However, these models are still far from perfect.

🔢 Counting atoms is tricky for an LLM, equivalent to counting the “r”s in strawberry.
🌀 Models struggled when representing molecules in a non-canonical format
📉 Performance was better for molecules with fewer atoms

(6/🧵)

ChemIQ more closely resembles real-world challenges, with tasks including:

📈 SAR analysis
⚗️ Reaction prediction
🚀 NMR elucidation

For example, o3-mini-high solved 74% of NMR spectra for molecules up to 10 atoms, and even solved one with 21 atoms. (5/🧵)

💥 SMILES to IUPAC conversion has been notoriously challenging, with all previous LLMs scoring ~0% on this task. In our evaluation, o3-mini-high named ~30% of structures correctly. (4/🧵)

🧪 We built ChemIQ as a benchmark for molecular comprehension and reasoning. o3-mini-high answered 59% of questions correctly, substantially improving on GPT-4o’s 7%. Additionally, more reasoning consistently improved performance across all questions. (2/🧵)

🚀 LLMs can now do chemistry! Our new preprint shows that state-of-the-art reasoning models can now perform advanced chemical reasoning tasks, without any assistance from external tools. Here’s what o3-mini can already do👇 (1/🧵)

🔥 Molecule networks 🔥 New cheminformatics data visualisation:

📊 Rapid interpretation of SAR
🔊 Find molecules in the noise
❌ No more lists of molecules

My new favourite visualisation method!

🚀 Read how I visualise Free Wilson analysis here

www.blopig.com/blog/2025/01...