Table 2. Optimized Hyperparameters for Training of QSAR Neural Networksa.
QSAR model parameters |
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Data set | Layers | Nodes per layer | Activation | Learning rate |
AutoDock Vina | 3 | 256, 256, 32 | ReLU, Sigmoid | 1 × 10–3 |
CNS MPO | 3 | 256, 256, 32 | ReLU6 | 1 × 10–3 |
Aggregation | 4 | 128, 128, 128, 32 | ReLU, Sigmoid | 5 × 10–4 |
Models were trained for 1000 epochs.