ArticleNature communications2021
Machine learning based energy-free structure predictions of molecules, transition states, and solids.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
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Who cites it
24 citing papers in PubMed.
- Automated transition state generation for mechanistic exploration in organic synthesis.Nature communications · 2026Article
- A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations.Scientific data · 2026Article
- Artificial intelligence in drug discovery: from algorithmic foundations to clinical translation.Frontiers in pharmacology · 2026Review
- 33 Unresolved Questions in Nanoscience and Nanotechnology.ACS nano · 2025Article
- Multiscale graph equivariant diffusion model for 3D molecule design.Science advances · 2025Article
- Unlocking the potential ofFrontiers in bioinformatics · 2025Review
- Direct simulation and machine learning structure identification unravel soft martensitic transformation and twinning dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Deductive machine learning models for product identification.Chemical science · 2024Article
- Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials.Precision chemistry · 2024Article
- Benchmarking machine-readable vectors of chemical reactions on computed activation barriers.Digital discovery · 2024Article
- Prospective de novo drug design with deep interactome learning.Nature communications · 2024Article
- Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.RSC advances · 2024Article
- Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design.Journal of chemical theory and computation · 2023Article
- Systematic Comparison of Experimental Crystallographic Geometries and Gas-Phase Computed Conformers for Torsion Preferences.Journal of chemical information and modeling · 2023Article
- Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design.Journal of chemical information and modeling · 2023Article
- Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction.Nano-micro letters · 2023Review
- Advancements in small molecule drug design: A structural perspective.Drug discovery today · 2023Review
- Prediction of Enzyme Catalysis by Computing Reaction Energy Barriers via Steered QM/MM Molecular Dynamics Simulations and Machine Learning.Journal of chemical information and modeling · 2023Article
- Algorithmic Graph Theory, Reinforcement Learning and Game Theory in MD Simulations: From 3D Structures to Topological 2D-Molecular Graphs (2D-MolGraphs) and Vice Versa.Molecules (Basel, Switzerland) · 2023Article
- SELFIES and the future of molecular string representations.Patterns (New York, N.Y.) · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology. Conventionally, force-fields or ab initio methods determine structure through energy minimization, which is either approximate or computationally demanding. This accuracy/cost trade-off prohibits the generation of synthetic big data sets accounting for chemical space with atomistic detail. Exploiting implicit correlations among relaxed structures in training data sets, our machine learning model Graph-To-Structure (G2S) generalizes across compound space in order to infer interatomic distances for out-of-sample compounds, effectively enabling the direct reconstruction of coordinates, and thereby bypassing the conventional energy optimization task. The numerical evidence collected includes 3D coordinate predictions for organic molecules, transition states, and crystalline solids. G2S improves systematically with training set size, reaching mean absolute interatomic distance prediction errors of less than 0.2 Å for less than eight thousand training structures - on par or better than conventional structure generators. Applicability tests of G2S include successful predictions for systems which typically require manual intervention, improved initial guesses for subsequent conventional ab initio based relaxation, and input generation for subsequent use of structure based quantum machine learning 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.