scholarly journals Novel Development of Predictive Feature Fingerprints to Identify Chemistry-Based Features for the Effective Drug Design of SARS-CoV-2 Target Antagonists and Inhibitors Using Machine Learning

ACS Omega ◽  
2021 ◽  
Vol 6 (7) ◽  
pp. 4857-4877
Author(s):  
Kelvin Cooper ◽  
Christopher Baddeley ◽  
Bernie French ◽  
Katherine Gibson ◽  
James Golden ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Effective Drug ◽  
Antagonists And Inhibitors ◽  
Predictive Feature

Novel Next-Generation Sequencing and Networks-Based Therapeutic Targets: Realistic and More Effective Drug Design and Discovery

2014 ◽  
Vol 20 (1) ◽  
pp. 11-22 ◽  
Author(s):  
Dimitrios Roukos ◽  
Giannis Baltogiannis ◽  
Christos Katsouras ◽  
Aris Bechlioulis ◽  
Katerina Naka ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Drug Design ◽  
Therapeutic Targets ◽  
Effective Drug ◽  
Next Generation ◽  
Generation Sequencing

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

scholarly journals The comparison of machine learning methods for prediction study of type 2 diabetes mellitus’s drug design

2020 ◽  
Author(s):  
Nadya Asanul Husna ◽  
Alhadi Bustamam ◽  
Arry Yanuar ◽  
Devvi Sarwinda ◽  
Oky Hermansyah
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Drug Design ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals A review of mathematical representations of biomolecular data

2020 ◽  
Vol 22 (8) ◽  
pp. 4343-4367 ◽  
Author(s):  
Duc Duy Nguyen ◽  
Zixuan Cang ◽  
Guo-Wei Wei
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Computational Biology ◽  
Structure Prediction ◽  
Critical Assessment ◽  
Drug Design Data Resource ◽  
Design Data ◽  
Data Resource ◽  
Mathematical Representations ◽  
Grand Challenges

Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges.

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