Abstract
Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics.
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.
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.
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.
Keywords: Machine learning (ML), anticancer drugs, linear discriminant analysis (LDA), principal components analysis (PCA), support vector machine (SVM), random forest (RF), k-nearest neighbor (kNN), naïve bayes (NB), deep learning, web servers.
Graphical Abstract
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Call for Papers in Thematic Issues
09 June, 2024
New drug therapy for eye diseases
Eyesight is one of the most critical senses, accounting for over 80% of our perceptions. Our quality of life might be significantly affected by eye disease, including glaucoma, diabetic retinopathy, dry eye, etc. Although the development of microinvasive ocular surgery reduces surgical complications and improves overall outcomes, medication therapy is ...read more
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