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Artificial intelligence has deeply revolutionized the field of medicinalchemistry with many impressive applications, but the success of theseapplications requires a massive amount of training samples with high-qualityannotations, which seriously limits the wide usage of data-driven methods. Inthis paper, we focus on the reaction yield prediction problem, which assistschemists in selecting high-yield reactions in a new chemical space only with afew experimental trials. To attack this challenge, we first put forth MetaRF,an attention-based differentiable random forest model specially designed forthe few-shot yield prediction, where the attention weight of a random forest isautomatically optimized by the meta-learning framework and can be quicklyadapted to predict the performance of new reagents while given a few additionalsamples. To improve the few-shot learning performance, we further introduce adimension-reduction based sampling method to determine valuable samples to beexperimentally tested and then learned. Our methodology is evaluated on threedifferent datasets and acquires satisfactory performance on few-shotprediction. In high-throughput experimentation (HTE) datasets, the averageyield of our methodology's top 10 high-yield reactions is relatively close tothe results of ideal yield selection.

MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails