Improved accuracy of synthetic accessibility prediction models

Abstract

Estimation of synthetic accessibility is an important task for computer-aided drug design. A number of methods to predict synthetic accessibility are reported. Most of them are based on retrosynthetic analysis, molecular complexity, and/or fragment contributions, and there is almost no method using machine learning. We have reported a deep learning-based model to predict synthetic accessibility. Although our prediction model is successfully distinguished synthetically difficult compounds from easier ones, it cannot quantify synthetic feasibility especially for compounds of medium synthetic accessibility. To address the issue, we first examined whether optimizing the discriminant model would improve the quantitative prediction accuracy of synthetic feasibility. The results show that the model improved prediction accuracy for test sets from 99.08% to 99.32%, but it was impossible to distinguish compounds of medium synthetic accessibility in the validation set. The methods for interpreting the predictive model outputs are currently being further investigated.