ArticleScientific data2024
Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Exploring Conformational Transitions of Adenine RNA Dimer via Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Towards the design of artificial sensing materials via quantum-informed explainable AI.Journal of cheminformatics · 2026Article
- Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations.Journal of chemical theory and computation · 2026Article
- Accurate density functional theory for noncovalent interactions in charged systems.Science advances · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Unphysical Structure Collapse in Quantum Mechanics Calculations With Continuum Solvation.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry.Journal of chemical information and modeling · 2026Article
- Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction.Digital discovery · 2026Article
- Extending the range of graph neural networks with global encodings.Nature communications · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Lattice Energy Partitions in Crystals of Flexible Molecules and the 40% Limit.Journal of the American Chemical Society · 2025Article
- A dataset of chemical reaction pathways incorporating halogen chemistry.Scientific data · 2025Article
- Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions.Nature communications · 2025Article
- Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields.Journal of the American Chemical Society · 2025Article
- Molecule Graph Networks with Many-Body Equivariant Interactions.Journal of chemical theory and computation · 2025Article
- Surface Hopping Nested Instances Training Set for Excited-state Learning.Scientific data · 2025Article
- Studying Noncovalent Interactions in Molecular Systems with Machine Learning.Chemical reviews · 2025Review
- Quantum chemical properties of chlorinated polycyclic aromatic hydrocarbons for delta machine learning.Scientific data · 2025Article
- Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling.Journal of cheminformatics · 2025Article
- MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics.Scientific data · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.
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