The Computational Methods for Anticancer Drug Development
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Drug Development".
Deadline for manuscript submissions: 25 September 2024 | Viewed by 496
Special Issue Editor
Special Issue Information
Dear Colleagues,
Computational methods, especially deep learning (DL) and other machine learning (ML) methods, have become increasingly popular in anticancer drug development in recent years. The availability of large-scale data sets as well as advances in new ML technologies enable us to apply ML methods to all stages of anticancer drug discovery and development, such as for guiding the high-throughput screening and optimization as well as identifying new drug targets, predicting treatment responses, identifying new biomarkers, and repositioning approved drugs. Although the accuracy of computational models has been greatly improved in recent years, we still face challenges on how to interpret most DL methods due to their complex “black-boxes” nature. Since most existing methods utilize preclinical disease models to make predictions and validate the results, very limited solutions currently exist that can make more clinically actionable predictions using different types of data, such as clinical data. Furthermore, user-friendly toolboxes are still lacking for non-computational cancer researchers, meaning powerful computational models are unable to be used in their research. In this Special Issue, we mainly focus on novel and explainable computational methods as well as easy-to-use toolboxes to accelerate the process of developing personalized anticancer treatments.
Dr. Liye He
Guest Editor
Manuscript Submission Information
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Keywords
- computational methods
- precision medicine
- drug repositioning
- deep learning
- drug combinations
- machine learning
- targeted therapy
- biomarkers
- drug screening
- computational toolboxes
- web-based applications
