Towards understanding aromatase inhibitory activity via QSAR modeling

Shoombuatong, Watshara; Schaduangrat, Nalini; Nantasenamat, Chanin

doi:10.17179/excli2018-1417

Cited by 10 publications

(4 citation statements)

References 97 publications

(170 reference statements)

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“…In the current study, we aimed to develop a classification model that is able to determine active from inactive compounds, and build a web-app for differentiating compounds for M pro with selectivity. We followed the Organisation for Economic Co-operation and Development (OECD) recommendations to develop robust QSAR models for this purpose [64]. These guidelines comprise of the following major points: (i) the data set should have a defined endpoint, (ii) it should use an explicit learning algorithm, (iii) there should be a defined applicability domain of the built model, (iv) appropriate measurement of robustness and predictivity and (v) interpretation of the important features of the QSAR model.…”

Section: Discussionmentioning

confidence: 99%

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

et al. 2023

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (Mpro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure–activity relationship (CSAR) model to find substructures that leads to to anti-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set’s modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to Mpro. Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.

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Section: Discussionmentioning

confidence: 99%

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

et al. 2023

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“…The feature subset achieving the highest Matthews correlation coefficient (MCC) was considered as the optimal feature subset. The implementation of these classifiers in the two-step feature selection strategy is the same as used in our previous studies 18 , 31 , 38 – 41 …”

Section: Methodsmentioning

confidence: 99%

“…In order to examine the performance of our proposed predictor, we used five common statistical metrics including ACC, MCC, sensitivity (Sn) and specificity (Sp) 24 , 42 as described follows: where TP, TN, FP and FN represent the number of true positives, true negatives, false positive and false negatives, respectively. In addition, the area under the receiver operating characteristic (AUC) was employed as another statistical metric 39 – 41 , 43 .…”

Section: Methodsmentioning

confidence: 99%

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins

Ahmad

Charoenkwan

Quinn

et al. 2022

Sci Rep

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Fast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository (https://github.com/saeed344/SCORPION).

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“…Partial least square (PLS) is a very common modeling method when correlated latent variables exist among descriptors (Helland 2001). As a generalization of the traditional multiple linear regression (MLR) algorithm, PLS has the ability to analyze data with strong collinearity, large noise, and multiple variables, and it also improves the overfitting problem of the MLR algorithm to a certain extent (Shoombuatong et al 2018). In addition, it has the ability to model multiple response variables simultaneously (Cao et al 2010a).…”

Section: Partial Least Square (Pls)mentioning

confidence: 99%

Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates

Wang¹,

Ding²,

Shi³

et al. 2021

Arch Toxicol

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Organophosphates (OPs) are hazardous chemicals widely used in industry and agriculture. Distribution of their residues in nature causes serious risks to humans, animals, and plants. To reduce hazards from OPs, quantitative structure-activity relationship (QSAR) models for predicting their acute oral toxicity in rats and mice and inhibition constants concerning human acetylcholinesterase were developed according to the bioactivity data of 456 unique OPs. Based on robust, two-dimensional molecular descriptors and quantum chemical descriptors, which accurately reflect OP electronic structures and reactivities, the influences of eight machine-learning algorithms on the prediction performance of the QSAR models were explored, and consensus QSAR models were constructed. Several strict model validation indices and the results of applicability domain evaluations show that the established consensus QSAR models exhibit good robustness, practical prediction abilities, and wide application scopes. Poor correlation was observed between acute oral toxicity at the mammalian level and the inhibition constants at the molecular level, indicating that the acute toxicity of OPs cannot be evaluated only by the experimental data of enzyme inhibitory activity, their toxicokinetic characteristics must also be considered. The constructed QSAR models described herein provide rapid, theoretical assessment of the bioactivity of unstudied or unknown OPs, as well as guidance for making decisions regarding their regulation.

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Towards understanding aromatase inhibitory activity via QSAR modeling

Cited by 10 publications

References 97 publications

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins

Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates

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