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Table 1 Performance of SVM models using three data options with continuous descriptors, fingerprints and fragment counts

From: Open-source QSAR models for pKa prediction using multiple machine learning approaches

Data option Data set Feature sets Number of features Train Fivefold CV Test
R2 RMSE Q2 RMSE R2 RMSE
1 Acidic Continuous 870 0.96 0.65 0.58 2.18 0.68 1.91
1 Acidic Fingerprints 1548 0.91 1.00 0.64 2.02 0.71 1.81
1 Acidic Fingerprints + counts 2104 0.94 0.80 0.64 2.02 0.72 1.80
1 Basic Continuous 876 0.96 0.64 0.65 1.94 0.65 1.93
1 Basic Fingerprints 1535 0.91 0.99 0.69 1.84 0.69 1.83
1 Basic Fingerprints + counts 2079 0.93 0.87 0.72 1.73 0.70 1.80
2 Acidic Continuous 913 0.98 0.49 0.61 2.10 0.69 1.89
2 Acidic Fingerprints 1552 0.9 1.05 0.63 2.04 0.69 1.87
2 Acidic Fingerprints + counts 2141 0.94 0.85 0.63 2.05 0.71 1.81
2 Basic Continuous 913 0.97 0.52 0.67 1.88 0.66 1.88
2 Basic Fingerprints 1534 0.90 1.02 0.68 1.83 0.75 1.63
2 Basic Fingerprints + counts 2085 0.93 0.88 0.71 1.76 0.78 1.53
3 Acidic Continuous 510 0.96 0.66 0.59 2.17 0.57 2.20
3 Acidic Fingerprint 1580 0.91 1.00 0.64 2.01 0.68 1.91
3 Acidic Fingerprints + counts 2395 0.93 0.86 0.65 1.99 0.69 1.87
3 Basic Continuous 510 0.95 0.75 0.61 2.01 0.6 2.09
3 Basic Fingerprints 1543 0.91 0.94 0.72 1.72 0.67 1.90
3 Basic Fingerprints + counts 2358 0.93 0.84 0.73 1.67 0.71 1.79