Графический дизайн как визуальный язык межкультурного взаимодействия

This article describes how visual graphics language as a sign system can be in contact with the audience, overcoming the language barrier. In terms of graphic design it can be available to transfer information, and even affect the viewer, causing artistic and emotional reflection.Эта статья о том, как визуальный язык графики в виде знаковой символики может входить в контакт со зрителем, преодолевая языковый барьер. На языке графического дизайна можно доступно передать информацию и даже воздействовать на зрителя, вызывая при этом художественно-эмоциональные образы

The analysis of similarity.

In this plot, similarity is 0 means that all training samples are selected from the subset in which the similarity between the samples and the test set is below a certain value. According to the Pearson correlation coefficient (Rp) and Root mean square error (RMSE) of predicted versus experimental binding affinity, the performance of the model continues to improve as protein similarity increases. The ifp denotes interaction fingerprint.</p

Comparison on CASF-2016.

Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and interaction fingerprints, LGN achieves Pearson correlation coefficients of up to 0.842 on the PDBbind 2016 core set, compared to 0.807 when using the features of complex graphs alone. Finally, we verify the rationalization and generalization of our model through comprehensive experiments. We also compare our model with state-of-the-art baseline methods, which validates the superiority of our model. To reduce the impact of data similarity, we increase the robustness of the model by incorporating ensemble learning.</div

The performance of our fusion model on the training set and test set of two datasets.

For both training set, the prediction affinity and experimentally measured affinity are fitted well. For test set, CASF-2016 shows preferable outcome while PDBbindv2020 has more outliers. The reason is that PDBbindv2020 has more novel samples whose structures have just been found. Since the purpose of prediction is to find more hopeful candidate than others, the coefficient is commonly used as metric in this field.</p

The analysis of different models.

Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and interaction fingerprints, LGN achieves Pearson correlation coefficients of up to 0.842 on the PDBbind 2016 core set, compared to 0.807 when using the features of complex graphs alone. Finally, we verify the rationalization and generalization of our model through comprehensive experiments. We also compare our model with state-of-the-art baseline methods, which validates the superiority of our model. To reduce the impact of data similarity, we increase the robustness of the model by incorporating ensemble learning.</div

Brief description of the hyperparameters of model.

Brief description of the hyperparameters of model.</p

Brief description of the featurization of complex.

Brief description of the featurization of complex.</p

The framework of the ensemble model.

By using a boosting framework, we could get a more robust model and better performance. For each model, we only use part of the dataset by random sampling.</p

The visualization of the effects of different fingerprints and the performance of different models.

Notably, higher values of Rp correspond to superior results, and we use 2-Rp in the diagram to facilitate comparison. For optimal results, all three metrics should exhibit low values. FPS denotes fingerprint SIFP, FPE denotes fingerprint ECIF, FPC denotes fingerprint CFP and FPSE denotes the combination of SIFP and ECIF.</p

Performance of the individual model and the fusion model.

Performance of the individual model and the fusion model.</p