ArticleNature communications2024
Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Cross-material catalyst discovery via deep learning.Nature materials · 2026Article
- tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes.Journal of chemical information and modeling · 2026Article
- Calix[4]arene Design for Enhanced Carbon Capture via Topological Learning.Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Explainable artificial intelligence for materials discovery: application to catalysts for the HER and ORR.Chemical science · 2026Article
- Machine learning-derived stage-specific design rules for metal-organic framework selection in seasonal hydrogen storage.Scientific reports · 2026Article
- Tree-Based Methods to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Peering inside the black box by learning the relevance of many-body functions in neural network potentials.Nature communications · 2025Article
- Multi-level QTAIM-enriched graph neural networks for resolving properties of transition metal complexes.Digital discovery · 2025Article
- Organic Fusion of Molecular Simulation and Wet-Lab Validation: A Promising High-Throughput Strategy for Screening Bioactive Food Peptides.Foods (Basel, Switzerland) · 2025Review
- Non-destructive measurement of eggshell strength using NIR spectroscopy and explainable artificial intelligence.Journal of the science of food and agriculture · 2025Article
- Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data.Chemical research in toxicology · 2025Article
- Polarizable Water Model with Ab Initio Neural Network Dynamic Charges and Spontaneous Charge Transfer.Journal of chemical theory and computation · 2025Article
- Analyzing Atomic Interactions in Molecules as Learned by Neural Networks.Journal of chemical theory and computation · 2025Article
- Drug repurposing of natural depsipeptide fromFrontiers in pharmacology · 2025Article
- Quantum Topological Atomic Properties of 44K molecules.Scientific data · 2024Article
- Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors.Nature communications · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.