ArticleJournal of chemical theory and computation2023
Geometric Deep Learning for Molecular Crystal Structure Prediction.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- MXtalTools: A Toolkit for Machine Learning on Molecular Crystals.Journal of chemical information and modeling · 2026Article
- Prodrug-ML: prodrug-likeness prediction via machine learning on sampled negative decoys.Journal of computer-aided molecular design · 2026Article
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- Application of Synchrotron Radiation Based X-Ray Diffraction in Zeolite Research: Advanced Analysis from Atomic Structure to Dynamic Behavior.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Efficient Molecular Crystal Structure Prediction and Stability Assessment with AIMNet2 Neural Network Potentials.Crystal growth & design · 2025Article
- Atom's Dynamics and Crystal Structure: An Ordinal Pattern Method.The journal of physical chemistry. A · 2025Article
- Rapid prediction of molecular crystal structures using simple topological and physical descriptors.Nature communications · 2024Article
- Machine Learning Classification of Local Environments in Molecular Crystals.Journal of chemical theory and computation · 2024Article
- Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials.Chemical science · 2023Review
- Graph embedding and geometric deep learning relevance to network biology and structural chemistry.Frontiers in artificial intelligence · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on molecular graphs. Leveraging developments in graph-based learning and the availability of large molecular crystal data sets, we train models for density prediction and stability ranking which are accurate, fast to evaluate, and applicable to molecules of widely varying size and composition. Our density prediction model, MolXtalNet-D, achieves state-of-the-art performance, with lower than 2% mean absolute error on a large and diverse test data set. Our crystal ranking tool, MolXtalNet-S, correctly discriminates experimental samples from synthetically generated fakes and is further validated through analysis of the submissions to the Cambridge Structural Database Blind Tests 5 and 6. Our new tools are computationally cheap and flexible enough to be deployed within an existing crystal structure prediction pipeline both to reduce the search space and score/filter crystal structure candidates.
Identifiers
What OpenQuestion holds
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.