Deep Learning for Variational Multi-Scale Molecular Modeling

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method defines a variational and adaptive training objective which allows end-to-end training of parametric molecular models using deep neural networks. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems.

Deep Learning for Multi-Scale Molecular Modeling

10.26434/chemrxiv.9640814.v3 ◽

2020 ◽

Author(s):

Jun Zhang ◽

Yaokun Lei ◽

Yi Isaac Yang ◽

Yi Qin Gao

Keyword(s):

Coarse Grained ◽

Atomistic Simulations ◽

Molecular Models ◽

Enhanced Sampling ◽

Physical Systems ◽

Molecular Systems ◽

Fine Grained ◽

Multi Scale ◽

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method defines a variational and adaptive training objective which allows end-to-end training of parametric molecular models using deep neural networks. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems.

Reinforcement Learning for Multi-Scale Molecular Modeling

10.26434/chemrxiv.9640814.v2 ◽

2020 ◽

Author(s):

Jun Zhang ◽

Yaokun Lei ◽

Yi Isaac Yang ◽

Yi Qin Gao

Keyword(s):

Reinforcement Learning ◽

Parametric Models ◽

Coarse Grained ◽

Atomistic Simulations ◽

Enhanced Sampling ◽

Molecular Systems ◽

Fine Grained ◽

Multi Scale ◽

Molecular simulations are widely applied in the study of chemical and bio-physical systems of interest. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to take advantage of both strategies. In this approach, simulations on different scales are executed simultaneously and benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations; In turn, FG simulations can be boosted by the guidance of CG models. Our method grounds on unsupervised and reinforcement learning, defined by a variational and adaptive training objective, and allows end-to-end training of parametric models. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems.

Reinforcement Learning for Multi-Scale Molecular Modeling

10.26434/chemrxiv.9640814.v1 ◽

2019 ◽

Author(s):

Jun Zhang ◽

Yaokun Lei ◽

Yi Isaac Yang

Keyword(s):

Reinforcement Learning ◽

Parametric Models ◽

Coarse Grained ◽

Enhanced Sampling ◽

Molecular Systems ◽

Fine Grained ◽

Multi Scale ◽

Atom Level ◽

Molecular simulations are widely applied in the study of chemical and bio-physical systems of interest. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atom level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to ally these two worlds. In our approach, simulations on different scales are executed simultaneously and benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations; In turn, FG simulations can be boosted by the guidance of CG models. Our method grounds on unsupervised and reinforcement learning, defined by a variational and adaptive training objective, and allows end-to-end and online training of parametric models. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems.

Efficient parallelization of perturbative Monte Carlo QM/MM simulations in heterogeneous platforms

The International Journal of High Performance Computing Applications ◽

10.1177/1094342016649420 ◽

2016 ◽

Vol 31 (6) ◽

pp. 499-516 ◽

Cited By ~ 1

Author(s):

Sebastião Miranda ◽

Jonas Feldt ◽

Frederico Pratas ◽

Ricardo A Mata ◽

Nuno Roma ◽

...

Keyword(s):

Monte Carlo ◽

Heterogeneous Systems ◽

Coarse Grained ◽

Molecular Systems ◽

Fine Grained ◽

Central Processing ◽

Speed Up ◽

Graphical Processing ◽

Computational Bottleneck ◽

The Cost

A novel perturbative Monte Carlo mixed quantum mechanics (QM)/molecular mechanics (MM) approach has been recently developed to simulate molecular systems in complex environments. However, the required accuracy to efficiently simulate such complex molecular systems is usually granted at the cost of long executing times. To alleviate this problem, a new parallelization strategy of multi-level Monte Carlo molecular simulations is herein proposed for heterogeneous systems. It simultaneously exploits fine-grained (at the data level), coarse-grained (at the Markov chain level) and task-grained (pure QM, pure MM and QM/MM procedures) parallelism to ensure an efficient execution in heterogeneous systems composed of central processing units and multiple and possibly different graphical processing units. This is achieved by making use of the OpenCL library, together with appropriate dynamic load balancing schemes. From the conducted evaluation with real benchmarking data, a speed-up of 56x in the computational bottleneck part was observed, which results in a global speed-up of 38x for the whole simulation, reducing the time of a typical simulation from 80 hours to only 2 hours.

Lightweight Multi-Scale Asymmetric Attention Network for Image Super-Resolution

Micromachines ◽

10.3390/mi13010054 ◽

2021 ◽

Vol 13 (1) ◽

pp. 54

Author(s):

Min Zhang ◽

Huibin Wang ◽

Zhen Zhang ◽

Zhe Chen ◽

Jie Shen

Keyword(s):

Super Resolution ◽

Coarse Grained ◽

Attention Network ◽

Fine Grained ◽

Multi Scale ◽

Multiple Paths ◽

Comparable Performance ◽

Image Super Resolution ◽

Pixel Correlation ◽

Single Image Super Resolution

Recently, with the development of convolutional neural networks, single-image super-resolution (SISR) has achieved better performance. However, the practical application of image super-resolution is limited by a large number of parameters and calculations. In this work, we present a lightweight multi-scale asymmetric attention network (MAAN), which consists of a coarse-grained feature block (CFB), fine-grained feature blocks (FFBs), and a reconstruction block (RB). MAAN adopts multiple paths to facilitate information flow and accomplish a better balance of performance and parameters. Specifically, the FFB applies a multi-scale attention residual block (MARB) to capture richer features by exploiting the pixel-to-pixel correlation feature. The asymmetric multi-weights attention blocks (AMABs) in MARB are designed to obtain the attention maps for improving SISR efficiency and readiness. Extensive experimental results show that our method has comparable performance with fewer parameters than the current advanced lightweight SISR.

Multigraining: An algorithm for simultaneous fine-grained and coarse-grained simulation of molecular systems

The Journal of Chemical Physics ◽

10.1063/1.2187488 ◽

2006 ◽

Vol 124 (15) ◽

pp. 154106 ◽

Cited By ~ 80

Author(s):

Markus Christen ◽

Wilfred F. van Gunsteren

Keyword(s):

Coarse Grained ◽

Molecular Systems ◽

Fine Grained

Impact of natural disasters on consumer behavior: Case of the 2017 El Niño phenomenon in Peru

PLoS ONE ◽

10.1371/journal.pone.0244409 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0244409

Author(s):

Hugo Alatrista-Salas ◽

Vincent Gauthier ◽

Miguel Nunez-del-Prado ◽

Monique Becker

Keyword(s):

El Niño ◽

El Nino ◽

Coarse Grained ◽

Equatorial Pacific Ocean ◽

Economic Activities ◽

Fine Grained ◽

Multi Scale ◽

Eastern Equatorial Pacific ◽

Eastern Equatorial Pacific Ocean ◽

Purchasing Patterns

El Niño is an extreme weather event featuring unusual warming of surface waters in the eastern equatorial Pacific Ocean. This phenomenon is characterized by heavy rains and floods that negatively affect the economic activities of the impacted areas. Understanding how this phenomenon influences consumption behavior at different granularity levels is essential for recommending strategies to normalize the situation. With this aim, we performed a multi-scale analysis of data associated with bank transactions involving credit and debit cards. Our findings can be summarized into two main results: Coarse-grained analysis reveals the presence of the El Niño phenomenon and the recovery time in a given territory, while fine-grained analysis demonstrates a change in individuals’ purchasing patterns and in merchant relevance as a consequence of the climatic event. The results also indicate that society successfully withstood the natural disaster owing to the economic structure built over time. In this study, we present a new method that may be useful for better characterizing future extreme events.

Bottom-Up Coarse-Grained Modeling of DNA

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.645527 ◽

2021 ◽

Vol 8 ◽

Author(s):

Tiedong Sun ◽

Vishal Minhas ◽

Nikolay Korolev ◽

Alexander Mirzoev ◽

Alexander P. Lyubartsev ◽

...

Keyword(s):

Degrees Of Freedom ◽

Deoxyribonucleic Acid ◽

Persistence Length ◽

Coarse Graining ◽

Potential Of Mean Force ◽

Coarse Grained ◽

Bottom Up ◽

Slow Dynamic ◽

Molecular Systems ◽

Fine Grained

Recent advances in methodology enable effective coarse-grained modeling of deoxyribonucleic acid (DNA) based on underlying atomistic force field simulations. The so-called bottom-up coarse-graining practice separates fast and slow dynamic processes in molecular systems by averaging out fast degrees of freedom represented by the underlying fine-grained model. The resulting effective potential of interaction includes the contribution from fast degrees of freedom effectively in the form of potential of mean force. The pair-wise additive potential is usually adopted to construct the coarse-grained Hamiltonian for its efficiency in a computer simulation. In this review, we present a few well-developed bottom-up coarse-graining methods, discussing their application in modeling DNA properties such as DNA flexibility (persistence length), conformation, “melting,” and DNA condensation.

From quantum to subcellular scales: multi-scale simulation approaches and the SIRAH force field

Interface Focus ◽

10.1098/rsfs.2018.0085 ◽

2019 ◽

Vol 9 (3) ◽

pp. 20180085 ◽

Cited By ~ 7

Author(s):

Matías R. Machado ◽

Ari Zeida ◽

Leonardo Darré ◽

Sergio Pantano

Keyword(s):

Force Field ◽

Computational Methods ◽

Coarse Grained ◽

Cellular Biology ◽

Base Pairs ◽

Fine Grained ◽

Multi Scale ◽

Double Stranded Dna ◽

Structural Methods ◽

Simulation Scheme

Modern molecular and cellular biology profits from astonishing resolution structural methods, currently even reaching the whole cell level. This is encompassed by the development of computational methods providing a deep view into the structure and dynamics of molecular processes happening at very different scales in time and space. Linking such scales is of paramount importance when aiming at far-reaching biological questions. Computational methods at the interface between classical and coarse-grained resolutions are gaining momentum with several research groups dedicating important efforts to their development and tuning. An overview of such methods is addressed herein, with special emphasis on the SIRAH force field for coarse-grained and multi-scale simulations. Moreover, we provide proof of concept calculations on the implementation of a multi-scale simulation scheme including quantum calculations on a classical fine-grained/coarse-grained representation of double-stranded DNA. This opens the possibility to include the effect of large conformational fluctuations in chromatin segments on, for instance, the reactivity of particular base pairs within the same simulation framework.