Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category
Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-05-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fchem.2018.00162/full |
| _version_ | 1818900362801709056 |
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| author | Abraham Yosipof Rita C. Guedes Alfonso T. García-Sosa |
| author_facet | Abraham Yosipof Rita C. Guedes Alfonso T. García-Sosa |
| author_sort | Abraham Yosipof |
| collection | DOAJ |
| description | Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule. |
| first_indexed | 2024-12-19T20:02:40Z |
| format | Article |
| id | doaj.art-b7aef9ae6a694e588879a424258f5cf3 |
| institution | Directory Open Access Journal |
| issn | 2296-2646 |
| language | English |
| last_indexed | 2024-12-19T20:02:40Z |
| publishDate | 2018-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj.art-b7aef9ae6a694e588879a424258f5cf32022-12-21T20:07:37ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462018-05-01610.3389/fchem.2018.00162369120Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ CategoryAbraham Yosipof0Rita C. Guedes1Alfonso T. García-Sosa2Department of Information Systems and Department of Business Administration, College of Law & Business, Ramat-Gan, IsraelDepartment of Medicinal Chemistry, Faculty of Pharmacy, Research Institute for Medicines (iMed.ULisboa), Universidade de Lisboa, Lisbon, PortugalDepartment of Molecular Technology, Institute of Chemistry, University of Tartu, Tartu, EstoniaData mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.http://journal.frontiersin.org/article/10.3389/fchem.2018.00162/fullmachine-learningdrugdata-mininglogisticorgandrug design |
| spellingShingle | Abraham Yosipof Rita C. Guedes Alfonso T. García-Sosa Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category Frontiers in Chemistry machine-learning drug data-mining logistic organ drug design |
| title | Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category |
| title_full | Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category |
| title_fullStr | Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category |
| title_full_unstemmed | Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category |
| title_short | Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category |
| title_sort | data mining and machine learning models for predicting drug likeness and their disease or organ category |
| topic | machine-learning drug data-mining logistic organ drug design |
| url | http://journal.frontiersin.org/article/10.3389/fchem.2018.00162/full |
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