Abstract
Machine learning (ML) is a field of computer science that allows interrogation to allow modified navigation (learning) of the data and through statistical derivation prediction of unseen data or events. ML has been a high-profile topic for many years and is ubiquitous in many aspects of daily life – from e-mail spam and malware filtering to search results refining online customer service and fraud detection. More recently, ML has been pervasive in solving complex nonlinear phenomena in pharmaceutical and medical sciences. It has been used in modeling chemical data sets for two decades. It has only recently become a useful approach to improve healthcare diagnoses and to provide personalized medical treatments. The rapid growth in data collection and integration, as well as the accessibility of increasing computing power, especially in cloud services, explains this unforeseen capacity to transform data into information, information into knowledge, and knowledge into wisdom (see Fig. 7.1). In this section, we briefly introduce the concepts and types of ML and its application for drug discovery, drug product development, and clinical application. The literature in these fields and the importance and challenges of interpreting ML results are also discussed.
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Hickey, A.J., Smyth, H.D.C. (2020). Computational Modeling of Nonlinear Phenomena Using Machine Learning. In: Pharmaco-complexity. AAPS Introductions in the Pharmaceutical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-42783-2_7
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