Drug Design with Machine Learning

2011 ◽  
Keyword(s):  
Machine Learning ◽  
Drug Design

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.

scholarly journals Less may be more: an informed reflection on molecular descriptors for drug design and discovery

2020 ◽  
Vol 5 (1) ◽  
pp. 317-329 ◽  
Author(s):  
Trent Barnard ◽  
Harry Hagan ◽  
Steven Tseng ◽  
Gabriele C. Sosso
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Molecular Descriptors ◽  
Physical Intuition

The phenomenal advances of machine learning in the context of drug design have led to the development of a plethora of molecular descriptors. And yet, there might be value in using just a handful of them – inspired by our physical intuition.

Recent Trends in Drug Design and Discovery

2020 ◽  
Vol 20 (19) ◽  
pp. 1761-1770
Author(s):  
Devadasan Velmurugan ◽  
R. Pachaiappan ◽  
Chandrasekaran Ramakrishnan
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Molecular Modeling ◽  
Drug Design ◽  
Computational Methods ◽  
Data Analytics ◽  
Drug Targets ◽  
Structural Data ◽  
Target Protein ◽  
Structure Based Drug Design

Introduction: Structure-based drug design is a wide area of identification of selective inhibitors of a target of interest. From the time of the availability of three dimensional structure of the drug targets, mostly the proteins, many computational methods had emerged to address the challenges associated with drug design process. Particularly, drug-likeness, druggability of the target protein, specificity, off-target binding, etc., are the important factors to determine the efficacy of new chemical inhibitors. Objective: The aim of the present research was to improve the drug design strategies in field of design of novel inhibitors with respect to specific target protein in disease pathology. Recent statistical machine learning methods applied for structural and chemical data analysis had been elaborated in current drug design field. Methods: As the size of the biological data shows a continuous growth, new computational algorithms and analytical methods are being developed with different objectives. It covers a wide area, from protein structure prediction to drug toxicity prediction. Moreover, the computational methods are available to analyze the structural data of varying types and sizes of which, most of the semi-empirical force field and quantum mechanics based molecular modeling methods showed a proven accuracy towards analysing small structural data sets while statistics based methods such as machine learning, QSAR and other specific data analytics methods are robust for large scale data analysis. Results: In this present study, the background has been reviewed for new drug lead development with respect specific drug targets of interest. Overall approach of both the extreme methods were also used to demonstrate with the plausible outcome. Conclusion: In this chapter, we focus on the recent developments in the structure-based drug design using advanced molecular modeling techniques in conjunction with machine learning and other data analytics methods. Natural products based drug discovery is also discussed.

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