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 |