Name
Title | ||
|---|---|---|
An adaptive graph learning method for automated molecular interactions and properties predictions |
Abstract | ||
|---|---|---|
Improving drug discovery efficiency is a core and long-standing challenge in drug discovery. For this purpose, many graph learning methods have been developed to search potential drug candidates with fast speed and low cost. In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery. Here we propose a flexible method that can adapt to any dataset and make accurate predictions. The proposed method employs an adaptive pipeline to learn from a dataset and output a predictor. Without any manual intervention, the method achieves far better prediction performance on all tested datasets than traditional methods, which are based on hand-designed neural architectures and other fixed items. In addition, we found that the proposed method is more robust than traditional methods and can provide meaningful interpretability. Given the above, the proposed method can serve as a reliable method to predict molecular interactions and properties with high adaptability, performance, robustness and interpretability. This work takes a solid step forward to the purpose of aiding researchers to design better drugs with high efficiency. Deep learning methods can provide useful predictions for drug design, but their hyperparameters need to be carefully tweaked to give good performance on a specific problem or dataset. Li et al. present here a method that finds appropriate architectures and hyperparameters for a wide range of drug design tasks and can achieve good performance without human intervention. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1038/s42256-022-00501-8 | NATURE MACHINE INTELLIGENCE |
DocType | Volume | Issue |
Journal | 4 | 7 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
14 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuquan Li | 1 | 0 | 0.68 |
| Chang-Yu Hsieh | 2 | 0 | 0.34 |
| Ruiqiang Lu | 3 | 0 | 0.34 |
| Xiaoqing Gong | 4 | 0 | 0.34 |
| Xiaorui Wang | 5 | 0 | 0.34 |
| Pengyong Li | 6 | 1 | 1.36 |
| Shuo Liu | 7 | 0 | 0.34 |
| Yanan Tian | 8 | 0 | 0.34 |
| Dejun Jiang | 9 | 2 | 2.38 |
| Jiaxian Yan | 10 | 0 | 0.34 |
| Qifeng Bai | 11 | 0 | 0.34 |
| Huanxiang Liu | 12 | 0 | 0.34 |
| Shengyu Zhang | 13 | 329 | 42.48 |
| X. J. Yao | 14 | 155 | 22.48 |