| AAE | Adversarial autoencoder |
| AEs | Autoencoders |
| AMP | Antimicrobial peptide |
| ANNs | Artificial neural networks |
| CADD | Computer-aided drug design |
| CBP | CREB-binding protein |
| CNNs | Convolutional neural networks |
| D-MPNN | Direct-message-passing neural network |
| DB | Database |
| DDIs | Drug–Drug Interactions |
| DDR1 | Discoidin domain receptor1 |
| DDNs | Drug–Disease Networks |
| DSENs | Drug–Side effect Networks |
| DTIs | Drug–target interactions |
| FDA | Food and drug administration |
| GANs | Generative adversarial networks |
| GAT | Graph attention network |
| GCNs | Graph convolutional networks |
| GNN | Graph neural network |
| GPCR | G-protein coupled receptor |
| GPU | Graphical processing units |
| GRU | Gated recurrent units |
| GVAE | Grammar variational autoencoder |
| HTS | High-throughput screening |
| IC50 | Half-maximum inhibitory concentration |
| LSTM | Long short-term memory |
| MAE | Mean absolute error |
| MTDNN | Multitask deep neural network |
| NLP | Natural language processing |
| NNs | Neural networks |
| ORGAN | Objective-Reinforced Generative Adversarial Networks |
| ORGANIC | Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry |
| PPIs | Protein–Protein Interactions |
| PRISMA | Preferred reporting items for systematic review and meta-analyses |
| PUNs | Pretrained unsupervised networks |
| QSAR | Quantitative Structure–Activity Relationships |
| RANC | Reinforced adversarial neural computer |
| ReLU | Rectified linear unit |
| RL | Reinforcement Learning |
| RNNs | Recurrent neural networks |
| ROC-AUC | Receiver operating characteristic curve-area under curve |
| ROR-γt | Retinoic-acid-receptor related orphan receptor-gamma t |
| SMILES | Simplified molecular input line entry system |
| SOM | Self-organizing map |
| TL | Transfer Learning |
| TTD | Therapeutic Target DB |
| VAE | Variational autoencoder |
| WAE | Wasserstein autoencoder |