Optimization and analysis of force field parameters by combination of genetic algorithms and neural networks

Hunger, Johannes; Hüttner, Gottfried

doi:10.1002/(sici)1096-987x(199903)20:4<455::aid-jcc6>3.0.co;2-1

Cited by 100 publications

(64 citation statements)

References 13 publications

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“…In these cases, it is required to build an approximation model to assess solutions employing polynomial regression [52], neural networks [53][54][55], SVM [56], Markov fitness models [57], kriging [58] or radial basis functions [59], for example. Some authors combine their use with that of real objective functions [60,61].…”

Section: Specifically-located Hybridizationsmentioning

confidence: 99%

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

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This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer's willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.

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Section: Specifically-located Hybridizationsmentioning

confidence: 99%

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

et al. 2017

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“…The most relevant descriptors were chosen through a genetic algorithm technique [47][48][49]. A genetic algorithm starts by selecting a large number of randomly chosen variables (chromosome), and variables in each chromosome called "genes".…”

Section: Multiple Linear Regressions Based On Genetic Algorithm Ordinmentioning

confidence: 99%

Analysis of B-Raf $$^{\mathrm{V600E}}$$ V 600 E inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies

Aalizadeh

Pourbasheer

2015

Mol Divers

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In the present work, a molecular modeling study was carried out using 2D and 3D quantitative structure-activity relationships for the various series of compounds known as B-Raf[Formula: see text] inhibitors. For 2D-QSAR analysis, a linear model was developed by MLR based on GA-OLS with appropriate results [Formula: see text], which was validated by several external validation techniques. To perform a 3D-QSAR analysis, CoMFA and CoMSIA methods were used. The selected CoMFA model could provide reliable statistical values [Formula: see text] based on the training set in the biases of the selected alignment. Using the same selected alignment, a statistically reliable CoMSIA model, out of thirty-one different combinations, was also obtained [Formula: see text]. The predictive accuracy of the derived models was rigorously evaluated with the external test set of nineteen compounds based on several validation techniques. Molecular docking simulations and pharmacophore analyses were also performed to derive the true conformations of the most potent inhibitors with B-Raf[Formula: see text] kinase.

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“…(Pietropaolo et al, 2011) Reactivity and other properties are directly linked to the conformational arrangement of molecules. (Hunger & Huttner, 1999) Every chemical property must be understood according to its molecular structure and atomic connections. (Pietropaolo et al, 2011) Indeed, knowledge of structural arrangement is important since it underlies studies in chemical reactions and other molecular behaviors.…”

Section: Conformational Analysis: Early History and Importancementioning

confidence: 99%

Quantum Chemistry and Chemometrics Applied to Conformational Analysis

Bruni¹,

Leite²

2012

Quantum Chemistry - Molecules for Innovations

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Optimization and analysis of force field parameters by combination of genetic algorithms and neural networks

Cited by 100 publications

References 13 publications

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Learnheuristics: hybridizing metaheuristics with machine learning for optimization with dynamic inputs

Analysis of B-Raf $$^{\mathrm{V600E}}$$ V 600 E inhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies

Quantum Chemistry and Chemometrics Applied to Conformational Analysis

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