MetaRF: attention-based random forest for reaction yield prediction with a few trails.
J Cheminform
; 15(1): 43, 2023 Apr 10.
Article
in En
| MEDLINE
| ID: mdl-37038222
ABSTRACT
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 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
J Cheminform
Year:
2023
Document type:
Article