Table 1. Validation Results of the Different Machine Learning Models of Dissociation Constant Rate (log(k_off)) Prediction^a.

structure	feature extraction	ML model	R2	MAE
2D structure (SMILES)	AutoQSAR	KPLS	0.80	0.32
	ECFP6	XGBoost	0.777 ± 0.027	0.359 ± 0.026
		SVR	0.914 ± 0.094	0.174 ± 0.104
3D structure	Field-Based QSAR	PLS	0.74	0.41
	PaDEL_3D	XGBoost	0.875 ± 0.101	0.200 ± 0.098
		SVR	0.825 ± 0.075	0.305 ± 0.072
complex structure	Distance Shell Feature Extraction (DSFE)	XGBoost	0.812 ± 0.093	0.298 ± 0.072
		SVR	0.933 ± 0.065	0.182 ± 0.078

^aR² is the coefficient of determination on the test set, and MAE is the mean absolute error between the observed and predicted values. The mean ± SD value was calculated by randomly splitting the data set 10 times. The AutoQSAR and Field-Based QSAR models were constructed using Maestro software (Schrödinger, 2023), and only the best-performing model was selected; thus, there is no standard deviation (SD).