Exploring Molecular Descriptors and Fingerprints to Predict mTOR Kinase Inhibitors using Machine Learning Techniques

2020 ◽  
pp. 1-1
Author(s):  
Chetna Kumari ◽  
Muhammad Abulaish ◽  
Naidu Subbarao
Keyword(s):  
Machine Learning ◽  
Kinase Inhibitors ◽  
Molecular Descriptors ◽  
Machine Learning Techniques ◽  
Mtor Kinase ◽  
Learning Techniques

Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

2012 ◽  
pp. 155-165
Author(s):  
S. Prasanthi ◽  
S.Durga Bhavani ◽  
T. Sobha Rani ◽  
Raju S. Bapi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Learning Techniques

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques

2020 ◽  
Vol 16 (4) ◽  
pp. 407-419
Author(s):  
Aytun Onay ◽  
Melih Onay
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Support ◽  
Support System ◽  
Molecular Descriptors ◽  
Machine Learning Techniques ◽  
Drug Candidate ◽  
Drug Molecules ◽  
Learning Techniques

Background: Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones. Introduction: Classification models developed in this study can be used as a simple filter in drug modelling to eliminate potentially inappropriate molecules in the early stages. In this work, we developed a Drug Decision Support System (DDSS) to classify each drug candidate molecule as potentially drug or non-drug and to predict its disease group. Methods: Molecular descriptors were identified for the determination of a number of rules in drug molecules. They were derived using ADRIANA.Code program and Lipinski's rule of five. We used Artificial Neural Network (ANN) to classify drug molecules correctly according to the types of diseases. Closed frequent molecular structures in the form of subgraph fragments were also obtained with Gaston algorithm included in ParMol Package to find common molecular fragments for withdrawn drugs. Results: We observed that TPSA, XlogP Natoms, HDon_O and TPSA are the most distinctive features in the pool of the molecular descriptors and evaluated the performances of classifiers on all datasets and found that classification accuracies are very high on all the datasets. Neural network models achieved 84.6% and 83.3% accuracies on test sets including cardiac therapy, anti-epileptics and anti-parkinson drugs with approved and withdrawn drugs for drug classification problems. Conclusion: The experimental evaluation shows that the system is promising at determination of potential drug molecules to classify drug molecules correctly according to the types of diseases.

Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

2013 ◽  
pp. 937-947
Author(s):  
S. Prasanthi ◽  
S.Durga Bhavani ◽  
T. Sobha Rani ◽  
Raju S. Bapi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Learning Techniques

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

scholarly journals CryptoChem for Encoding and Storing Information Using Chemical Structures

2020 ◽  
Author(s):  
Phyo Phyo Zin ◽  
Xinhao Li ◽  
Dhoha TRIKI ◽  
Denis Fourches
Keyword(s):  
Machine Learning ◽  
Molecular Descriptors ◽  
Central Element ◽  
Machine Learning Techniques ◽  
Textual Information ◽  
Chemical Structures ◽  
Learning Techniques ◽  
Different Types ◽  
Initial Concept ◽  
Machine Learning Models

This study presents CryptoChem, a new method and associated software to securely store and transfer information using chemicals. Relying on the concept of Big Chemical Data, molecular descriptors and machine learning techniques, CryptoChem offers a highly complex and robust system with multiple layers of security for transmitting confidential information. This revolutionary technology adds fully untapped layers of complexity and is thus of relevance for different types of applications and users. The algorithm directly uses chemical structures and their properties as the central element of the secured storage. QSDR (Quantitative Structure-Data Relationship) models are used as private keys to encode and decode the data. Herein, we validate the software with a series of five datasets consisting of numerical and textual information with increasing size and complexity. We discuss <i>(i)</i> the initial concept and current features of CryptoChem, <i>(ii)</i> the associated Molread and Molwrite programs which encode messages as series of molecules and decodes them with an ensemble of QSDR machine learning models, <i>(iii)</i> the Analogue Retriever and Label Swapper methods, which enforce additional layers of security, <i>(iv)</i> the results of encoding and decoding the five datasets using CryptoChem, and (v) the comparison of CryptoChem to contemporary encryption methods. CryptoChem is freely available for testing at <a href="https://github.com/XinhaoLi74/CryptoChem">https://github.com/XinhaoLi74/CryptoChem</a>

scholarly journals A Chemoinformatic Exploration of the Chemical Space of Natural Steroids.

2020 ◽  
Author(s):  
Marcelo Otero ◽  
Silvina Sarno ◽  
Sofía Acebedo ◽  
Javier Alberto Ramirez
Keyword(s):  
Machine Learning ◽  
Molecular Descriptors ◽  
Chemical Space ◽  
Machine Learning Techniques ◽  
Potential Contribution ◽  
Biological Functions ◽  
Metabolic Evolution ◽  
Computational Approaches ◽  
Large Sets ◽  
Learning Techniques

Chemoinformatic tools have been widely used to analyze the properties of large sets of natural compounds, mostly in the context of drug discovery. Nevertheless, fewer reports have aimed to answer basic biological questions. In this work, we have applied unsupervised machine learning techniques to assess the diversity and complexity of a set of natural steroids by characterizing them through simple topological and physicochemical molecular descriptors. As a most noteworthy result, these properties, derived from the molecular graphs of the compounds, are closely related to their biological functions and to their biosynthetic origins. Moreover, a trend paralleling diversification of the properties and metabolic evolution can be established, demonstrating the potential contribution of these computational approaches to better understanding the vast wealth of natural products.

scholarly journals CryptoChem for Encoding and Storing Information Using Chemical Structures

2020 ◽  
Author(s):  
Phyo Phyo Zin ◽  
Xinhao Li ◽  
Dhoha TRIKI ◽  
Denis Fourches
Keyword(s):  
Machine Learning ◽  
Molecular Descriptors ◽  
Central Element ◽  
Machine Learning Techniques ◽  
Textual Information ◽  
Chemical Structures ◽  
Learning Techniques ◽  
Different Types ◽  
Initial Concept ◽  
Machine Learning Models

This study presents CryptoChem, a new method and associated software to securely store and transfer information using chemicals. Relying on the concept of Big Chemical Data, molecular descriptors and machine learning techniques, CryptoChem offers a highly complex and robust system with multiple layers of security for transmitting confidential information. This revolutionary technology adds fully untapped layers of complexity and is thus of relevance for different types of applications and users. The algorithm directly uses chemical structures and their properties as the central element of the secured storage. QSDR (Quantitative Structure-Data Relationship) models are used as private keys to encode and decode the data. Herein, we validate the software with a series of five datasets consisting of numerical and textual information with increasing size and complexity. We discuss <i>(i)</i> the initial concept and current features of CryptoChem, <i>(ii)</i> the associated Molread and Molwrite programs which encode messages as series of molecules and decodes them with an ensemble of QSDR machine learning models, <i>(iii)</i> the Analogue Retriever and Label Swapper methods, which enforce additional layers of security, <i>(iv)</i> the results of encoding and decoding the five datasets using CryptoChem, and (v) the comparison of CryptoChem to contemporary encryption methods. CryptoChem is freely available for testing at <a href="https://github.com/XinhaoLi74/CryptoChem">https://github.com/XinhaoLi74/CryptoChem</a>

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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