This repository is the official implementation for our paper "OS-MSWGBM: Intelligent Analysis of Organic Synthesis Based on Multiscale Subtraction Weighted Network and LightGBM". Organic synthesis plays a vital role in optimizing existing drugs and innovating new drugs. As a significant and challenging research frontier in the field of organic synthesis, cross-coupling reactions have also attracted considerable attention. In the past few years, machine learning has realized great potential in predicting the performance of cross-coupling reactions. However, most of the existing machine learning predictions are based on the two-dimensional feature information of the cross-coupling reactions. In order to obtain the coupling reaction feature in a multifaceted way, we exploit the three-dimensional features of the molecules based on the molecular stick-and-ball model and the persistent homology analysis of topological data, respectively. On this basis, a weighted light convolutional neural network with multi-scale subtraction (OS-MSW) is proposed to extract the deep abstract features of the input data, and the extracted abstract features are applied to LightGBM for yield prediction, thus constructing a highly efficient prediction system OS-MSWGBM. In addition, the interpretability of the OS-MSW model is analyzed in this paper. The experiments demonstrate that OS-MSWGBM exhibits higher efficiency and more accurate prediction results, as well as notably stable prediction performance, which can provide reliable decision-making information for experimental personnel or organizations.
If you find this repository useful, please consider citing our paper with the following BibTeX entry.
@article{os-mswgbm,
title={OS-MSWGBM: Intelligent Analysis of Organic Synthesis Based on Multiscale Subtraction Weighted Network and LightGBM},
author={Wang, Lanfeng and Guo, Yanhui and Zhang, Zelin and Qin, Meng'en and Li, Zixin and Sun, Xiaoli and Yang, Xiaohui},
journal={MATCH-COMMUNICATIONS IN MATHEMATICAL AND IN COMPUTER CHEMISTRY},
volume={93},
number={1},
year={2025},
publisher={UNIV KRAGUJEVAC, FAC SCIENCE PO BOX 60, RADOJA DOMANOVICA 12, KRAGUJEVAC~…}
}This project is released under the MIT License.