MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails
This work addresses the challenge of limited training data for chemists selecting high-yield reactions in new chemical spaces, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the reaction yield prediction problem in medicinal chemistry by proposing MetaRF, a differentiable random forest model optimized with meta-learning for few-shot learning, achieving satisfactory performance on three datasets, with its top 10 high-yield reactions in HTE datasets yielding results close to ideal selection.
Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously limits the wide usage of data-driven methods. In this paper, we focus on the reaction yield prediction problem, which assists chemists in selecting high-yield reactions in a new chemical space only with a few experimental trials. To attack this challenge, we first put forth MetaRF, an attention-based differentiable random forest model specially designed for the few-shot yield prediction, where the attention weight of a random forest is automatically optimized by the meta-learning framework and can be quickly adapted to predict the performance of new reagents while given a few additional samples. To improve the few-shot learning performance, we further introduce a dimension-reduction based sampling method to determine valuable samples to be experimentally tested and then learned. Our methodology is evaluated on three different datasets and acquires satisfactory performance on few-shot prediction. In high-throughput experimentation (HTE) datasets, the average yield of our methodology's top 10 high-yield reactions is relatively close to the results of ideal yield selection.