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AI-Assisted Modeling and Coding: The Overlooked Contribution of Generative AI to Chemistry

Didier Mathieu

Aug 2026 · AI Chemistry · 0 citations · 36 references

TL;DR

These tools can be used by chemists to test new ideas, make the most of existing open-source software and rapidly develop custom solutions that significantly ease day-to-day work.

Abstract

In the context of artificial intelligence in chemistry, the first applications that come to mind are widely discussed ones, such as building surrogate models to replace costly numerical simulations or designing new compounds. However, one area where generative AI truly stands out is programming assistance, ranging from code improvement suggestions to vibe coding simple programs from scratch. This article explores how these tools can be used by chemists to test new ideas, make the most of existing open-source software and rapidly develop custom solutions that significantly ease day-to-day work. Using the problem of estimating Hansen solubility parameters as an academic example, their strengths and limitations are illustrated through fully customizable sample codes, including a solubility parameter calculator, a molecular spreadsheet library and a molecular editor. In addition to serving as models, these codes could be of practical interest to some readers due to unique features making them address gaps in the current offerings.

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