QSAR classification and regression models for β-secretase inhibitors using relative distance matrices

Ruiz, Irene Luque; Gómez‐Nieto, Miguel Ángel

doi:10.1080/1062936x.2018.1442879

Cited by 5 publications

(3 citation statements)

References 46 publications

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“…SVM can map input variables into high‐dimensional feature space, from which linear regression analysis is performed. [ 17,18 ]…”

Section: Methodsmentioning

confidence: 99%

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Prediction of enantioselectivity in thiol addition to imines catalyzed by chiral phosphoric acids

2022

J of Physical Organic Chem

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Predicting enantioselectivities in asymmetric catalytic reactions through chemometric approach is a challenging area. In this paper, quantitative structure−selectivity relationship (QSSR) models were successfully developed for thiol addition to N‐acylimines catalyzed by chiral phosphoric acids. Ten Dragon molecular descriptors calculated from thiols and phosphoric acid catalysts were used to correlate with 1075 enantioselectivities ΔΔG. Machine learning algorithms, support vector machine (SVM) and random forest (RF), were adopted to build QSSRs after descriptor subset selection based on the multiple linear regression technique. The SVM and RF models, respectively, have coefficient of determination R2 of 0.925 and 0.944 for the training set, and of 0.837 and 0.851 for the test set. Both SVM and RF models were subjected to internal and external validation and estimation in prediction ability and systematic error.

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“…SVM can map input variables into high‐dimensional feature space, from which linear regression analysis is performed. [ 17,18 ]…”

Section: Methodsmentioning

confidence: 99%

“…RF, as an ensemble machine learning algorithm, is very successful in fitting and evaluating large data. [ 18–21 ] It constructs a decision tree by generating bootstrapped samples from a training dataset. When the trees are established, feature subsets are randomly selected to find the best split rule at each split of trees.…”

Section: Methodsmentioning

confidence: 99%

Prediction of enantioselectivity in thiol addition to imines catalyzed by chiral phosphoric acids

2022

J of Physical Organic Chem

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“…The one-complement D of the Tanimoto/Jaccard coefficient, where D=1−J, has been proven to be a real metric, satisfying all the known properties of distance measures [35]. In comparison to vector space-based methods, there is limited research reported in the literature exploring the quantitative relationship between computed molecular similarity and activity in QSAR/QSPR modeling [7,16,19,36,37,38,39,40,41,42,43,44,45].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling

Kausar

Falcão

2019

Molecules

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The performance of quantitative structure–activity relationship (QSAR) models largely depends on the relevance of the selected molecular representation used as input data matrices. This work presents a thorough comparative analysis of two main categories of molecular representations (vector space and metric space) for fitting robust machine learning models in QSAR problems. For the assessment of these methods, seven different molecular representations that included RDKit descriptors, five different fingerprints types (MACCS, PubChem, FP2-based, Atom Pair, and ECFP4), and a graph matching approach (non-contiguous atom matching structure similarity; NAMS) in both vector space and metric space, were subjected to state-of-art machine learning methods that included different dimensionality reduction methods (feature selection and linear dimensionality reduction). Five distinct QSAR data sets were used for direct assessment and analysis. Results show that, in general, metric-space and vector-space representations are able to produce equivalent models, but there are significant differences between individual approaches. The NAMS-based similarity approach consistently outperformed most fingerprint representations in model quality, closely followed by Atom Pair fingerprints. To further verify these findings, the metric space-based models were fitted to the same data sets with the closest neighbors removed. These latter results further strengthened the above conclusions. The metric space graph-based approach appeared significantly superior to the other representations, albeit at a significant computational cost.

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Modeling of BACE-1 Inhibitors as Anti-Alzheimer’s Agents

et al. 2023

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QSAR classification and regression models for β-secretase inhibitors using relative distance matrices

Cited by 5 publications

References 46 publications

Prediction of enantioselectivity in thiol addition to imines catalyzed by chiral phosphoric acids

Prediction of enantioselectivity in thiol addition to imines catalyzed by chiral phosphoric acids

Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling

Modeling of BACE-1 Inhibitors as Anti-Alzheimer’s Agents

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