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The floor for any drug-response model is predicting each drug's average effect across cell lines; tuned gradient-boosted trees such as LightGBM often tie or beat deep models under honest splits, which is why evaluation frameworks now exist.
LightGBM is Microsoft's free gradient-boosting framework based on decision trees (Wikipedia) and a common strong baseline. DrEval (Bernett and colleagues) and the IMPROVE project from Argonne and the NCI (with models such as UNO and GraphDRP) benchmark deep drug-response models against naive baselines under fixed protocols; the naive mean-drug predictor is hard to beat because most variance in a screen is between drugs, not between cell lines. Transformer models such as DeepTTA (Jiang and colleagues) report headline correlations that fall under leave-cell-line-out and cross-study splits.
Shares Spearman rank correlation, Ridge regression and the multilayer perceptron, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Benchmarks, leaderboards and contamination, Drug-response data splits: leave-cell-line-out, leave-drug-out, leave-tissue-out, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Spearman rank correlation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Ridge regression and the multilayer perceptron, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Drug-response data splits: leave-cell-line-out, leave-drug-out, leave-tissue-out, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Ridge regression and the multilayer perceptron, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Benchmarks, leaderboards and contamination, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Benchmarks, leaderboards and contamination, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.