| Autor: |
Yunchao (Lance) Liu, Rocco Moretti, Yu Wang, Bobby Bodenheimer, Tyler Derr, Jens Meiler |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
bioRxiv |
| DOI: |
10.1101/2023.04.17.537185 |
| Popis: |
In recent years several applications of graph neural networks (GNNs) to molecular tasks have emerged. Whether GNNs outperform the traditional descriptor-based methods in the quantitative structure activity relationship (QSAR) modeling in early computer-aided drug discovery (CADD) remains an open question. This paper introduces a simple yet effective strategy to boost the predictive power of QSAR deep learning models. The strategy proposes to train GNNs together with traditional descriptors, combining the strengths of both methods. The enhanced model consistently outperforms vanilla descriptors or GNN methods on nine well-curated high throughput screening datasets over diverse therapeutic targets.Abstract Figure |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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