Input: DDI network A | |
The parameters: learning rate, epochs, dropout, batch-size, input-dim, hidden-dim, output-dim (both in Feature extractor and Predictor) | |
Output: DDI network \( \hat{\mathrm{A}} \) reconstructed by DPDDI | |
1: Initialize parameter sets W(0) and W(1) in Feature extractor. | |
2: Learn drug representations Z. | |
3: for epoch in epochs (Feature extractor in Table 1.): | |
4: Compute the loss function based on Eq. 2. | |
5: Calculate gradient and adopt Adam optimizer to update W(0) and W(1). | |
6: end for | |
7: Obtain the representations Z of drugs according to Eq. 4 and Eq. 5. | |
8: for each drug pair, do | |
9: Feature aggregation by concatenating operation. | |
10: end for | |
11: Initialize parameter sets in Predictor based on DNN. | |
12: Feed representation vector of each drug pair into Predictor. | |
13: for epoch in epochs (Predictor in Table 1.): | |
14: Compute the loss function based on Eq. 3. | |
15: Calculate gradient and adopt Adam optimizer to update parameter sets . | |
16: end for | |
17: Obtain the DDI network \( \hat{\mathrm{A}} \). |