Machine Learning, Molecular Modeling, and QSAR Studies on Natural Products Against Alzheimer’s Disease

Moura, Érika Paiva de; Fernandes, Natan Dias; Medeiros, Herbert Igor Rodrigues de; Scotti, Luciana

doi:10.2174/0929867328666210603104749

Cited by 13 publications

(14 citation statements)

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“…In this study, the classification models were built using the support vector machine (SVM), logistic regression (LR), k-nearest neighbor (KNN), artificial neural network (ANN), naïve Bayes (NB), random forest (RF) (with a tree number of 20 and the maximum tree depth of 15), and decision tree (DT). An in-depth description of the application of these methods in drug discovery can be obtained from some excellent studies and research papers [33,34]. All of these calculations were integrated with Orange Canvas 3.11 software (freely available at https://orange.biolab.si/, accessed on 8 March 2018).…”

Section: Model Performance Evaluationmentioning

confidence: 99%

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Guan

Fan

et al. 2022

Molecules

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Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors.

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Section: Model Performance Evaluationmentioning

confidence: 99%

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Guan

Fan

et al. 2022

Molecules

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“…With the development of supervised machine learning methods such as support vector regression, partial least-squares (PLS) regression, and artificial neural networks, these methods have now been significantly improved (SVR). PC descriptors of the structures and molecular fingerprints are used in these techniques. ,, The potential for solving issues has been dramatically increased as a result. Using the methods of artificial neural network QSAR ANN , support vector regression QSAR SVR , and kernel-based PLS regression QSAR KPLS , models were created from the relation between molecular descriptors and activity. ,, A trustworthy predictive QSAR model has been created that can be utilized to direct the creation of new chemicals and routine synthesis or acquisition of additional compounds.…”

Section: Introductionmentioning

confidence: 99%

“…QSAR can also be applied to chemical modeling. A highly efficient QSAR KPLS method based on unrestricted chemical fingerprinting in combination with kernel-based (KPLS) is also used. ,, …”

Section: Introductionmentioning

confidence: 99%

“…PC descriptors of the structures and molecular fingerprints are used in these techniques. 34 , 35 , 38 The potential for solving issues has been dramatically increased as a result. Using the methods of artificial neural network QSAR ANN , support vector regression QSAR SVR , and kernel-based PLS regression QSAR KPLS , models were created from the relation between molecular descriptors and activity.…”

Section: Introductionmentioning

confidence: 99%

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Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer’s Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors

et al. 2023

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Searching for thiosemicarbazone derivatives with the potential to inhibit acetylcholinesterase for the treatment of Alzheimer’s disease (AD) is an important current goal. The QSARKPLS, QSARANN, and QSARSVR models were constructed using binary fingerprints and physicochemical (PC) descriptors of 129 thiosemicarbazone compounds screened from a database of 3791 derivatives. The R 2 and Q 2 values for the QSARKPLS, QSARANN, and QSARSVR models are greater than 0.925 and 0.713 using dendritic fingerprint (DF) and PC descriptors, respectively. The in vitro pIC50 activities of four new design-oriented compounds N1, N2, N3, and N4, from the QSARKPLS model using DFs, are consistent with the experimental results and those from the QSARANN and QSARSVR models. The designed compounds N1, N2, N3, and N4 do not violate Lipinski-5 and Veber rules using the ADME and BoiLED-Egg methods. The binding energy, kcal mol–1, of the novel compounds to the 1ACJ-PDB protein receptor of the AChE enzyme was also obtained by molecular docking and dynamics simulations consistent with those predicted from the QSARANN and QSARSVR models. New compounds N1, N2, N3, and N4 were synthesized, and the experimental in vitro pIC50 activity was determined in agreement with those obtained from in silico models. The newly synthesized thiosemicarbazones N1, N2, N3, and N4 can inhibit 1ACJ-PDB, which is predicted to be able to cross the barrier. The DFT B3LYP/def-SV(P)-ECP quantization calculation method was used to calculate E HOMO and E LUMO to account for the activities of compounds N1, N2, N3, and N4. The quantum calculation results explained are consistent with those obtained in in silico models. The successful results here may contribute to the search for new drugs for the treatment of AD.

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“…Natural products are compounds isolated from plants that provide a variety of lead structures for the development of new drugs by the pharmaceutical industry [ 2 , 5 - 10 ]. The interest in these substances increases because of their beneficial effects on human health.…”

mentioning

confidence: 99%

Medicinal Chemistry Studies Against Neurodegenerative Diseases

Scotti

2022

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Machine Learning, Molecular Modeling, and QSAR Studies on Natural Products Against Alzheimer’s Disease

Cited by 13 publications

References 0 publications

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer’s Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors

Medicinal Chemistry Studies Against Neurodegenerative Diseases

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