Deep learning models learn chemistry from less data; fine-tuning on pharma ELN works
marwinsegler · x · 2026-09-22
A research team presented a new deep learning study for chemical reaction prediction with three findings:
- Models can now learn from much less data;
- A new ensemble framework improves robustness;
- Fine-tuning on pharma ELN (electronic lab notebook) data is feasible.
The paper includes extensive analyses, and model code and weights are open-sourced on GitHub—an empirical AI4Science advance for chemistry and drug discovery.
More from Research
- HyperFrames and Google DeepMind Launch Code2Video Bench for Agentic Video Tasks — aftahi_ai · 2026-09-22
- Researcher proposes strict ICLR reforms: 3-paper cap, 10-page limit, reproducibility required — A_K_Nain · 2026-09-22
- Jev reportedly does tensor logic under the hood: differentiable IF args, no wasted gen tokens — StewartalsopIII · 2026-09-22
- Microsoft's BI-Bench: Frontier LLMs Score Under 50% on End-to-End Business Intelligence — MicrosoftResearch · 2026-09-22
- What Is RLCD? Fudan Researcher Explains the Method Behind Jev — SinclairWang1 · 2026-09-22
- Is synthetic-cell SpudCell the enabling tech for mirror life? Researchers weigh in — NikoMcCarty · 2026-09-22