Reproduce “Comparative Strategies for Handling Imbalanced QSAR Regression: A Case Study on hERG Activity Prediction” on OpenResearch
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About this paper
Ming-Yen Sung, Hsin Liu, Tung-Sheng Cho, Cheng-Wei Lee, Yufeng J. Tseng
The authors predict hERG assay activity for QSAR screening, define response regions using assay and biological context, compare imbalance treatments, and select trained models by validation error in the target interval, [−100, −20). For a GCN trained without imbalance intervention, this yields test MAE 29.56 ± 1.37 versus 32.88 ± 2.43 under global selection; the improvement is not universal.
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