Raman Spectroscopy Using a Multiclass Extension of Fisher-Based Feature Selection Support Vector Machines (FFS-SVM) for Characterizing In-Vitro Apoptotic Cell Death Induced by Paclitaxel

Fenn, Michael B.; Guarracino, Mario Rosario; Pi, Jiaxing; Pardalos, Pãnos M.

doi:10.1007/978-3-319-09584-4_27

Cited by 1 publication

(1 citation statement)

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“…Currently, there are two common types of approaches. One approach is based on partitioning strategies (including one-versus-one, one-versus-rest, rest-versus-one, − one-versus-one-versus-rest, and other strategies), while the other approach is based on reformulating the construction of the multiclass SVM classifer by solving larger optimization problems . Readers who are interested in a comprehensive discussion of the SVM methods are referred to the literature. , …”

Section: How Artificial Intelligence Workmentioning

confidence: 99%

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

et al. 2019

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Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

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