Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
21 May 2024
21 May 2024
Historique:
received:
11
10
2023
accepted:
24
04
2024
medline:
22
5
2024
pubmed:
22
5
2024
entrez:
21
5
2024
Statut:
epublish
Résumé
Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used to analyze complex models, but they are highly dependent on the AI technique and the origin of the reference data. Alternatively, interpretable real-space tools can be employed directly, but they are often expensive to compute. To address this dilemma between explainability and accuracy, we developed SchNet4AIM, a SchNet-based architecture capable of dealing with local one-body (atomic) and two-body (interatomic) descriptors. The performance of SchNet4AIM is tested by predicting a wide collection of real-space quantities ranging from atomic charges and delocalization indices to pairwise interaction energies. The accuracy and speed of SchNet4AIM breaks the bottleneck that has prevented the use of real-space chemical descriptors in complex systems. We show that the group delocalization indices, arising from our physically rigorous atomistic predictions, provide reliable indicators of supramolecular binding events, thus contributing to the development of Explainable Chemical Artificial Intelligence (XCAI) models.
Identifiants
pubmed: 38773090
doi: 10.1038/s41467-024-48567-9
pii: 10.1038/s41467-024-48567-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4345Subventions
Organisme : Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness)
ID : PID2021-122763NB-I00
Organisme : Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness)
ID : FPU19/02903
Organisme : Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness)
ID : EST22/00100
Informations de copyright
© 2024. The Author(s).
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