Classification of HIV-1 Protease Inhibitors by Machine Learning Methods

Accurate predictions of the coal temperature in coal spontaneous combustion (CSC) are important for ensuring coal mine safety. Gas coal (the Zhaolou coal mine in Shandong Province, China) was used in this paper. A large CSC experimental device was adopted to obtain its characteristic temperatures from the macroscopic characteristics of gas production. A simulated annealing-support vector machine (SA-SVM) prediction model was proposed to reflect the complex nonlinear mapping between characteristic gases and the coal temperature. The risk degree of CSC was estimated in the time domain, and the model was verified by using in situ data from an actual working face. Furthermore, back-propagation neural network (BPNN) and single SVM methods were adopted for comparison. The results showed that the BPNN could not adapt to the small-sample problem due to overfitting and the output of a single SVM was unstable due to its strong dependence on the setting of hyperparameters. Through the SA global optimization process, the optimal combination of hyperparameters was obtained. Therefore, SA-SVM had higher prediction accuracy, robustness, and error tolerance rate and better environmental adaptability. These findings have certain practical significances for eliminating the hidden danger of CSC in the gob and providing timely warnings about potential danger.

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“…A regression model analysis of a single SVM and a BPNN , was also performed. The BPNN model adopted a single hidden layer.…”

Section: Resultsmentioning

confidence: 99%

Prediction Model for Coal Spontaneous Combustion Based on SA-SVM

et al. 2021

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“…This step was terminated till all the top‐ranking descriptors were iterated. (3) The recursive feature elimination based on a decision tree classifier with ten‐fold CV was performed [9b,13] . Via the above‐mentioned procedures, 27 Mordred descriptors were selected (cf.…”

Section: Methodsmentioning

confidence: 99%

HDAC3i‐Finder: A Machine Learning‐based Computational Tool to Screen for HDAC3 Inhibitors

Ding

Chen

et al. 2020

Molecular Informatics

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Histone deacetylase 3 (HDAC3) is a potential drug target for treatment of human diseases such as cancer, chronic inflammation, neurodegenerative diseases and diabetes. Machine learning (ML) as an essential cheminformatics approach has been widely used for QSAR modeling. However, none of them has been applied to HDAC3. To this end, we carefully compiled a set of 1098 compounds from the ChEMBL database that have been assayed against HDAC3 and calculated three different sets of molecular features for each compound, i. e. two‐dimensional Mordred descriptors, MACCS keys (166 bits) and Morgan2 fingerprints (1024 bits). Five ML classifiers, i. e. k‐Nearest Neighbour (KNN), Support Vector Machine (SVM), Random forest (RF), eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) were trained on each feature set and optimized for classification. A total of 15 models were generated and carefully compared, among which the best‐performing one was the XGBoost model based on the Morgan2 fingerprints, i. e. XGBoost_morgan2. Evaluated on a well‐curated benchmarking set named MUBD‐HDAC3, this model achieved a high early ROC enrichment (ROCE0.5 %: 41.02). A further retrospective screening of an annotated chemical library in PubChem demonstrated that the best model could identify 8 novel‐scaffold HDAC3 inhibitors while assaying only 1 % of the compounds. To make this model accessible for the scientific community, we developed a python GUI application named HDAC3i‐Finder to facilitate prospective screening for HDAC3 inhibitors. The source code of HDAC3i‐Finder is available at https://github.com/jwxia2014/HDAC3i‐Finder.

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“…Yang Li et al classified HIV-1 Protease Inhibitors using Decision Tree. They applied DT model for 3 different descriptor data set and achieved training set accuracy as 86.00, 89.52 and 80.89 [5]. Huge data are generated from high throughput screening (HTS) and machine learning models are applied to analyse and build effective cheminformatics models for the screening of various diseases [6] parenteral and topical (PT) has been added to the dataset.…”

Section: N Priya G Shobanamentioning

confidence: 99%

Multivariate Classification of Drugs using Parametric and Nonparametric Machine Learning Models

Priya¹,

Shobana*²

2020

IJITEE

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In pharmaceutical research, traditional drug discovery process is time consuming and expensive, where several compounds are experimentally tested for their biological activities. Series of lab experiments are conducted to analyze newly synthesized drug’s pharmaceutical activities and its biological effects on human. With every new drug discovery, the required clinical properties can be determined using machine learning models and this greatly reduces the experimental cost. This paper explores parametric and non-parametric machine learning models to classify administration properties of drugs and its toxicity. The multinomial classification of drugs was based on their physicochemical and ADMET properties. Balanced data samples were drawn from chEMBL and was pre-processed. Features were reduced using Recursive Feature Elimination and the attributes were ranked based on their importance to reduce highly correlated attributes. The performance of parametric and non-parametric machine learning models was analyzed on cheminformatic data that includes physiochemical, biological and pharmaceutical properties of the drug molecules. Selecting the potent drug candidate along with its administration properties greatly reduces wet lab experimental time and cost. Multiclass classification can be determined efficiently using non-parametric machine learning model. Optimal feature engineering, tuning hyperparameters and adopting hybrid algorithms would result in more accurate predictions in future for cheminformatics data.

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Classification of HIV-1 Protease Inhibitors by Machine Learning Methods

Cited by 13 publications

References 29 publications

Prediction Model for Coal Spontaneous Combustion Based on SA-SVM

Prediction Model for Coal Spontaneous Combustion Based on SA-SVM

HDAC3i‐Finder: A Machine Learning‐based Computational Tool to Screen for HDAC3 Inhibitors

Multivariate Classification of Drugs using Parametric and Nonparametric Machine Learning Models

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