| CADD | Computer-aided drug design |
| QSAR | Quantitative structure–activity relationships |
| NMR | Nuclear magnetic resonance |
| DNDD | De novo drug design |
| MCSS | Multiple copy simultaneous search |
| ChEMBL | Chemical database of bioactive molecules with drug-like properties |
| ADMET | Absorption, distribution, metabolism, excretion, and toxicity |
| AI | Artificial intelligence |
| ML | Machine learning |
| DL | Deep learning |
| RNN | Recurrent neural networks |
| CNN | Convolutional neural networks |
| GAN | Generative adversarial networks |
| AE | Autoencoders |
| RL | Reinforcement learning |
| DRL | Deep reinforcement learning |
| SMILES | Simplified molecular-input line-entry system |
| ReLeaSE | Reinforcement learning for structural evolution |
| TL | Transfer learning |
| LSTM | Long short-term memory |
| nll | |
| 2D | Two-dimensional |
| DNN | Deep neural network |
| RANC | Reinforced adversarial neural computer |
| ATNC | Adversarial threshold neural computer |
| IDC | Internal diversity clustering |
| VAE | Variational autoencoder |
| 3D | Three-dimensional |
| MW | Molecular weight |
| LogP | Octanol-water partition coefficient |
| HBD | Hydrogen-bond donor |
| HBA | Hydrogen-bond acceptor |
| TPSA | Topological polar surface area |
| seq2seq AE | Sequence to sequence autoencoder |
| GRU | Gated recurrent unit |
| AAE | Adversarial autoencoder |
| PSO | Particle swarm optimization |
| OECD | Organization’s for the Economic Cooperation and Development |
| SA | Synthetic accessibility |
| SC | Synthetic complexity |
| MOA | Mechanism-of-action |
| COVID-19 | Coronavirus disease 2019 |
| SARS-CoV-2 | Severe acute respiratory syndrome coronavirus 2 |
| Mpro | Main protease |
| ACE-2 | Angiotensin II |
| WGAN | Wasserstein GAN |
| US FDA | United States food and drug administration |
| GCGR | Glucagon receptor |
| EMA | European medicines agency |
| HMA | Heads of medical agencies |
| QMRF | QSAR model report format |
| DDR1 | Discoidin domain receptor 1 |