PySpark and RDKit: Moving towards Big Data in Cheminformatics

Lovrić, Mario; Molero, José Manuel; Kern, Roman

doi:10.1002/minf.201800082

Cited by 75 publications

(46 citation statements)

References 10 publications

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“…5. All models were trained on an in-house big data server described in [40]. The evaluation of the models was conducted using the root mean squared error (RMSE, Eq.…”

Section: Model Training and Validationmentioning

confidence: 99%

Parasitic resistance as a predictor of faulty anodes in electro galvanizing: a comparison of machine learning, physical and hybrid models

Lovrić

Meister

Steck

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

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In industrial electro galvanizing lines aged anodes deteriorate zinc coating distribution over the strip width, leading to an increase in electricity and zinc cost. We introduce a data-driven approach in predictive maintenance of anodes to replace the cost- and labor-intensive manual inspection, which is still common for this task. The approach is based on parasitic resistance as an indicator of anode condition which might be aged or mis-installed. The parasitic resistance is indirectly observable via the voltage difference between the measured and baseline (theoretical) voltage for healthy anode. Here we calculate the baseline voltage by means of two approaches: (1) a physical model based on electrical and electrochemical laws, and (2) advanced machine learning techniques including boosting and bagging regression. The data was collected on one exemplary rectifier unit equipped with two anodes being studied for a total period of two years. The dataset consists of one target variable (rectifier voltage) and nine predictive variables used in the models, observing electrical current, electrolyte, and steel strip characteristics. For predictive modelling, we used Random Forest, Partial Least Squares and AdaBoost Regression. The model training was conducted on intervals where the anodes were in good condition and validated on other segments which served as a proof of concept that bad anode conditions can be identified using the parasitic resistance predicted by our models. Our results show a RMSE of 0.24 V for baseline rectifier voltage with a mean ± standard deviation of 11.32 ± 2.53 V for the best model on the validation set. The best-performing model is a hybrid version of a Random Forest which incorporates meta-variables computed from the physical model. We found that a large predicted parasitic resistance coincides well with the results of the manual inspection. The results of this work will be implemented in online monitoring of anode conditions to reduce operational cost at a production site.

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“…5. All models were trained on an in-house big data server described in [40]. The evaluation of the models was conducted using the root mean squared error (RMSE, Eq.…”

Section: Model Training and Validationmentioning

confidence: 99%

Parasitic resistance as a predictor of faulty anodes in electro galvanizing: a comparison of machine learning, physical and hybrid models

Lovrić

Meister

Steck

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

Self Cite

View full text Add to dashboard Cite

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“…The sequence- and structure-based descriptors were calculated by the RDKit 101 package that uses Python language, except for the DPC and PseAAC that were calculated using PyBioMed 102 package, and the NetC that was extracted from structures using Biopython package 103 .…”

Section: Methodsmentioning

confidence: 99%

Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space

Oliveira

Santana

Josino

et al. 2021

Sci Rep

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Cell-penetrating peptides (CPPs) are naturally able to cross the lipid bilayer membrane that protects cells. These peptides share common structural and physicochemical properties and show different pharmaceutical applications, among which drug delivery is the most important. Due to their ability to cross the membranes by pulling high-molecular-weight polar molecules, they are termed Trojan horses. In this study, we proposed a machine learning (ML)-based framework named BChemRF-CPPred (beyondchemicalrules-basedframework forCPP prediction) that uses an artificial neural network, a support vector machine, and a Gaussian process classifier to differentiate CPPs from non-CPPs, using structure- and sequence-based descriptors extracted from PDB and FASTA formats. The performance of our algorithm was evaluated by tenfold cross-validation and compared with those of previously reported prediction tools using an independent dataset. The BChemRF-CPPred satisfactorily identified CPP-like structures using natural and synthetic modified peptide libraries and also obtained better performance than those of previously reported ML-based algorithms, reaching the independent test accuracy of 90.66% (AUC = 0.9365) for PDB, and an accuracy of 86.5% (AUC = 0.9216) for FASTA input. Moreover, our analyses of the CPP chemical space demonstrated that these peptides break some molecular rules related to the prediction of permeability of therapeutic molecules in cell membranes. This is the first comprehensive analysis to predict synthetic and natural CPP structures and to evaluate their chemical space using an ML-based framework. Our algorithm is freely available for academic use at http://comptools.linc.ufpa.br/BChemRF-CPPred.

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“…One thousand one hundred and fourteen FDA-approved, non-nutraceutical drugs were collected from Drugbank 4.0 [55] and a reference dataset of 294 ligands with biological activity on CES1 (documented Ki) was gathered from Chembl [56]. Conformers for those molecules were either retrieved through PubChem [57], returning the experimental structure conformer if available, or generated using the open source cheminformatics library, RDKit [58].…”

Section: Methodsmentioning

confidence: 99%

Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1

et al. 2019

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The human carboxylesterase 1 (CES1), responsible for the biotransformation of many diverse therapeutic agents, may contribute to the occurrence of adverse drug reactions and therapeutic failure through drug interactions. The present study is designed to address the issue of potential drug interactions resulting from the inhibition of CES1. Based on an ensemble of 10 crystal structures complexed with different ligands and a set of 294 known CES1 ligands, we used docking (Autodock Vina) and machine learning methodologies (LDA, QDA and multilayer perceptron), considering the different energy terms from the scoring function to assess the best combination to enable the identification of CES1 inhibitors. The protocol was then applied on a library of 1114 FDA-approved drugs and eight drugs were selected for in vitro CES1 inhibition. An inhibition effect was observed for diltiazem (IC50 = 13.9 µM). Three others drugs (benztropine, iloprost and treprostinil), exhibited a weak CES1 inhibitory effects with IC50 values of 298.2 µM, 366.8 µM and 391.6 µM respectively. In conclusion, the binding site of CES1 is relatively flexible and can adapt its conformation to different types of ligands. Combining ensemble docking and machine learning approaches improves the prediction of CES1 inhibitors compared to a docking study using only one crystal structure.

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PySpark and RDKit: Moving towards Big Data in Cheminformatics

Cited by 75 publications

References 10 publications

Parasitic resistance as a predictor of faulty anodes in electro galvanizing: a comparison of machine learning, physical and hybrid models

Parasitic resistance as a predictor of faulty anodes in electro galvanizing: a comparison of machine learning, physical and hybrid models

Predicting cell-penetrating peptides using machine learning algorithms and navigating in their chemical space

Combined Ensemble Docking and Machine Learning in Identification of Therapeutic Agents with Potential Inhibitory Effect on Human CES1

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