TracIn was implemented by adapting the code from Pruthi et al. TracIn is applied to a FNN implemented using tensorflow. It featured three dense hidden layers with 512 neurons each. Each dense layer employs the Exponential Linear Unit (ELU) activation function, and dropout layers with a rate of 0.5 follow each dense layer. The output layer, using a sigmoid activation function, yields binary classification outputs. The Adam optimizer with a learning rate of 0.001 was used for model training, and binary cross-entropy was applied as a loss function. A one-hidden-layer TensorFlow LSTM model was used instead when using SMILES strings as input. Its input layer is a 64-neuron LSTM layer with a dropout of 0.2. the hidden layer has 512 neurons and uses the ELU activation function, followed by a dropout layer of rate 0.5. The output layer and the training parameters are equivalent to the previously mentioned FNN. Self influence was implemented by squaring the loss gradients resulting from the change in the last two layers' weights of the model checkpoint at 30, 60, and 90 epochs during training and summing over all 3 checkpoints. Test-influence was Dataimplemented by multiplying the loss gradients with all validation set gradients, averaging over all validation samples for each training sample and summing over all three checkpoints. MVS-A was implemented using the MVS-A codebase. MVS-A uses an LGBMClassifier with 100 boosting iterations, lambda regularization of 1.0, column subsampling of 0.95, and a seed according to the current replicate number. The model was trained on the training set, and the self-influence scores were calculated using the following formula: