Machine-learning approaches in drug discovery: methods and applications

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This paper focuses on machine learning approaches in the context of ligand-based virtual screening for addressing complex compound classification problems and predicting new active molecules.

During the past decade, virtual screening (VS) has evolved from traditional
similarity searching, which utilizes single reference compounds, into an
advanced application domain for data mining and machine-learning
approaches, which require large and representative training-set
compounds to learn robust decision rules. The explosive growth in the
amount of public domain-available chemical and biological data has
generated huge effort to design, analyze, and apply novel learning
methodologies. This article focuses on machine-learning techniques within the
context of ligand-based VS (LBVS). In addition,  several relevant
VS studies from recent publications are analyzed, providing a detailed view of the
current state-of-the-art in this field and highlighting not only the
problematic issues, but also the successes and opportunities for further


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