mlsanityData to 14 Oct 2026

PubChemQC B3LYP/6-31G*//PM6 dataset: the Electronic Structures of 86 Million Molecules using B3LYP/6-31G* calculations

arXiv 2305.18454 ↗First posted 29 May 2023physics.chem-ph cs.LG q-bio.BMcited by 12

Not on the map. Why?

Abstract (arXiv)

This article presents the "PubChemQC B3LYP/6-31G*//PM6" dataset, containing electronic properties of 85,938,443 molecules. It includes orbitals, orbital energies, total energies, dipole moments, and other relevant properties. The dataset encompasses a wide range of molecules, from essential compounds to biomolecules up to 1000 molecular weight, covering 94.0% of the original PubChem Compound catalog (as of August 29, 2016). The electronic properties were calculated using the B3LYP/6-31G* and PM6 methods. The dataset is available in three formats: (i) GAMESS quantum chemistry program files, (ii) selected JSON output files, and (iii) a PostgreSQL database, enabling researchers to query molecular properties. Five sub-datasets offer more specific data. The first two subsets include molecules with C, H, O, and N, under 300 and 500 molecular weight respectively. The third and fourth subsets contain C, H, N, O, P, S, F, and Cl, under 300 and 500 molecular weight respectively. The fifth subset includes C, H, N, O, P, S, F, Cl, Na, K, Mg, and Ca, under 500 molecular weight. Coefficients of determination ranged from 0.892 (CHON500) to 0.803 (whole) for the HOMO-LUMO energy gap. These findings represent extensive investigations and can be utilized for drug discovery, material science, and other applications. The datasets are available under the Creative Commons Attribution 4.0 International license at https://nakatamaho.riken.jp/pubchemqc.riken.jp/b3lyp_pm6_datasets.html.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 7 of its 96 references that are in the map. First posted on arXiv 29 May 2023.

Builds on

7 of 96 references.

References · 7

  1. 2018How Powerful are Graph Neural Networks?cited by 4,393
  2. 2017MoleculeNet: A Benchmark for Molecular Machine Learningcited by 1,051
  3. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 919
  4. 2017Deep Learning Scaling is Predictable, Empiricallycited by 582
  5. 2021Inverse design of 3d molecular structures with conditional generative neural networkscited by 93
  6. 2019Deep learning for molecular design - a review of the state of the artcited by 38
  7. 2022GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Predictioncited by 28

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 5; 2025: 3; 2026: 2.

12 papers.