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GPU-accelerated machine learning techniques enable QSAR modeling of large HTS data
2012
2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
Quantitative structure activity relationship (QSAR) modeling using high-throughput screening (HTS) data is a powerful technique which enables the construction of predictive models. These models are utilized for the in silico screening of libraries of molecules for which experimental screening methods are both cost-and time-expensive. Machine learning techniques excel in QSAR modeling where the relationship between structure and activity is often complex and non-linear. As these HTS data sets
doi:10.1109/cibcb.2012.6217246
dblp:conf/cibcb/LoweBWM12
fatcat:wvn6gdiuijcmpgcv5k2ubo4myi