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Quantitative estimate of druglikeness

As explained in Graph Neural Networks in Action

QED, or quantitative estimate of drug-likeness, is a score estimating how closely a molecule's properties resemble those of known drugs.

QED is a score estimating how closely a molecule’s properties resemble those of known drugs. It helps prioritize candidate molecules before synthesis and biological testing, where evaluating every possible structure is impractical. section 5.4.1 section 5.4.2

A practical QED workflow connects molecular representations, graph learning, and property optimization:

  • Represent molecules as SMILES and convert them into molecular graphs with atoms, bonds, and heavy-atom counts. section 5.4.1
  • Compare structures with different QED values; examples include molecules near 0.95 and others near 0.12. section 5.4.2
  • Train a graph autoencoder or GNN to predict QED from molecular graphs and use the learned latent-space gradient to search for candidates. section 5.4.2

How Graph Neural Networks in Action covers it

How deep it goes
Defines QED, compares high and low values, and implements prediction and optimization without deriving the score itself.
Examples
Uses ZINC molecules, property tables, molecular-graph figures, and examples contrasting approximately 0.95 and 0.12 QED.
What it assumes
Builds from SMILES and molecular graphs, showing their conversion with RDKit rather than requiring a stated chemistry prerequisite.
What you can do after
Apply via PyTorch Geometric and RDKit by converting SMILES to graphs, predicting QED, and optimizing generated candidates.
How it connects
Connects QED with molecular graphs, SMILES, graph neural networks, graph autoencoders, latent spaces, and drug discovery.
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This explainer was generated by Manning's knowledge graph from the text of Graph Neural Networks in Action. The section links point to the parts of the book it draws on.