NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Galvelis, Raimondas; Varela-Rial, Alejandro; Doerr, Stefan H.; Fino, Roberto; Eastman, Peter; Markland, Thomas E.; Chodera, John D.; Fabritiis, Gianni De

doi:10.48550/arxiv.2201.08110

Cited by 2 publications

(2 citation statements)

References 45 publications

(67 reference statements)

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“… 127 Despite these successes, there are a number of long-range MLIP models, such as those using message passing or Qeq, that are challenging to efficiently plug into existing software, and further development effort is needed. There is also appreciable optimization space available at the hardware and algorithm levels, for example, see the works of Guo et al 121 and Galvelis et al 128 Constructing MLIP interfaces with simulation packages that fully leverage the power of accelerated computing architectures is a nontrivial task. Unified effort between MLIP development, model implementation, and performance optimization is an ongoing need.…”

Section: Outlook and Concluding Remarksmentioning

confidence: 99%

Machine Learning Interatomic Potentials and Long-Range Physics

Anstine

Isayev

2023

J. Phys. Chem. A

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Advances in machine learned interatomic potentials (MLIPs), such as those using neural networks, have resulted in short-range models that can infer interaction energies with near ab initio accuracy and orders of magnitude reduced computational cost. For many atom systems, including macromolecules, biomolecules, and condensed matter, model accuracy can become reliant on the description of short- and long-range physical interactions. The latter terms can be difficult to incorporate into an MLIP framework. Recent research has produced numerous models with considerations for nonlocal electrostatic and dispersion interactions, leading to a large range of applications that can be addressed using MLIPs. In light of this, we present a Perspective focused on key methodologies and models being used where the presence of nonlocal physics and chemistry are crucial for describing system properties. The strategies covered include MLIPs augmented with dispersion corrections, electrostatics calculated with charges predicted from atomic environment descriptors, the use of self-consistency and message passing iterations to propagated nonlocal system information, and charges obtained via equilibration schemes. We aim to provide a pointed discussion to support the development of machine learning-based interatomic potentials for systems where contributions from only nearsighted terms are deficient.

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Section: Outlook and Concluding Remarksmentioning

confidence: 99%

Machine Learning Interatomic Potentials and Long-Range Physics

Anstine

Isayev

2023

J. Phys. Chem. A

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“…On a related note, geometric deep learning is also increasingly being found useful in neighboring fields, such as the training of machine-learning potentials. [10,216,217] Ab initio calculations supported by more flexible and accurate potentials could prove immensely useful in SBDD scenarios where few or no data are available, such as in free-energy calculations on lead optimization stages. Diffusion models [218] -a family of generative models inspired by non-equilibrium thermodynamics -are also gaining increasing popularity in deep learning thanks to their generative capabilities, and have found pioneering applications in the molecular sciences.…”

Section: Gaps Opportunities and Outlookmentioning

confidence: 99%

Structure‐Based Drug Discovery with Deep Learning**

et al. 2023

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Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and chemical biology, for example, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules de novo. While most of the deep learning efforts in drug discovery have focused on ligand‐based approaches, structure‐based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding‐mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep‐learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a renaissance in structure‐based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure‐based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.

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NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics

Cited by 2 publications

References 45 publications

Machine Learning Interatomic Potentials and Long-Range Physics

Machine Learning Interatomic Potentials and Long-Range Physics

Structure‐Based Drug Discovery with Deep Learning**

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