by: Trung Le, Track « Chemoinformatics and Materials Informatics », Bar Ilan - Strasbourg, 2025
With the rise of artificial intelligence (AI) models such as ChatGPT, DeepSeek, Mistral AI, or DALL-E, AI has growing importance in many applications, including chemistry. In chemistry, artificial intelligence is developed as a subject of chemoinformatics. Chemoinformatics and automation have already been associated with running complex chemical experiments for screening, synthesis, and other tasks. The paper by Boiko et al. [1] envisions a "CoScientist," a multiple large language model (LLM) based intelligent agent, to support chemists in designing and running chemical experiments.
The CoScientist browses the internet and relevant documentation, and uses application programming interfaces (APIs) to control robotic devices. The prototype uses a modular architecture (Figure 1). The main module, the “Planner”, orchestrates the actions of software processes (workers) able to search the web (GOOGLE), browse web pages (BROWSE), prototype scripts for controllers (PYTHON) with the help of relevant documentation (DOCUMENTATION), and finally, run the experiment on the hardware (EXPERIMENT).
The prototype has been assembled around a liquid handler and a heater-shaker to act autonomously using data from the internet, performing the necessary calculations, and ultimately writing and runner the controller code for the hardware. The system demonstrated "reasoning" capabilities as it was able to identify and search for missing information, solving multi-step problems.
Overall, the result presents a promising proof of concept for the future of autonomous experiments. Echoing the words of Derek Lowe, “It’s not that machines are going to replace chemists. It’s that the chemists who use machines will replace those that don’t” [2].
References:
[1] Boiko, D.A., MacKnight, R., Kline, B. et al. Autonomous chemical research with large language models. Nature 624, 570–578 (2023). https://doi.org/10.1038/s41586-023-06792-0
[2] Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., Isayev, O., Curtalolo, S., Fourches, D., Cohen, Y., Aspuru-Guzik, A., Winkler, D. A., Agrafiotis, D., Cherkasov, A., & Tropsha, A. (2020). Qsar without borders. Chemical Society Reviews, 49(11), 3525–3564. https://doi.org/10.1039/d0cs00098a