Inverse design of 3d molecular structures with conditional generative neural networks.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
21 02 2022
21 02 2022
Historique:
received:
10
09
2021
accepted:
28
01
2022
entrez:
22
2
2022
pubmed:
23
2
2022
medline:
23
2
2022
Statut:
epublish
Résumé
The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel molecules from a learned distribution. Here, we propose a conditional generative neural network for 3d molecular structures with specified chemical and structural properties. This approach is agnostic to chemical bonding and enables targeted sampling of novel molecules from conditional distributions, even in domains where reference calculations are sparse. We demonstrate the utility of our method for inverse design by generating molecules with specified motifs or composition, discovering particularly stable molecules, and jointly targeting multiple electronic properties beyond the training regime.
Identifiants
pubmed: 35190542
doi: 10.1038/s41467-022-28526-y
pii: 10.1038/s41467-022-28526-y
pmc: PMC8861047
doi:
Banques de données
figshare
['10.6084/m9.figshare.978904']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
973Informations de copyright
© 2022. The Author(s).
Références
Hajduk, P. J. & Greer, J. A decade of fragment-based drug design: Strategic advances and lessons learned. Nat. Rev. Drug Discov. 6, 211–219 (2007).
pubmed: 17290284
doi: 10.1038/nrd2220
Mandal, S., Moudgil, M. & Mandal, S. K. Rational drug design. Eur. J. Pharmacol 625, 90–100 (2009).
pubmed: 19835861
doi: 10.1016/j.ejphar.2009.06.065
Gantzer, P., Creton, B. & Nieto-Draghi, C. Inverse-QSPR for de novo design: A review. Mol. Inf. 39, 1900087 (2020).
doi: 10.1002/minf.201900087
Freeze, J. G., Kelly, H. R. & Batista, V. S. Search for catalysts by inverse design: Artificial intelligence, mountain climbers, and alchemists. Chem. Rev. 119, 6595–6612 (2019).
pubmed: 31059236
doi: 10.1021/acs.chemrev.8b00759
Kang, K., Meng, Y. S., Breger, J., Grey, C. P. & Ceder, G. Electrodes with high power and high capacity for rechargeable lithium batteries. Science 311, 977–980 (2006).
pubmed: 16484487
doi: 10.1126/science.1122152
Hautier, G. et al. Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations. J. Mater. Chem. 21, 17147–17153 (2011).
doi: 10.1039/c1jm12216a
Scharber, M. C. et al. Design rules for donors in bulk-heterojunction solar cells–towards 10% energy-conversion efficiency. Adv. Mater. 18, 789–794 (2006).
doi: 10.1002/adma.200501717
Yu, L., Kokenyesi, R. S., Keszler, D. A. & Zunger, A. Inverse design of high absorption thin-film photovoltaic materials. Adv. Energy Mater. 3, 43–48 (2013).
doi: 10.1002/aenm.201200538
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine learning for molecular and materials science. Nature 559, 547–555 (2018).
pubmed: 30046072
doi: 10.1038/s41586-018-0337-2
von Lilienfeld, O. A., Müller, K.-R. & Tkatchenko, A. Exploring chemical compound space with quantum-based machine learning. Nat. Rev. Chem. 4, 347–358 (2020).
doi: 10.1038/s41570-020-0189-9
Schüttet, K. et al. Machine Learning Meets Quantum Physics, volume 968 of Lecture Notes in Physics (Springer International Publishing, 2020).
Unke, O. T. et al. Machine learning force fields. Chem. Rev. 121, 10142–10186 (2021).
pubmed: 33705118
pmcid: 8391964
doi: 10.1021/acs.chemrev.0c01111
Westermayr, J., Gastegger, M., Schütt, K. T. & Maurer, R. J. Perspective on integrating machine learning into computational chemistry and materials science. J. Chem. Phys. 154, 230903 (2021).
pubmed: 34241249
doi: 10.1063/5.0047760
Ceriotti, M., Clementi, C. & Anatole von Lilienfeld, O. Machine learning meets chemical physics. J. Chem. Phys. 154, 160401 (2021).
pubmed: 33940847
doi: 10.1063/5.0051418
Keith, J. A. et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem. Rev. 121, 9816–9872 (2021).
pubmed: 34232033
pmcid: 8391798
doi: 10.1021/acs.chemrev.1c00107
Behler, J. & Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98, 146401 (2007).
pubmed: 17501293
doi: 10.1103/PhysRevLett.98.146401
Rupp, M., Tkatchenko, A., Müller, K.-R. & Von Lilienfeld, O. A. Fast and accurate modeling of molecular atomization energies with machine learning. Phys. Rev. Lett. 108, 058301 (2012).
pubmed: 22400967
doi: 10.1103/PhysRevLett.108.058301
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R. & Tkatchenko, A. Quantum-chemical insights from deep tensor neural networks. Nat. Commun. 8, 13890 (2017a).
pubmed: 28067221
pmcid: 5228054
doi: 10.1038/ncomms13890
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 1263–1272 (PMLR, 2017).
Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192–3203 (2017).
pubmed: 28507695
pmcid: 5414547
doi: 10.1039/C6SC05720A
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R. SchNet—A deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).
pubmed: 29960322
doi: 10.1063/1.5019779
Chmiela, S., Sauceda, H. E., Müller, K.-R. & Tkatchenko, A. Towards exact molecular dynamics simulations with machinelearned force fields. Nat. Commun. 9, 3887 (2018).
pubmed: 30250077
pmcid: 6155327
doi: 10.1038/s41467-018-06169-2
Unke, O. T. & Meuwly, M. PhysNet: A neural network for predicting energies, forces, dipole moments, and partial charges. J. Chem. Theory Comput. 15, 3678–3693 (2019).
pubmed: 31042390
doi: 10.1021/acs.jctc.9b00181
Klicpera, J., Groß, J. & Günnemann, S. Directional message passing for molecular graphs. In International Conference on Learning Representations (ICLR) https://openreview.net/forum?id=B1eWbxStPH (2020).
Christensen, A. S., Bratholm, L. A., Faber, F. A. & Anatole von Lilienfeld, O. FCHL revisited: Faster and more accurate quantum machine learning. J. Chem. Phys. 152, 044107 (2020).
pubmed: 32007071
doi: 10.1063/1.5126701
Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. arXiv preprint arXiv 2101.03164 (2021).
Schütt, K., Unke, O. & Gastegger, M. Equivariant message passing for the prediction of tensorial properties and molecular spectra. In Proc. 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 9377–9388 (PMLR, 2021).
Zunger, A. Inverse design in search of materials with target functionalities. Nat. Rev. Chem. 2, 1–16 (2018).
doi: 10.1038/s41570-018-0121
Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science 361, 360–365 (2018).
pubmed: 30049875
doi: 10.1126/science.aat2663
Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).
doi: 10.1021/ci00057a005
Elton, D. C., Boukouvalas, Z., Fuge, M. D. & Chung, P. W. Deep learning for molecular design–a review of the state of the art. Mol. Syst. Des. Eng 4, 828–849 (2019).
doi: 10.1039/C9ME00039A
Mansimov, E., Mahmood, O., Kang, S. & Cho, K. Molecular geometry prediction using a deep generative graph neural network. Sci. Rep. 9, 1–13 (2019).
doi: 10.1038/s41598-019-56773-5
Simm, G. & Hernandez-Lobato, J. M. A generative model for molecular distance geometry. In Proc. 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 8949–8958 (PMLR, 2020).
Gogineni, T. et al. Torsionnet: A reinforcement learning approach to sequential conformer search. Adv. Neur 33, 20142–20153 (2020).
Xu, M., Luo, S., Bengio, Y., Peng, J. & Tang, J. Learning neural generative dynamics for molecular conformation generation. In International Conference on Learning Representations, https://openreview.net/forum?id=pAbm1qfheGk (2021a).
Xu, M. et al. An end-to-end framework for molecular conformation generation via bilevel programming. In Proc. 38
Ganea, O.-E. et al. GeoMol: Torsional geometric generation of molecular 3d conformer ensembles. arXiv preprint arXiv:2106.07802 (2021).
Lemm, D., von Rudorff, G. F. & von Lilienfeld, O. A. Machine learning based energy-free structure predictions of molecules, transition states, and solids. Nat. Commun. 12, 4468 (2021).
pubmed: 34294693
pmcid: 8298673
doi: 10.1038/s41467-021-24525-7
Stieffenhofer, M., Bereau, T. & Wand, M. Adversarial reverse mapping of condensed-phase molecular structures: Chemical transferability. APL Mater 9, 031107 (2021).
doi: 10.1063/5.0039102
Noé, F., Olsson, S., Köhler, J. & Wu, H. Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning. Science 365, eaaw1147 (2019).
pubmed: 31488660
doi: 10.1126/science.aaw1147
Köhler, J., Klein, L. & Noe, F. Equivariant flows: Exact likelihood generative learning for symmetric densities. In Proc. 37
Ingraham, J., Riesselman, A., Sander, C. & Marks, D. Learning protein structure with a differentiable simulator. In International Conference on Learning Representations, https://openreview.net/forum?id=Byg3y3C9Km (2018).
Lemke, T. & Peter, C. Encodermap: Dimensionality reduction and generation of molecule conformations. J. Chem. Theory Comput. 15, 1209–1215 (2019).
pubmed: 30632745
doi: 10.1021/acs.jctc.8b00975
AlQuraishi, M. End-to-end differentiable learning of protein structure. Cell Syst 8, 292–301 (2019).
pubmed: 31005579
pmcid: 6513320
doi: 10.1016/j.cels.2019.03.006
Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).
pubmed: 31942072
doi: 10.1038/s41586-019-1923-7
Jumperet, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
doi: 10.1038/s41586-021-03819-2
Gebauer, N. W. A., Gastegger, M. and Schütt, K. T. Generating equilibrium molecules with deep neural networks. NeurIPS Workshop on Machine Learning for Molecules and Materials, arXiv:1810.11347 (2018).
Gebauer, N., Gastegger, M. & Schütt, K. Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. In Advances in Neural Information Processing Systems 32, pages 7566–7578 (Curran Associates, Inc., 2019).
Hoffmann, M. & Noé, F. Generating valid euclidean distance matrices. arXiv preprint arXiv:1910.03131 (2019).
Nesterov, V., Wieser, M. & Roth, V. 3DMolNet: A generative network for molecular structures. arXiv preprint arXiv:2010.06477 (2020).
Simm, G., Pinsler, R. & Hernandez-Lobato, J. M. Reinforcement learning for molecular design guided by quantum mechanics. In Proc. 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 8959–8969 (PMLR, 2020).
Simm, G. N. C., Pinsler, R. Csányi, G. & Hernández-Lobato, J. M. Symmetry-aware actor-critic for 3d molecular design. In International Conference on Learning Representations, https://openreview.net/forum?id=jEYKjPE1xYN (2021).
Li, Y., Pei, J. & Lai, L. Learning to design drug-like molecules in three-dimensional space using deep generative models. arXiv preprint arXiv:2104.08474 (2021).
Joshi, R. P. et al. 3D-Scaffold: A deep learning framework to generate 3d coordinates of drug-like molecules with desired scaffolds. J. Phys. Chem. B 125, 12166–12176 (2021).
pubmed: 34662142
doi: 10.1021/acs.jpcb.1c06437
Satorras, V. G., Hoogeboom, E., Fuchs, F. B., Posner, I. & Welling, M. E(n) equivariant normalizing flows. arXiv preprint arXiv:2105.09016 (2021).
Meldgaard, S. A. et al. Generating stable molecules using imitation and reinforcement learning. Mach. Learn. Sci. Technol 3, 015008 (2022).
doi: 10.1088/2632-2153/ac3eb4
O’Boyle, N. M. et al. Open Babel: An open chemical toolbox. J. Cheminf. 3, 33 (2011).
doi: 10.1186/1758-2946-3-33
Ramakrishnan, R., Dral, P. O., Rupp, M. & von Lilienfeld, O. A. Quantum chemistry structures and properties of 134 kilo molecules. Sci. Data 1, 140022 (2014).
pubmed: 25977779
pmcid: 4322582
doi: 10.1038/sdata.2014.22
Reymond, J.-L. The chemical space project. Acc. Chem. Res. 48, 722–730 (2015).
pubmed: 25687211
doi: 10.1021/ar500432k
Ruddigkeit, L., Van Deursen, R., Blum, L. C. & Reymond, J.-L. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inf. Model. 52, 2864–2875 (2012).
pubmed: 23088335
doi: 10.1021/ci300415d
Zubatyuk, R., Smith, J. S., Leszczynski, J. & Isayev, O. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Sci. Adv. 5, eaav6490 (2019).
pubmed: 31448325
pmcid: 6688864
doi: 10.1126/sciadv.aav6490
Glavatskikh, M., Leguy, J., Hunault, G., Cauchy, T. & Da Mota, B. Dataset’s chemical diversity limits the generalizability of machine learning predictions. J. Cheminf. 11, 1–15 (2019).
doi: 10.1186/s13321-019-0391-2
Huang, B. & von Lilienfeld, O. A. Quantum machine learning using atom-in-molecule-based fragments selected on the fly. Nat. Chem. 12, 945–951 (2020).
pubmed: 32929248
doi: 10.1038/s41557-020-0527-z
Gastegger, M., Kauffmann, C., Behler, J. & Marquetand, P. Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes. J. Chem. Phys. 144, 194110 (2016).
pubmed: 27208939
doi: 10.1063/1.4950815
Gastegger, M. & Behler, J. Machine learning molecular dynamics for the simulation of infrared spectra. Chem. Sci. 8, 6924–6935 (2017).
pubmed: 29147518
pmcid: 5636952
doi: 10.1039/C7SC02267K
Ramachandran, P. & Varoquaux, G. Mayavi: 3D visualization of scientific data. Comput Sci. Eng. 13, 40–51 (2011). ISSN 1521-9615.
Schütt, K. et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. In Advances in Neural Information Processing Systems 30, pages 992–1002 (Curran Associates, Inc., 2017b).
Schütt, K. T. et al. SchNetPack: A deep learning toolbox for atomistic systems. J. Chem. Theory Comput. 15, 448–455 (2019).
pubmed: 30481453
doi: 10.1021/acs.jctc.8b00908
Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. International Conference for Learning Representations, arXiv:1412.6980, 2014.
RDKit, online. RDKit: Open-source cheminformatics. http://www.rdkit.org (2021).
Gebauer, N. W. A., Gastegger, M., Hessmann, S. S. P., Müller, K.-R. & Schütt, K. T. atomistic-machine-learning/cG-SchNet: Inverse design of 3d molecular structures with conditional generative neural networks. Zenodo https://doi.org/10.5281/zenodo.5907027 (2022).