Comprehensive ensemble in QSAR prediction for drug discovery

Kwon, Sunyoung; Bae, Ho; Jo, Jeonghee; Yoon, Sungroh

doi:10.1186/s12859-019-3135-4

Cited by 161 publications

(91 citation statements)

References 41 publications

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“…The GB algorithm was also successfully applied by Ancuceanu et al [ 37 ] for cytotoxicity prediction. The performance of GB in the modeling of various QSARs was compared to other ensemble methods in a study by Kwon et al [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2

Mizera

Latek

Cielecka‐Piontek

2020

IJMS

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The selective targeting of the cannabinoid receptor 2 (CB2) is crucial for the development of peripheral system-acting cannabinoid analgesics. This work aimed at computer-assisted identification of prospective CB2-selective compounds among the constituents of Cannabis Sativa. The molecular structures and corresponding binding affinities to CB1 and CB2 receptors were collected from ChEMBL. The molecular structures of Cannabis Sativa constituents were collected from a phytochemical database. The collected records were curated and applied for the development of quantitative structure-activity relationship (QSAR) models with a machine learning approach. The validated models predicted the affinities of Cannabis Sativa constituents. Four structures of CB2 were acquired from the Protein Data Bank (PDB) and the discriminatory ability of CB2-selective ligands and two sets of decoys were tested. We succeeded in developing the QSAR model by achieving Q2 5-CV > 0.62. The QSAR models helped to identify three prospective CB2-selective molecules that are dissimilar to already tested compounds. In a complementary structure-based virtual screening study that used available PDB structures of CB2, the agonist-bound, Cryogenic Electron Microscopy structure of CB2 showed the best statistical performance in discriminating between CB2-active and non-active ligands. The same structure also performed best in discriminating between CB2-selective ligands from non-selective ligands.

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Section: Discussionmentioning

confidence: 99%

Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2

Mizera

Latek

Cielecka‐Piontek

2020

IJMS

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“…Ensemble learning is a common technique in machine learning, where multiple models are constructed and combined. Many studies have shown that ensemble learning improves prediction accuracy compared to individual models 23,[27][28][29] . We applied this technique by simply averaging the individual outputs without weighting.…”

Section: Hyperparameter Optimization and Model Trainingmentioning

confidence: 99%

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks

Sakai

Nagayasu

Shibui

et al. 2021

Sci Rep

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Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.

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“…The researchers in this ield extended their studies by binding more than one molecule. It leads to the inding of the 2D-QSAR technique (Kwon et al, 2019).…”

Section: Qsarmentioning

confidence: 99%

In Silico Prediction Tool for Drug-likeness of Compounds based on Ligand Based Screening

Ani

Anand

et al. 2020

ijrps

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Drug Likeness prediction is a time-consuming and tedious process. An in-vitro method the drug development takes a long time to come to market. The failure rate is also another one to think about in this method. There are many in-silico methods currently available and developing to help the drug discovery and development process. Many online tools are available for predicting and classifying a drug after analyzing the drug-likeness properties of compounds. But most tools have their advantages and disadvantages. In this study, a tool is developed to predict the drug-likeness of compounds given as input to this software. This may help the chemists in analyzing a compound before actually preparing a compound for the drug discovery process. The tool includes both descriptor-based calculation and fingerprint-based calculation of the particular compounds. The descriptor-calculation also includes a set of rules and filters like Lipinski’s rule, Ghose filter, Veber filter and BBB likeness. The previous studies proved that the fingerprint-based prediction is more accurate than descriptor-based prediction. So, in the current study, the drug-likeness prediction tool incorporated the molecular descriptors and fingerprint-based calculations based on five different fingerprint types. The current study incorporated five different machine learning algorithms for prediction of drug-likeness and selected the algorithm, which has a high accuracy rate. When a chemist inputs a particular compound in SMILES format, the drug-likeness prediction tool predicts whether the given candidate compound is drug or non-drug.

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Comprehensive ensemble in QSAR prediction for drug discovery

Cited by 161 publications

References 41 publications

Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2

Virtual Screening of C. Sativa Constituents for the Identification of Selective Ligands for Cannabinoid Receptor 2

Prediction of pharmacological activities from chemical structures with graph convolutional neural networks

In Silico Prediction Tool for Drug-likeness of Compounds based on Ligand Based Screening

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