PlayMolecule glimpse: understanding protein-ligand property predictions with interpretable neural networks
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- dc.contributor.author Varela-Rial, Alejandro
- dc.contributor.author Maryanow, Iain
- dc.contributor.author Majewski, Maciej
- dc.contributor.author Doerr, Stefan, 1987-
- dc.contributor.author Schapin, Nikolai
- dc.contributor.author Jiménez Luna, José, 1993-
- dc.contributor.author De Fabritiis, Gianni
- dc.date.accessioned 2022-01-25T07:29:55Z
- dc.date.available 2022-01-25T07:29:55Z
- dc.date.issued 2022
- dc.description.abstract 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.
- dc.description.sponsorship 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.
- dc.format.mimetype application/pdf
- dc.identifier.citation Varela-Rial A, Maryanow I, Majewski M, Doerr S, Schapin N, Jiménez-Luna J, De Fabritiis G. PlayMolecule glimpse: understanding protein-ligand property predictions with interpretable neural networks. J Chem Inf Model. 2022;62(2):225-31. DOI: 10.1021/acs.jcim.1c00691
- dc.identifier.doi http://dx.doi.org/10.1021/acs.jcim.1c00691
- dc.identifier.issn 1549-9596
- dc.identifier.uri http://hdl.handle.net/10230/52307
- dc.language.iso eng
- dc.publisher American Chemical Society (ACS)
- dc.relation.ispartof J Chem Inf Model. 2022;62(2):225-31
- dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/823712
- dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-116564GB-I0
- dc.rights © 2021 American Chemical Society. This work is licensed under a Creative Commons Attribution 4.0 International License
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri https://creativecommons.org/licenses/by/4.0/
- dc.title PlayMolecule glimpse: understanding protein-ligand property predictions with interpretable neural networks
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion