# Chemical classification program synthesis using generative artificial intelligence

arXiv: https://arxiv.org/abs/2505.18470 · HTML: https://mlsanity.com/paper/2505.18470/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 62 references that are in the map.
- First posted on arXiv 24 May 2025.

## Builds on (most cited first, 15 of 15)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Deep Reinforcement Learning for De-Novo Drug Design](https://mlsanity.com/paper/1711.10907/) (2017)
- [Calibrated Language Models Must Hallucinate](https://mlsanity.com/paper/2311.14648/) (2023)
- [CodeMirage: Hallucinations in Code Generated by Large Language Models](https://mlsanity.com/paper/2408.08333/) (2024)
- [ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models](https://mlsanity.com/paper/2409.13989/) (2024)
- [Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)](https://mlsanity.com/paper/2312.10904/) (2023)
- [A Simple Standard for Sharing Ontological Mappings (SSSOM)](https://mlsanity.com/paper/2112.07051/) (2021)
- [Gene Set Summarization using Large Language Models](https://mlsanity.com/paper/2305.13338/) (2023)
- [Ontology Development Kit: a toolkit for building, maintaining, and standardising biomedical ontologies](https://mlsanity.com/paper/2207.02056/) (2022)
- [CurateGPT: A flexible language-model assisted biocuration tool](https://mlsanity.com/paper/2411.00046/) (2024)
- [When one Logic is Not Enough: Integrating First-order Annotations in OWL Ontologies](https://mlsanity.com/paper/2210.03497/) (2022)

## Built on by (most cited first, 1 of 1)

- [HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification](https://mlsanity.com/paper/2609.37740/) (2026)

## Abstract (arXiv)

Accurately classifying chemical structures is essential for cheminformatics and bioinformatics, including tasks such as identifying bioactive compounds of interest, screening molecules for toxicity to humans, finding non-organic compounds with desirable material properties, or organizing large chemical libraries for drug discovery or environmental monitoring. However, manual classification is labor-intensive and difficult to scale to large chemical databases. Existing automated approaches either rely on manually constructed classification rules, or are deep learning methods that lack explainability. This work presents an approach that uses generative artificial intelligence to automatically write chemical classifier programs for classes in the Chemical Entities of Biological Interest (ChEBI) database. These programs can be used for efficient deterministic run-time classification of SMILES structures, with natural language explanations. The programs themselves constitute an explainable computable ontological model of chemical class nomenclature, which we call the ChEBI Chemical Class Program Ontology (C3PO). We validated our approach against the ChEBI database, and compared our results against deep learning models and a naive SMARTS pattern based classifier. C3PO outperforms the naive classifier, but does not reach the performance of state of the art deep learning methods. However, C3PO has a number of strengths that complement deep learning methods, including explainability and reduced data dependence. C3PO can be used alongside deep learning classifiers to provide an explanation of the classification, where both methods agree. The programs can be used as part of the ontology development process, and iteratively refined by expert human curators.