I wish there was something like LEAN for biology.
I wish there was something like LEAN for biology. But there just isn't nearly enough data to build anything close to a good drug discovery simulator. To have a LEAN for drug discovery, we would need to simulate human physiology down to the molecular interactions at a reasonable accuracy. We currently have disparate datasets that allows us to make some predictions on how drugs will interact with molecules. And we have studies on how these molecules might affect our organs/bodies. But all of this data are at different resolutions, sparsity, and have different experimental context. It is extremely hard to reconcile for a holistic model. DNA is the great unifier in molecular biology as it is the digital anchor/foundation that everything above relies on. But there is just so much complexity above it. An analogy in the AI space would be model distillation where you are trying to discover all the weights in a LLM by giving it a prompt and seeing what the results are. With biology, we know the prompt (DNA sequence, genotype) and we can kind of quantify the result (phenotype). And we have to figure out all the weights in between (how DNA becomes a fully functioning organism).
Posted by daemonk on Oct 27, 2026, 11:30 PM
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This is my biggest problem with agents, they constantly defensively code for cases that have no chance of happening and in most cases a p...
NewI wish there was something like LEAN for biology.
NewThe issue is really the extreme difficulty of programming them, especially for anything sequential (rather than combinatorial).
I probably could have beared with it if it was only limited to programming, but it showing up in creative fields has made me feel all sor...
I hate that I'm in deep enough to know what you mean.
The biggest problem with “AI” is that big corps are shoving it down our throats.
in the middel of a coding session with 5.6 sol.
[Hero Run] 535B-A23B on 18T tokens
I wish there was something like LEAN for biology.
I wish there was something like LEAN for biology. But there just isn't nearly enough data to build anything close to a good drug discovery simulator. To have a LEAN for drug discovery, we would need to simulate human physiology down to the molecular interactions at a reasonable accuracy. We currently have disparate datasets that allows us to make some predictions on how drugs will interact with molecules. And we have studies on how these molecules might affect our organs/bodies. But all of this data are at different resolutions, sparsity, and have different experimental context. It is extremely hard to reconcile for a holistic model. DNA is the great unifier in molecular biology as it is the digital anchor/foundation that everything above relies on. But there is just so much complexity above it. An analogy in the AI space would be model distillation where you are trying to discover all the weights in a LLM by giving it a prompt and seeing what the results are. With biology, we know the prompt (DNA sequence, genotype) and we can kind of quantify the result (phenotype). And we have to figure out all the weights in between (how DNA becomes a fully functioning organism).
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