:00Z)
• Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations [51.68332623405432]
  Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Recently, machine learned force fields (MLFFs) emerged as an alternative means to execute MD simulations. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations.
  arXiv  Detail & Related papers  (2022-05-17T13:08:28Z)
• A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization [0.1469945565246172]
  In this work, we suggest an end-to-end model which directly predicts per-atom energy from the coordinates of particles. We tested our model against several challenges in molecular simulation problems, including periodic boundary condition (PBC), $n$-body interaction, and binary composition.
  arXiv  Detail & Related papers  (2021-10-27T12:18:25Z)
• Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials [0.0]
  We propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. The accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models.
  arXiv  Detail & Related papers  (2021-09-15T16:46:46Z)
• Neural Upscaling from Residue-level Protein Structure Networks to Atomistic Structure [2.087827281461409]
  "neural upscaling" is able to effectively recapitulate detailed structural information for intrinsically disordered proteins. Results suggest that scalable network-based models for protein structure and dynamics may be used in settings where atomistic detail is desired.
  arXiv  Detail & Related papers  (2021-08-25T23:43:57Z)
• Molecular spin qudits for quantum simulation of light-matter interactions [62.223544431366896]
  We show that molecular spin qudits provide an ideal platform to simulate the quantum dynamics of photon fields strongly interacting with matter. The basic unit of the proposed molecular quantum simulator can be realized by a simple dimer of a spin 1/2 and a spin $S$ transition metal ion, solely controlled by microwave pulses.
  arXiv  Detail & Related papers  (2021-03-17T15:03:12Z)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials [0.17590081165362778]
  NequIP is a SE(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. The method achieves state-of-the-art accuracy on a challenging set of diverse molecules and materials while exhibiting remarkable data efficiency.
  arXiv  Detail & Related papers  (2021-01-08T18:49:10Z)
• Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with an artificial neural-network correlator ansatz [62.997667081978825]
  We introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei. We compute the binding energies and point-nucleon densities of $Aleq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian.
  arXiv  Detail & Related papers  (2020-07-28T14:52:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.