Literature DB >> 33531509

QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules.

Johannes Hoja1,2, Leonardo Medrano Sandonas1, Brian G Ernst3, Alvaro Vazquez-Mayagoitia4, Robert A DiStasio5, Alexandre Tkatchenko6.   

Abstract

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for ≈4.2 million equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equilibrium structures-comprised of constitutional/structural isomers and stereoisomers, e.g., enantiomers and diastereomers (including cis-/trans- and conformational isomers)-as well as 100 non-equilibrium structural variations thereof to reach a total of ≈4.2 million molecular structures. Computed at the tightly converged quantum-mechanical PBE0+MBD level of theory, QM7-X contains global (molecular) and local (atom-in-a-molecule) properties ranging from ground state quantities (such as atomization energies and dipole moments) to response quantities (such as polarizability tensors and dispersion coefficients). By providing a systematic, extensive, and tightly-converged dataset of quantum-mechanically computed physicochemical properties, we expect that QM7-X will play a critical role in the development of next-generation machine-learning based models for exploring greater swaths of CCS and performing in silico design of molecules with targeted properties.

Entities:  

Year:  2021        PMID: 33531509     DOI: 10.1038/s41597-021-00812-2

Source DB:  PubMed          Journal:  Sci Data        ISSN: 2052-4463            Impact factor:   6.444


  34 in total

1.  970 million druglike small molecules for virtual screening in the chemical universe database GDB-13.

Authors:  Lorenz C Blum; Jean-Louis Reymond
Journal:  J Am Chem Soc       Date:  2009-07-01       Impact factor: 15.419

Review 2.  Exploring chemical space for drug discovery using the chemical universe database.

Authors:  Jean-Louis Reymond; Mahendra Awale
Journal:  ACS Chem Neurosci       Date:  2012-04-25       Impact factor: 4.418

3.  Operators in quantum machine learning: Response properties in chemical space.

Authors:  Anders S Christensen; Felix A Faber; O Anatole von Lilienfeld
Journal:  J Chem Phys       Date:  2019-02-14       Impact factor: 3.488

4.  Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17.

Authors:  Lars Ruddigkeit; Ruud van Deursen; Lorenz C Blum; Jean-Louis Reymond
Journal:  J Chem Inf Model       Date:  2012-11-01       Impact factor: 4.956

5.  Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.

Authors:  Rafael Gómez-Bombarelli; Jorge Aguilera-Iparraguirre; Timothy D Hirzel; David Duvenaud; Dougal Maclaurin; Martin A Blood-Forsythe; Hyun Sik Chae; Markus Einzinger; Dong-Gwang Ha; Tony Wu; Georgios Markopoulos; Soonok Jeon; Hosuk Kang; Hiroshi Miyazaki; Masaki Numata; Sunghan Kim; Wenliang Huang; Seong Ik Hong; Marc Baldo; Ryan P Adams; Alán Aspuru-Guzik
Journal:  Nat Mater       Date:  2016-08-08       Impact factor: 43.841

6.  Comparing molecules and solids across structural and alchemical space.

Authors:  Sandip De; Albert P Bartók; Gábor Csányi; Michele Ceriotti
Journal:  Phys Chem Chem Phys       Date:  2016-05-18       Impact factor: 3.676

7.  Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space.

Authors:  Katja Hansen; Franziska Biegler; Raghunathan Ramakrishnan; Wiktor Pronobis; O Anatole von Lilienfeld; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  J Phys Chem Lett       Date:  2015-06-18       Impact factor: 6.475

8.  Quantum chemistry structures and properties of 134 kilo molecules.

Authors:  Raghunathan Ramakrishnan; Pavlo O Dral; Matthias Rupp; O Anatole von Lilienfeld
Journal:  Sci Data       Date:  2014-08-05       Impact factor: 6.444

9.  Quantum-chemical insights from deep tensor neural networks.

Authors:  Kristof T Schütt; Farhad Arbabzadah; Stefan Chmiela; Klaus R Müller; Alexandre Tkatchenko
Journal:  Nat Commun       Date:  2017-01-09       Impact factor: 14.919

10.  Machine learning unifies the modeling of materials and molecules.

Authors:  Albert P Bartók; Sandip De; Carl Poelking; Noam Bernstein; James R Kermode; Gábor Csányi; Michele Ceriotti
Journal:  Sci Adv       Date:  2017-12-13       Impact factor: 14.136

View more
  5 in total

1.  Physically inspired deep learning of molecular excitations and photoemission spectra.

Authors:  Julia Westermayr; Reinhard J Maurer
Journal:  Chem Sci       Date:  2021-06-30       Impact factor: 9.969

Review 2.  Ab Initio Machine Learning in Chemical Compound Space.

Authors:  Bing Huang; O Anatole von Lilienfeld
Journal:  Chem Rev       Date:  2021-08-13       Impact factor: 60.622

3.  SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects.

Authors:  Oliver T Unke; Stefan Chmiela; Michael Gastegger; Kristof T Schütt; Huziel E Sauceda; Klaus-Robert Müller
Journal:  Nat Commun       Date:  2021-12-14       Impact factor: 14.919

4.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

5.  VIB5 database with accurate ab initio quantum chemical molecular potential energy surfaces.

Authors:  Lina Zhang; Shuang Zhang; Alec Owens; Sergei N Yurchenko; Pavlo O Dral
Journal:  Sci Data       Date:  2022-03-11       Impact factor: 8.501

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.