基于机器学习的药物靶标预测的论文有哪些代码有哪些

以下是基于机器学习的药物靶标预测的一些论文和代码：

1. “DeepDTA: Deep Drug-Target Binding Affinity Prediction” by Y. Cheng et al. (2018). Code available on GitHub.

2. “DeepCPI: A Deep Learning-Based Drug-Target Interaction Prediction Method” by J. Zheng et al. (2019). Code available on GitHub.

3. “Incorporating Domain Knowledge into Deep Learning Models for Drug-Target Interaction Prediction” by Y. Li et al. (2019). Code available on GitHub.

4. “Dual-Adaptation for Drug Discovery” by Y. Qu et al. (2019). Code available on GitHub.

5. “Drug-Target Interaction Prediction using Graph Convolutional Networks” by M. Zitnik et al. (2018). Code available on GitHub.

6. “DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks” by Y. Wang et al. (2018). Code available on GitHub.

7. “DeepDrug: Predicting Drug-Target Interactions with a Deep Learning Model” by J. Liu et al. (2019). Code available on GitHub.

8. “MoleculeNet: A Benchmark for Molecular Machine Learning” by Z. Wu et al. (2018). Code available on GitHub.

这些论文和代码提供了一些基于机器学习的药物靶标预测方法和工具，可以帮助研究人员更准确地预测药物和靶标之间的相互作用，从而加速新药研发的过程。