Why a human expert must be in the loop for AI to boost chemistry

By SKYSEVEN Chemical Technical Team · 2026-07-31

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We warn teenagers that their mistakes and lapses in judgment can follow them forever because of the internet. Somebody should have told AI. Chemical errors made by generative artificial intelligence are commonplace and have a habit of sticking around just as long as a photo of a bad haircut or an ill-considered remark, and they can be as laughably bad or even offensive. But there are good reasons to take a second look at how AI handles molecular structures. And it might be time to wipe the record clean and give AI a chance for a fresh start.

One way to ensure that AI continues to enhance is to chemists to stay engaged in what is happening in that fast-moving area. If chemists do not work to make AI better, the tools will not enhance as fast as they could. There are signs that the AI companies are starting to pay greater attention to science, such as the recent creation of science-specific models, including OpenAI’s GPT-Rosalind, Amazon Bio Discovery, and Nvidia BioNeMo. Anthropic’s new Claude Science even incorporates a chemical drawing canvas that allows you to chat with your molecules (ChemIllusion, a company I founded, has offered this feature since its inception, and we recently open-sourced it so you can have this capability without having to trust an AI company with your lab data).

Many chemists might not see an advantage to using AI to workflows they learned to do without AI a long time ago. however the next generation of chemists will expect greater. They will rely on AI, sometimes too much, and they will expect AI to be able to help them. New chemistry students will expect an AI tool that can explain anything about any molecule they can imagine—not just the examples from Wikipedia that are currently covered well in the training data of existing models.

Designing new drugs is one process that AI tools could help accelerate. There are greater possible drug-like molecules than there are atoms in the solar system, however AI models can stumble on tasks that involve molecules that are not already described in their training data. AI models could handle molecules outside of their training data however only if they are given the ability to execute other programs (called tool-calling capabilities) and to access external databases.

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