Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical Descriptors and Solvent Conditions

Chen, Chia‐Hsiu; Tanaka, Kenichi; Funatsu, Kimito

doi:10.1007/s10895-018-2233-4

Cited by 23 publications

(19 citation statements)

References 27 publications

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“…The performances of all of the four DT-based ensemble learning models were similar in agreement of our previous work results which ensemble learning was suitable for fluorescence prediction than single models [40]. The…”

Section: Case Study 1: Fluorescence Dataset Performance Of Dt-based Esupporting

confidence: 90%

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Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

et al. 2020

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Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR). With the growing number of ensemble learning models such as random forest, the effectiveness of QSAR/QSPR will be limited by the machine's inability to interpret the predictions to researchers. In fact, many implementations of ensemble learning models are able to quantify the overall magnitude of each feature. For example, feature importance allows us to assess the relative importance of features and to interpret the predictions. However, different ensemble learning methods or implementations may lead to different feature selections for interpretation. In this paper, we compared the predictability and interpretability of four typical well-established ensemble learning models (Random forest, extreme randomized trees, adaptive boosting and gradient boosting) for regression and binary classification modeling tasks. Then, the blending methods were built by summarizing four different ensemble learning methods. The blending method led to better performance and a unification interpretation by summarizing individual predictions from different learning models. The important features of two case studies which gave us some valuable information to compound properties were discussed in detail in this report. QSPR modeling with interpretable machine learning techniques can move the chemical design forward to work more efficiently, confirm hypothesis and establish knowledge for better results.

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Section: Case Study 1: Fluorescence Dataset Performance Of Dt-based Esupporting

confidence: 90%

“…use of QC descriptors can successfully improve prediction and interpretability from our previous work [40] and provide more specific physical meanings. We consider solvent species in model constructing since the solvent effect plays a more critical role in the process of fluorescence [41].…”

Section: Descriptor Selectionmentioning

confidence: 88%

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

et al. 2020

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“…F06 [C-N] was used in a model to describe the anti-proliferative effect of phenyl 4-(2-oxoimidazolidin-1yl)-benzenesulfonates (local QSAR model) (93), anti-malaric effect (94), or skin permeability of substances (95). P_VSA_MR_6 has also been used for modeling of skin permeability (95), whereas we have identified the use of Chi1_EA(dm) only for the QSPR modeling of fluorescence properties of a number of fluorescent dyes (96). The aromatic nitrogen (N-073) has been shown to correlate positively with HIV-1 integrase activity inhibition (97) and negatively with the inhibition of the fibroblast growth factor (FGFR) (98).…”

Section: Discussionmentioning

confidence: 99%

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ancuceanu

Tamba

Stoicescu³

et al. 2019

Preprint

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Prototype of a family of at least nine members, c-src tyrosine kinase is a therapeutically interesting target, because its inhibition might be of interest not only in a number of malignancies, but also in a diverse array of conditions, from neurodegenerative pathologies to certain viral infections. Computational methods in drug discovery are considerably cheaper than conventional methods and offer opportunities of screening very large numbers of compounds in conditions that would be simply impossible within the wet lab experimental settings. We have explored the use of global QSAR models and molecular ligand docking in the discovery of new c-src tyrosine kinase inhibitors. Using a data set of 1038 compounds from ChEMBL and 19 blocks of molecular descriptors, we have developed over 200 QSAR classification models, based on six machine learning algorithms and 17 feature selection methods. We have selected 49 with reasonably good performance (positive predictive value and balanced accuracy higher than 70% in nested cross validation) and the models were assembled by stacking with a simple majority vote and used for the virtual screening of over the “named” ZINC data set (over 100,000 compounds). 744 compounds were predicted by at least 50% of the QSAR models as active, 147 compounds were within the applicability domain and predicted by at least 75% of the models to be active. The latter 147 compounds were submitted to molecular ligand docking using Vina and Ledock, and a number of 90 were predicted to be active based on the binding energy. External data from CHEMBL and PUBCHEM confirmed that at least 7.83% (in the case of QSAR) or 6.67% (in the case of integrated QSAR and molecular docking) of the compounds are active on the c-src target.

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“…A number of previous studies investigated quantitative AD: (1) range setting of each variable or latent variable after dimensional reduction [21] by Principal Component Analysis (PCA) [22], (2) distance from neighboring point [23] using kNN [20], (3) data density estimation [24] by OneClassSVM (OCSVM) [25], and (4) variation of predicted values [26] by ensemble learning [8,9]. [2,3,4,5,6,7,8,9,10,15,20] In this study, we proposed to use Tanimoto distance of binary unhashed Morgan fingerprint (radius = 4) with the training data as the AD estimation model. First, Tanimoto distances were calculated for each compound against all compounds in the training data.…”

Section: Applicability Domain (Ad)mentioning

confidence: 99%

“…QSPR studies for fluorescent materials are also being conducted. Chen et al [2] predicted absorption and emission wavelength with high precision by adding quantum calculation results and solvent information as descriptors. Among the fluorescent substances, those having boron-dipyrromethene (BODIPY) in the molecular skeleton have a sharp spectrum [3] and stably high quantum yield [4], and the influence of solvent to the wavelength is small.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Machine Learning and Applicability Domain Estimation for Fluorescence Properties and its Application to Structural Design

Sugawara

Kotera

Tanaka

et al. 2019

J. Comput. Aided Chem.

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Fluorescent substances are used in a wide range of applications, and the method that effectively design molecules having desirable absorption and emission wavelength is required. In this study, we used borondipyrromethene (BODIPY) compounds as a case study, and constructed high precision wavelength prediction model using ensemble learning. Prediction accuracy improved in stacking model using RDKit descriptors and Morgan fingerprint. The variables related to the molecular skeleton and the conjugation length were shown to be important. We also proposed an applicability domain (AD) estimation model that directly use the descriptors based on Tanimoto distance. The performance of the AD models was shown better than the OCSVM-based model. Using our proposed stacking model and AD model, newly generated compounds were screened and we obtained 602 compounds which were estimated inside the AD in both absorption wavelength and emission wavelength.

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Random Forest Approach to QSPR Study of Fluorescence Properties Combining Quantum Chemical Descriptors and Solvent Conditions

Cited by 23 publications

References 27 publications

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications

Use of QSAR Global Models and Molecular Docking for Developing New Inhibitors of c-Src Tyrosine Kinase

Ensemble Machine Learning and Applicability Domain Estimation for Fluorescence Properties and its Application to Structural Design

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