Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space
Open accessAbstract
Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.
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A deep learning algorithm trained on a dataset of known drug compounds and their biological activity was able to predict the activity of novel compounds with high accuracy.
“For instance, a DL algorithm has recently been trained using a dataset of known drug compounds, along with their corresponding biological activity [11]. The algorithm was then able to predict the activity of novel compounds with high accuracy.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [11]
AI algorithms including supervised and unsupervised learning, reinforcement, and evolutionary or rule-based algorithms can potentially contribute to solving problems of current medicinal chemistry methods.
“Different algorithms based on AI, including supervised and unsupervised learning methods, reinforcement, and evolutionary or rule-based algorithms, can potentially contribute to solving these problems.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [11]
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Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824 · see citing works
Text categorization with Support Vector Machines: Learning with many relevant features
Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,134 · see citing works
Rationale for mixing exact exchange with density functional approximations
Perdew, J. P., Ernzerhof, M., & Burke, K. (1996) · The Journal of Chemical Physics · doi:10.1063/1.472933 · Cited by 6,600 · see citing works
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010 · see citing works
Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107 · cited 1× · see citing works
“2,3 In this context, one of our goals is to shed light into the structure and properties of CCS in terms of molecular atomization energies that is an essential molecular property measuring the stability of a molecule with respect to its constituent atoms.”
BackgroundGaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Cśanyi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,144 · see citing works
Ab initio molecular simulations with numeric atom-centered orbitals
Blüm, V., Gehrke, R., Hanke, F., Havu, P., Havu, V., Ren, X., Reuter, K., & Scheffler, M. (2009) · Computer Physics Communications · doi:10.1016/j.cpc.2009.06.022 · Cited by 2,874 · see citing works
An extensive empirical study of feature selection metrics for text classification
Forman, G. (2003) · Cited by 2,379 · see citing works
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works
Self-citationQuantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966 · see citing works
Self-citationAtom-centered symmetry functions for constructing high-dimensional neural network potentials
Behler, J. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3553717 · Cited by 1,621 · see citing works
Benson, S. W. (1965) · Journal of Chemical Education · doi:10.1021/ed042p502 · Cited by 722 · see citing works
Machine learning of molecular electronic properties in chemical compound space
Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2013) · New Journal of Physics · doi:10.1088/1367-2630/15/9/095003 · Cited by 631 · see citing works
Self-citationAssessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., von Lilienfeld, O. A., Tkatchenko, A., & Müller, K. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400195d · Cited by 600 · see citing works
Self-citationVirtual screening: an endless staircase?
Schneider, G. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3139 · Cited by 536 · see citing works
Fink, T., & Reymond, J. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci600423u · Cited by 457 · see citing works
Machine learning of molecular electronic properties in chemical compound space
Montavon, G., Rupp, M., Gobre, V. V., Vázquez‐Mayagoitia, Á., Hansen, K., Tkatchenko, A., Müller, K., & von Lilienfeld, O. A. (2013) · MPG.PuRe (Max Planck Society) · doi:10.3929/ethz-b-000071765 · Cited by 414 · see citing works
Self-citationThe Blue Obelisk—Interoperability in Chemical Informatics
Guha, R., Howard, M. T., Hutchison, G., Murray‐Rust, P., Rzepa, H. S., Steinbeck, C., Wegner, J. K., & Willighagen, E. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050400b · Cited by 390 · see citing works
Virtual Exploration of the Small‐Molecule Chemical Universe below 160 Daltons
Fink, T., Bruggesser, H., & Reymond, J. (2005) · Angewandte Chemie International Edition · doi:10.1002/anie.200462457 · Cited by 356 · see citing works
Kirkpatrick, P. N., & Ellis, C. (2004) · Nature · doi:10.1038/432823a · Cited by 334 · see citing works
Lynch, B. J., & Truhlar, D. G. (2003) · The Journal of Physical Chemistry A · doi:10.1021/jp0221993 · Cited by 279 · see citing works
Using neural networks to represent potential surfaces as sums of products
Manzhos, S., & Carrington, T. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2387950 · Cited by 184 · see citing works
Patterson, A. L. (1939) · Nature · doi:10.1038/143939b0 · Cited by 53 · see citing works
Prediction of Intramolecular Polarization of Aromatic Amino Acids Using Kriging Machine Learning
Fletcher, T. L., Davie, S. J., & Popelier, P. L. A. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500416k · Cited by 42 · see citing works
Energy of Neutrons liberated in the Nuclear Fission of Uranium induced by Thermal Neutrons
Halban, H. V., Joliot, F., & Kowarski, L. (1939) · Nature · doi:10.1038/143939a0 · Cited by 34 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Lynch B. J.; Truhlar D. G. Robust and Affordable Multicoefficient Methods for Thermochemistry and Thermochemical. J. Phys. Chem. A 2003, 107, 3898.
Cited by
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Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665 · see citing works
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217 · see citing works
Self-citationANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921 · see citing works
Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445 · see citing works
Self-citationMachine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069 · see citing works
Machine Learning for Molecular Simulation
Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-042018-052331 · Cited by 956 · see citing works
Self-citationMuratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852 · see citing works
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808 · see citing works
Comparing molecules and solids across structural and alchemical space
De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp00415f · Cited by 778 · see citing works
The role of AI in drug discovery: challenges, opportunities, and strategies
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690 · see citing works
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651 · see citing works
Self-citationA Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works
Mater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565 · see citing works
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Morgan, D., & Jacobs, R. (2020) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010015 · Cited by 549 · see citing works
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works
Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289 · see citing works
Huang, B., & von Lilienfeld, O. A. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4964627 · Cited by 294 · see citing works
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Janet, J. P., Chan, L., & Kulik, H. J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00170 · Cited by 200 · see citing works
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Yao, K., Herr, J. E., Brown, S. N., & Parkhill, J. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b01072 · Cited by 126 · see citing works
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Yao, K., Herr, J. E., & Parkhill, J. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4973380 · Cited by 120 · see citing works
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Hu, W., Ye, S., Zhang, Y., Li, T., Zhang, G., Luo, Y., Mukamel, S., & Jiang, J. (2019) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b02517 · Cited by 92 · see citing works
Xu, M., Ran, T., & Chen, H. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01494 · Cited by 85 · see citing works
Haghighatlari, M., Vishwakarma, G., Altarawy, D., Subramanian, R., Kota, B. U., Sonpal, A., Setlur, S., & Hachmann, J. (2020) · WIREs Computational Molecular Science · doi:10.1002/wcms.1458 · Cited by 92 · see citing works
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Cordova, M., Wodrich, M. D., Meyer, B., Sawatlon, B., & Corminboeuf, C. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c00774 · Cited by 68 · see citing works
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Kranz, J. J., Kubillus, M., Ramakrishnan, R., von Lilienfeld, O. A., & Elstner, M. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00933 · Cited by 63 · see citing works
Self-citationMachine learning in materials design: Algorithm and application*
Song, Z., Chen, X., Meng, F., Cheng, G., Wang, C., Sun, Z., & Yin, W.-J. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abc0e3 · Cited by 63 · see citing works
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Gugler, S., Janet, J. P., & Kulik, H. J. (2020) · Molecular Systems Design & Engineering · doi:10.1039/c9me00069k · Cited by 43 · see citing works
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Iovanac, N. C., & Savoie, B. M. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b01398 · Cited by 41 · see citing works
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De, S., Musil, F., Ingram, T., Baldauf, C., & Ceriotti, M. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0192-4 · Cited by 42 · see citing works
- Authors
- Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A.
- Affiliations
- Fritz Haber Institute of the Max Planck Society (DE)
- Technische Universität Berlin (DE)
- University of Basel (CH)
- University of Basel , , Klingelbergstrasse 80 , ,
- Technical University of Berlin , , Marchstr. 23 , ,
- Corresponding author
- Tkatchenko, Alexandre
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-06-04
- Volume
- 6
- Issue
- 12
- Pages
- 2326-2331
- ISSN
- 1948-7185 (ISSN-L)
- PMC
- PMC4476293
- OpenAlex
- W1531674615
- Cited by
- 815
- References
- 25
- Citations by year
- 2026: 41 · 2025: 61 · 2024: 83 · 2023: 91 · 2022: 65 · 2021: 109 · 2020: 136 · 2019: 98 · 2018: 72 · 2017: 38 · 2016: 14 · 2015: 4 · 2012: 1
- FWCI
- 17.29
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The Journal of Physical Chemistry Letters (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- edoc (University of Basel) (acceptedVersion, other-oa)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
- edoc (University of Basel)
- Funders
- Natural Sciences and Engineering Research Council of Canada (NA)
- European Research Council (VDW-CMAT)
- Deutsche Forschungsgemeinschaft (MU 987/20)
- Schweizerische Nationalfonds zur Förderung der Wissenschaftlichen Forschung (PP00P2_138932)
- National Research Foundation of Korea (NA)
- Einstein Foundation Berlin (NA)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- molecular property prediction, atomization energies, total energy, many-body interactions, interatomic force field, molecular geometry, polarizability, frontier orbital energies
- MeSH terms
- Machine Learning, Ethanol, Models, Chemical, Quantum Theory, Thermodynamics
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