Semantic Scholar Open Access 2015 672 sitasi

Machine-learning approaches in drug discovery: methods and applications.

A. Lavecchia

Abstrak

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. Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS). In addition, I analyze several relevant VS studies from recent publications, 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 advances.

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A. Lavecchia

Format Sitasi

Lavecchia, A. (2015). Machine-learning approaches in drug discovery: methods and applications.. https://doi.org/10.1016/j.drudis.2014.10.012

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Informasi Jurnal
Tahun Terbit
2015
Bahasa
en
Total Sitasi
672×
Sumber Database
Semantic Scholar
DOI
10.1016/j.drudis.2014.10.012
Akses
Open Access ✓