Drug discovery is the task of applying machine learning to discover new candidate drugs.
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And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning.
SOTA for Graph Regression on Lipophilicity (RMSE metric )
These data were trained on a deep learning model which was also integrated with the Attention mechanism to facilitate training and interpreting.
SOTA for Drug Discovery on egfr-inh
Machine learning methods may have the potential to significantly accelerate drug discovery.
Protein binding site comparison (pocket matching) is of importance in drug discovery.
Deep generative models such as generative adversarial networks, variational autoencoders, and autoregressive models are rapidly growing in popularity for the discovery of new molecules and materials.
We show that the predictive power of the generated models is comparable to that of Random Forest (RF) models and fully-connected Deep Neural Networks trained on circular (Morgan) fingerprints.
SMILES is a linear representation of chemical structures which encodes the connection table, and the stereochemistry of a molecule as a line of text with a grammar structure denoting atoms, bonds, rings and chains, and this information can be used to predict chemical properties.