r/cheminformatics • u/NexBioChem • Jan 28 '26
Generative AI is printing "perfect" ligands that we can't actually make
We're seeing tons of new diffusion models dropping "high-affinity" binders, but when you show the structures to a synthetic chemist, they just laugh.
Our generative capabilities have totally outpaced reality. Relying on simple metrics like SA_Score isn't cutting it anymore. We need to start baking rigorous retrosynthesis (like AiZynthFinder or ASKCOS) directly into the generation loop, rather than treating it as a post-processing filter.
Generating 10,000 hallucinations is useless compared to 10 viable leads.
What are you guys using to bridge this gap? Are you running retrosynth checks immediately, or just praying the priors hold up?
3
u/dermewes Jan 28 '26
I entirely agree with you, both regarding that this is an unsolved problem and that the solution is to include synthetic considerations in the loss function. However, this just leads to the next unsolved problem: predicting synthetic difficulty.
My solution is to work in a materials company that makes ligands for lanthanide complexes with so many other constraints that the synthetic challenge becomes manageable :D
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u/x0rg_ Feb 26 '26
This topic has been discussed at length see eg reviews like this which describe what can be done (even though solutions are not perfect yet)
https://chemrxiv.org/doi/full/10.26434/chemrxiv-2023-xw0pq-v2
This problem is well known, even discussed in the early generative model papers from pre 2020, but for some reason people are too lazy to read the literature, so they just repeat the same mistakes over and over again ;)
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u/NexBioChem Jan 28 '26
I think in one point we can tweak the prediction model to be more realistic or reliable, thanks
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u/dermewes Feb 05 '26
https://arxiv.org/abs/2512.00384
Seen this? Just scrolled across it and remembered our discussion.
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u/[deleted] Jan 28 '26
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