PlayMolecule glimpse: understanding protein-ligand property predictions with interpretable neural networks
| Autor: | Alejandro Varela-Rial, Iain Maryanow, Maciej Majewski, Stefan Doerr, Nikolai Schapin, José Jiménez-Luna, Gianni De Fabritiis |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
0303 health sciences
General Chemical Engineering Proteins General Chemistry Library and Information Sciences Ligands 01 natural sciences 0104 chemical sciences Computer Science Applications 010404 medicinal & biomolecular chemistry 03 medical and health sciences Application Note Neural Networks Computer 030304 developmental biology |
| Zdroj: | Journal of Chemical Information and Modeling |
| Popis: | Deep learning has been successfully applied to structure-based protein-ligand affinity prediction, yet the black box nature of these models raises some questions. In a previous study, we presented KDEEP, a convolutional neural network that predicted the binding affinity of a given protein-ligand complex while reaching state-of-the-art performance. However, it was unclear what this model was learning. In this work, we present a new application to visualize the contribution of each input atom to the prediction made by the convolutional neural network, aiding in the interpretability of such predictions. The results suggest that KDEEP is able to learn meaningful chemistry signals from the data, but it has also exposed the inaccuracies of the current model, serving as a guideline for further optimization of our prediction tools. The authors thank Acellera Ltd. for funding. G.D.F. acknowledges support from PID2020-116564GB-I00/MICIN/AEI/10.13039/501100011033 Ministerio de Ciencia e Innovación. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 823712 (CompBioMed2) and from the Industrial Doctorates Plan of the Secretariat of Universities and Research of the Department of Economy and Knowledge of the Generalitat of Catalonia. |
| Databáze: | OpenAIRE |
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