Active learning for computational chemogenomics

This free research article brought to MedChemNet users by our sister journal Future Medicinal Chemistry, discusses the development of an active learning computational chemogenomic model that may assist in improving the rate of drug discovery.

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Mar 14, 2017

In this free research article, the collaborative research efforts of Daniel Reker, Petra Schneider, Gisbert Schneider (all Swiss Federal Institute of Technology, Zurich, Switzerland) and J.B. Brown (Kyoto University Graduate School of Medicine, Kyoto, Japan) explore the development of an active learning computational chemogenomic model for drug discovery.

The research team's models display high predictive performance on datasets many folds larger than those utilized for model construction, leading to the implication that chemogenomic active learning might actually be able to computationally identify the most beneficial assays for subsequent execution and evaluation. These results indicate that it could serve as a platform to iteratively include the experimental results in an actively updating model, which consequently would lead to making strides in improving discovery rates and reducing screening costs.

Read the full article here:

Reker D, Schneider P, Schneider G, Brown JB. Active learning for computational chemogenomics. Future Med. Chem. doi:10.4155/fmc-2016-0197 (Epub ahead of print) (2017)

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