Improving synthesis prediction of small molecules at scale with RetroChimera

Custom made molecules are advancing medicine, materials, and agriculture, but producing them is slow and expensive. A new Nature paper highlights RetroChimera, a predictive model that helps accelerate chemical synthesis, helping researchers explore a wide range of molecules. The post Improving synthesis prediction of…

Felix Pultar, John Gardner, Guoqing Liu, Marwin Segler۳۰ شهریور ۱۴۰۵ ۵ دقیقه مطالعهمتن اصلی

At a glance

We report on the recent publication of our retrosynthesis model RetroChimera in the journal Nature (opens in new tab).
The paper describes the model’s architecture as well as extensive validation studies, including the model’s ability to recall rare reaction types, and successful zero-shot transfer and fine-tuning on proprietary datasets.
We open-source RetroChimera’s implementation and weights in the hope that it will enable researchers to accelerate development of new medicinally relevant molecules and advanced materials.

Developing new medicines and materials requires making new molecules but planning how to make them is still largely manual, time-consuming, and costly. RetroChimera automatically proposes high-quality synthesis routes. The model combines two strong models with complementary strengths, learning how to rank their proposals to produce better predictions than either alone. In blind tests, PhD-level chemists prefer RetroChimera’s individual reaction predictions over preceding models and recorded literature reactions.