Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning

Kanada, Ryo; Tokuhisa, Atsushi; Tsuda, Koji; Okuno, Yasushi; Terayama, Kei

doi:10.3390/biom10030482

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…Machine learning has found applications in molecular simulations , too. In designing CG models themselves, ML has been extensively used in recent times (see refs − for a nonexhaustive list of references and ref for a detailed review). Again, these CG models are mostly focused on the structural properties of the systems.…”

Section: Introductionmentioning

confidence: 99%

Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach

Bag

Meinel

Müller‐Plathe

2022

J. Chem. Theory Comput.

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Coarse-grained molecular dynamics (MD) simulation is a promising alternative to all-atom MD simulation for the fast calculation of system properties, which is imperative in designing materials with a specific target property. There have been several coarse-graining strategies developed over the past few years that provide accurate structural properties of the system. However, these coarse-grained models share a major drawback in that they introduce an artificial acceleration in molecular mobility. In this paper, we report a data-driven approach to generate coarse-grained models that preserve the all-atom molecular mobility. We designed a machine learning model in the form of an artificial neural network, which directly predicts the simulation-ready mobility-preserving coarse-grained potential as an output given the all-atom force field (FF) parameters as inputs. As a proof of principle, we took 2,3,4-trimethylpentane as a model system and described the development of machine learning models in detail. We quantify the artificial acceleration in molecular mobility by defining the acceleration factor as the ratio of the coarse-grained and the all-atom diffusion coefficient. The predicted coarse-grained potential generated by the best machine learning model can bring down the acceleration factor to a value of ∼2, which could be otherwise as large as 7 for a typical value of 3 × 10–9 m2 s–1 for the all-atom diffusion coefficient. We believe our method will be of interest in the community as a route to generating coarse-grained potentials with accurate dynamics.

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Section: Introductionmentioning

confidence: 99%

Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach

Bag

Meinel

Müller‐Plathe

2022

J. Chem. Theory Comput.

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show abstract

“…As a result, we succeeded in constructing the phase diagram experimentally with discovering new phases within 11 cycles (Figure d). In addition, we showed that this framework was also useful for quick identification of successful parameter regions in molecular dynamics simulations of the F1-ATPase motor . Our implementation of active learning for phase diagram construction is available on GitHub at and a representative library of active learning, modAL, would also be useful.…”

Section: Algorithms For Black-box Optimization and Their Applicationsmentioning

confidence: 99%

“…In addition, we showed that this framework was also useful for quick identification of successful parameter regions in molecular dynamics simulations of the F1-ATPase motor. 29 Our implementation of active learning for phase diagram construction is available on GitHub at https:// github.com/tsudalab/PDC/ and a representative library of active learning, modAL, 46 would also be useful.…”

Section: Active Learningmentioning

confidence: 99%

Black-Box Optimization for Automated Discovery

et al. 2021

Self Cite

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In chemistry and materials science, researchers and engineers discover, design, and optimize chemical compounds or materials with their professional knowledge and techniques. At the highest level of abstraction, this process is formulated as blackbox optimization. For instance, the trial-and-error process of synthesizing various molecules for better material properties can be regarded as optimizing a black-box function describing the relation between a chemical formula and its properties. Various black-box optimization algorithms have been developed in the machine learning and statistics communities. Recently, a number of researchers have reported successful applications of such algorithms to chemistry. They include the design of photofunctional molecules and medical drugs, optimization of thermal emission materials and high Li-ion conductive solid electrolytes, and discovery of a new phase in inorganic thin films for solar cells. There are a wide variety of algorithms available for black-box optimization, such as Bayesian optimization, reinforcement learning, and active learning. Practitioners need to select an appropriate algorithm or, in some cases, develop novel algorithms to meet their demands. It is also necessary to determine how to best combine machine learning techniques with quantum mechanics-and molecular mechanics-based simulations, and experiments. In this Account, we give an overview of recent studies regarding automated discovery, design, and optimization based on black-box optimization. The Account covers the following algorithms: Bayesian optimization to optimize the chemical or physical properties, an optimization method using a quantum annealer, best-arm identification, gray-box optimization, and reinforcement learning. In addition, we introduce active learning and boundless objectivefree exploration, which may not fall into the category of black-box optimization. Data quality and quantity are key for the success of these automated discovery techniques. As laboratory automation and robotics are put forward, automated discovery algorithms would be able to match human performance at least in some domains in the near future.

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“…Other groups have also demonstrated the active learning framework efficacy in RBFE simulations, 36,37 as well as in docking, [38][39][40] forcefield development 41 and course-graining. 42 Recent work by…”

Section: Introductionmentioning

confidence: 99%

An Automated On-The-Fly Optimization of Resource Allocation for High-Throughput Protein-Ligand Binding Free Energy Simulations

Koby

Gutkin

Patel

et al. 2023

Preprint

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Molecular dynamics simulations to compute protein to small molecule binding free energies are becoming a valuable tool in the early stages of drug discovery. However, their cost and complexity are often prohibitive for high-throughput studies. Herein, we present an automated workflow for the thermodynamic integration scheme with the “on-the-fly” optimization of computational resource allocation for each λ-window of both relative and absolute binding free energy simulations. This iterative workflow utilizes automatic equilibration detection and convergence testing via the Jensen-Shannon distance to determine optimal simulation stopping points in an entirely data-driven manner. We benchmark our workflow on the well-characterized systems cyclin-dependent kinase 2 and T4 Lysozyme L99A/M102Q mutant, as well as the more flexible SARS-CoV-2 papain-like protease. We demonstrate that this proposed protocol can achieve over an 85% reduction in computational expense while maintaining similar levels of accuracy when compared to other benchmarking protocols. We examine the performance of this protocol on both small and large molecular transformations. The cost accuracy tradeoff of repeated runs is also investigated.

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Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning

Cited by 6 publications

References 33 publications

Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach

Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach

Black-Box Optimization for Automated Discovery

An Automated On-The-Fly Optimization of Resource Allocation for High-Throughput Protein-Ligand Binding Free Energy Simulations

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