scholarly journals MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yongbeom Kwon ◽  
Juyong Lee
Keyword(s):  
Global Optimization ◽  
Reinforcement Learning ◽  
Chemical Space ◽  
Optimization Methods ◽  
Optimization Method ◽  
Conformational Space ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Annealing Algorithm ◽  
Shed Light

AbstractHere, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.

scholarly journals MolFinder: An Evolutionary Algorithm for the Global Optimization of Molecular Properties and the Extensive Exploration of Chemical Space Using SMILES

2020 ◽  
Author(s):  
Yongbeom Kwon ◽  
Juyong Lee
Keyword(s):  
Global Optimization ◽  
Reinforcement Learning ◽  
Chemical Space ◽  
Optimization Methods ◽  
Optimization Method ◽  
Conformational Space ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Annealing Algorithm ◽  
Shed Light

Abstract Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.

scholarly journals MolFinder: An Efficient Global Molecular Property Optimization and Search Algorithm Using SMILES

2020 ◽  
Author(s):  
Yongbeom Kwon ◽  
Juyong Lee
Keyword(s):  
Global Optimization ◽  
Reinforcement Learning ◽  
Search Algorithm ◽  
Optimization Methods ◽  
Optimization Method ◽  
Conformational Space ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Annealing Algorithm ◽  
Shed Light

<div> <div> <div> <p>Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods. </p> </div> </div> </div>

scholarly journals MolFinder: An Efficient Global Molecular Property Optimization and Search Algorithm Using SMILES

2020 ◽  
Author(s):  
Yongbeom Kwon ◽  
Juyong Lee
Keyword(s):  
Global Optimization ◽  
Reinforcement Learning ◽  
Search Algorithm ◽  
Optimization Methods ◽  
Optimization Method ◽  
Conformational Space ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Annealing Algorithm ◽  
Shed Light

<div> <div> <div> <p>Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods. </p> </div> </div> </div>

A New Global Optimization Method for Simultaneous Computation on Expensive Black-Box Functions

2003 ◽  
Author(s):  
Liqun Wang ◽  
Songqing Shan ◽  
G. Gary Wang
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Black Box ◽  
Efficient Global Optimization ◽  
Global Optimization Method ◽  
Global Optimization Methods

The presence of black-box functions in engineering design, which are usually computation-intensive, demands efficient global optimization methods. This work proposes a new global optimization method for black-box functions. The global optimization method is based on a novel mode-pursuing sampling (MPS) method which systematically generates more sample points in the neighborhood of the function mode while statistically covers the entire search space. Quadratic regression is performed to detect the region containing the global optimum. The sampling and detection process iterates until the global optimum is obtained. Through intensive testing, this method is found to be effective, efficient, robust, and applicable to both continuous and discontinuous functions. It supports simultaneous computation and applies to both unconstrained and constrained optimization problems. Because it does not call any existing global optimization tool, it can be used as a standalone global optimization method for inexpensive problems as well. Limitation of the method is also identified and discussed.

scholarly journals A multiple-data-based efficient global optimization algorithm and its parallel implementation for automotive body design

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879434 ◽  
Author(s):  
Bing Xu ◽  
Yong Cai
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Graphics Processing Unit ◽  
Optimization Method ◽  
Efficient Global Optimization ◽  
Processing Unit ◽  
Global Optimization Algorithm ◽  
Multiple Data ◽  
Practical Engineering ◽  
Graphics Processing

The purpose of this article is to improve the convergence efficiency of the traditional efficient global optimization method. Furthermore, we try a graphics processing unit–based parallel computing method to improve the computing efficiency of the efficient global optimization method for both mathematical and practical engineering problems. First, we propose a multiple-data-based efficient global optimization algorithm instead of the multiple-surrogates-based efficient global optimization algorithm. Second, a novel graphics processing unit–based general-purpose computing technology is adopted to accelerate the solution efficiency of our multiple-data-based efficient global optimization algorithm. Third, a hybrid parallel computing approach using the OpenMP and compute unified device architecture is adopted to further improve the solution efficiency of forward problems in practical application. This is accomplished by integrating the graphics processing unit–based finite element method numerical analysis system into the optimization software. The numerical results show that for the same problem, the optimal result of the multiple-data-based efficient global optimization algorithm is consistently better than the multiple-surrogates-based efficient global optimization algorithm with the same optimization iterations. In addition, the graphics processing unit–based parallel simulation system helps in the reduction of the calculation time for practical engineering problems. The multiple-data-based efficient global optimization method performs stably in both high-order mathematical functions and large-scale nonlinear practical engineering optimization problems. An added benefit is that the computational time and accuracy are no longer obstacles.

scholarly journals SADEA-II: A generalized method for efficient global optimization of antenna design

2016 ◽  
Vol 4 (2) ◽  
pp. 86-97 ◽  
Author(s):  
Bo Liu ◽  
Slawomir Koziel ◽  
Nazar Ali
Keyword(s):  
Data Mining ◽  
Global Optimization ◽  
Design Optimization ◽  
Surrogate Model ◽  
Simulation Models ◽  
Optimization Methods ◽  
Optimization Method ◽  
Antenna Design ◽  
Efficient Global Optimization ◽  
Optimization Framework

Abstract Efficiency improvement is of great significance for simulation-driven antenna design optimization methods based on evolutionary algorithms (EAs). The two main efficiency enhancement methods exploit data-driven surrogate models and/or multi-fidelity simulation models to assist EAs. However, optimization methods based on the latter either need ad hoc low-fidelity model setup or have difficulties in handling problems with more than a few design variables, which is a main barrier for industrial applications. To address this issue, a generalized three stage multi-fidelity-simulation-model assisted antenna design optimization framework is proposed in this paper. The main ideas include introduction of a novel data mining stage handling the discrepancy between simulation models of different fidelities, and a surrogate-model-assisted combined global and local search stage for efficient high-fidelity simulation model-based optimization. This framework is then applied to SADEA, which is a state-of-the-art surrogate-model-assisted antenna design optimization method, constructing SADEA-II. Experimental results indicate that SADEA-II successfully handles various discrepancy between simulation models and considerably outperforms SADEA in terms of computational efficiency while ensuring improved design quality. Highlights An EFFICIENT antenna design global optimization method for problems requiring very expensive EM simulations. A new multi-fidelity surrogate-model-based optimization framework to perform RELIABLE efficient global optimization A data mining method to address distortions of EM models of different fidelities (bottleneck of multi-fidelity design).

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