Earlier this week, I presented on Wei Dei's work on ANNalog at ACS Fall 2026 in Chicago. ANNalog is a generative model that takes an input SMILES string and generates MedChem-similar molecules.
Among other things, I covered how we tweak the training set to encourage stereo to be retained, why training on aligned SMILES is important, and why we don't canonicalise the input.
I ended with some practical tips:
- Changes tend to occur on the RHS of the generated SMILES
- It follows that by presenting the input SMILES string in different ways, the user can selectively focus ANNalog’s attention on different parts of the molecule
- If changes should be distributed evenly, an option “-e variants” generates random SMILES variants and pools the results
- ANNalog allows the user to supply a fixed SMILES prefix to ensure that a particular portion of the molecule is unchanged
- Sampling can be used to create more diverse output;
- Feeding the result through more than once can increase diversity further (see option “-e recursive”)
- Not everything is gold
- chembl_gen_check results are appended to the output to flag unusual structures
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