An Evolutionary Algorithm for de Novo Optimization of Functional Transition Metal Compounds
Abstract
Development of functional inorganic and transition metal compounds is usually based on ad hoc qualified guesses, with computational methods playing a lesser role than in drug discovery. A de novo evolutionary algorithm (EA) is presented that automatically generates transition metal complexes using a search space constrained around chemically meaningful structures assembled from three kinds of fragments: a part shared by all structures and typically containing the metal center itself, one or several parts consisting of ligand skeletons, and unconstrained parts that may grow and vary freely. In EA optimizations, using a cost-efficient fitness function based on a linear quantitative structure-activity relationship model for catalytic activity, we demonstrate the capabilities of the method by retracing the transition from the first-generation, phosphine-based Grubbs olefin metathesis catalysts to second-generation catalysts containing N-heterocyclic carbene ligands instead of phosphines. Moreover, DFT calculations on selected high-fitness, last-generation structures from these evolutionary experiments suggest that, in terms of catalytic activity, the structures arrived at by virtual evolution alone compare favorably with existing, highly active catalysts. The structures from the evolution experiments are, however, complex and probably difficult to synthesize, but a set of manually simplified variations thereof might form the leads for a new generation of Grubbs catalysts.
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Self-citationMinenkov, Y. V., Chermak, E., & Cavallo, L. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b01163 · Cited by 49 · see citing works
Venkatraman, V., & Alsberg, B. K. (2014) · Dyes and Pigments · doi:10.1016/j.dyepig.2014.10.026 · Cited by 47 · see citing works
Self-citationDesigning High-Refractive Index Polymers Using Materials Informatics
Venkatraman, V., & Alsberg, B. K. (2018) · Polymers · doi:10.3390/polym10010103 · Cited by 46 · see citing works
Self-citationDENOPTIM: Software for Computational <i>de Novo</i> Design of Organic and Inorganic Molecules
Foscato, M., Venkatraman, V., & Jensen, V. R. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00516 · Cited by 45 · see citing works
Self-citationA Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes
Strandgaard, M., Linjordet, T., Kneiding, H., Burnage, A. L., Nova, A., Jensen, J. H., & Balcells, D. (2025) · JACS Au · doi:10.1021/jacsau.5c00242 · Cited by 45 · see citing works
Directional multiobjective optimization of metal complexes at the billion-system scale
Kneiding, H., Nova, A., & Balcells, D. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00616-5 · Cited by 42 · see citing works
Evolutionary de novo design of phenothiazine derivatives for dye-sensitized solar cells
Venkatraman, V., Foscato, M., Jensen, V. R., & Alsberg, B. K. (2015) · Journal of Materials Chemistry A · doi:10.1039/c5ta00625b · Cited by 42 · see citing works
Self-citationComputational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**
Seumer, J., Hansen, J. K. S., Nielsen, M. B., & Jensen, J. H. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202218565 · Cited by 41 · see citing works
See, X. Y., Wen, X., Wheeler, T. A., Klein, C. K., Goodpaster, J. D., Reiner, B. R., & Tonks, I. A. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c03939 · Cited by 39 · see citing works
Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge
Fey, N., & Lynam, J. M. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1590 · Cited by 38 · see citing works
Augmenting genetic algorithms with machine learning for inverse molecular design
Kneiding, H., & Balcells, D. (2024) · Chemical Science · doi:10.1039/d4sc02934h · Cited by 37 · see citing works
OSCAR: an extensive repository of chemically and functionally diverse organocatalysts
Gallarati, S., van Gerwen, P., Laplaza, R., Vela, S., Fabrizio, A., & Corminbœuf, C. (2022) · Chemical Science · doi:10.1039/d2sc04251g · Cited by 37 · see citing works
Ring‐Closing Metathesis Reactions: Interpretation of Conversion–Time Data
Thiel, V., Wannowius, K., Wolff, C., Thiele, C. M., & Plenio, H. (2013) · Chemistry - A European Journal · doi:10.1002/chem.201204150 · Cited by 36 · see citing works
Automated Building of Organometallic Complexes from 3D Fragments
Foscato, M., Venkatraman, V., Occhipinti, G., Alsberg, B. K., & Jensen, V. R. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci5003153 · Cited by 35 · see citing works
Self-citationThe activation mechanism of Fe-based olefin metathesis catalysts
Poater, A., Pump, E., Vummaleti, S. V. C., & Cavallo, L. (2014) · Chemical Physics Letters · doi:10.1016/j.cplett.2014.06.063 · Cited by 35 · see citing works
A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target
Gallarati, S., van Gerwen, P., Laplaza, R., Brey, L., Makaveev, A., & Corminbœuf, C. (2024) · Chemical Science · doi:10.1039/d3sc06208b · Cited by 33 · see citing works
Poater, A., Credendino, R., Slugovc, C., & Cavallo, L. (2013) · Dalton Transactions · doi:10.1039/c3dt32884h · Cited by 33 · see citing works
Virtual Screening of Hole Transport, Electron Transport, and Host Layers for Effective OLED Design
Lu, S.-Y., Mukhopadhyay, S., Froese, R. D. J., & Zimmerman, P. M. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00044 · Cited by 33 · see citing works
Artificial evolution of coumarin dyes for dye sensitized solar cells
Venkatraman, V., Abburu, S., & Alsberg, B. K. (2015) · Physical Chemistry Chemical Physics · doi:10.1039/c5cp04624f · Cited by 32 · see citing works
Self-citationThe (not so) simple prediction of enantioselectivity – a pipeline for high-fidelity computations
Laplaza, R., Sobez, J., Wodrich, M. D., Reiher, M., & Corminbœuf, C. (2022) · Chemical Science · doi:10.1039/d2sc01714h · Cited by 30 · see citing works
Mapping the properties of bidentate ligands with calculated descriptors (LKB-bid)
Fey, N., Koumi, A., Malkov, A. V., Moseley, J. D., Nguyen, B. N., Tyler, S. N. G., & Willans, C. E. (2020) · Dalton Transactions · doi:10.1039/d0dt01694b · Cited by 30 · see citing works
Lakuntza, O., Besora, M., & Maseras, F. (2018) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.8b02372 · Cited by 29 · see citing works
Computer-aided molecular design of imidazole-based absorbents for CO 2 capture
Venkatraman, V., Gupta, M., Foscato, M., Svendsen, H. F., Jensen, V. R., & Alsberg, B. K. (2016) · International journal of greenhouse gas control · doi:10.1016/j.ijggc.2016.02.023 · Cited by 27 · see citing works
Self-citationA Quantitative Model for Alkane Nucleophilicity Based on C−H Bond Structural/Topological Descriptors
Besora, M., Olmos, A., Gava, R., Noverges, B., Asensio, G., Caballero, A., Maseras, F., & Pérez, P. J. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201914386 · Cited by 25 · see citing works
Quantitative structure-property relationship modeling of Grätzel solar cell dyes
Venkatraman, V., Åstrand, P., & Alsberg, B. K. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23485 · Cited by 25 · see citing works
Self-citationRing Closure To Form Metal Chelates in 3D Fragment-Based de Novo Design
Foscato, M., Houghton, B. J., Occhipinti, G., Deeth, R. J., & Jensen, V. R. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00424 · Cited by 21 · see citing works
Self-citationHerndon, J. W. (2014) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2014.02.026 · Cited by 21 · see citing works
Smith, S. C., Horbaczewskyj, C. S., Tanner, T. F. N., Walder, J. J., & Fairlamb, I. J. S. (2024) · Digital Discovery · doi:10.1039/d3dd00249g · Cited by 20 · see citing works
Pazio, A. E., Woźniak, K., Grela, K., & Trzaskowski, B. (2015) · Dalton Transactions · doi:10.1039/c5dt03446a · Cited by 19 · see citing works
Pastor, J., Rezabal, E., Voituriez, A., Betzer, J., Marinetti, A., & Frison, G. (2018) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.7b03248 · Cited by 18 · see citing works
Martínez, J. P., Vummaleti, S. V. C., Falivene, L., Nolan, S. P., Cavallo, L., Solà, M., & Poater, A. (2016) · Chemistry - A European Journal · doi:10.1002/chem.201600383 · Cited by 17 · see citing works
MoleGear: A Java-Based Platform for Evolutionary De Novo Molecular Design
Chu, Y., & He, X. (2019) · Molecules · doi:10.3390/molecules24071444 · Cited by 17 · see citing works
Self-citationMukai, M., Nagao, K., Yamaguchi, S., & Ohmiya, H. (2022) · Bulletin of the Chemical Society of Japan · doi:10.1246/bcsj.20210349 · Cited by 17 · see citing works
Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**
Seumer, J., Hansen, J. K. S., Nielsen, M. B., & Jensen, J. H. (2023) · Angewandte Chemie · doi:10.1002/ange.202218565 · Cited by 15 · see citing works
Genetic algorithm-based re-optimization of the Schrock catalyst for dinitrogen fixation
Strandgaard, M., Seumer, J., Benediktsson, B., Bhowmik, A., Vegge, T., & Jensen, J. H. (2023) · PeerJ Physical Chemistry · doi:10.7717/peerj-pchem.30 · Cited by 14 · see citing works
Foscato, M., Occhipinti, G., Eliasson, S. H. H., & Jensen, V. R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01649 · Cited by 13 · see citing works
Self-citationDiscovery of molybdenum based nitrogen fixation catalysts with genetic algorithms
Strandgaard, M., Seumer, J., & Jensen, J. H. (2024) · Chemical Science · doi:10.1039/d4sc02227k · Cited by 13 · see citing works
Seumer, J., & Jensen, J. H. (2025) · PeerJ Physical Chemistry · doi:10.7717/peerj-pchem.34 · Cited by 13 · see citing works
Genetic Algorithms for the Discovery of Homogeneous Catalysts
Gallarati, S., van Gerwen, P., Schoepfer, A. A., Laplaza, R., & Corminbœuf, C. (2023) · CHIMIA International Journal for Chemistry · doi:10.2533/chimia.2023.39 · Cited by 13 · see citing works
Martínez, J. P., & Trzaskowski, B. (2022) · ChemPhysChem · doi:10.1002/cphc.202200580 · Cited by 12 · see citing works
Stabilization of activated fragments by shell‐wise construction of an embedding environment
Krausbeck, F., Sobez, J., & Reiher, M. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24749 · Cited by 11 · see citing works
CAT-COSMO-CAMPD: Integrated in silico design of catalysts and processes based on quantum chemistry
Gertig, C., Fleitmann, L., Hemprich, C., Hense, J., Bardow, A., & Leonhard, K. O. (2021) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2021.107438 · Cited by 11 · see citing works
High-throughput computational workflow for ligand discovery in catalysis with the CSD
Short, M. A. S., Tovee, C. A., Willans, C. E., & Nguyen, B. N. (2023) · Catalysis Science & Technology · doi:10.1039/d3cy00083d · Cited by 10 · see citing works
Trzaskowski, B., & Ostrowska, K. (2016) · RSC Advances · doi:10.1039/c6ra01194b · Cited by 10 · see citing works
Martínez, J. P., & Trzaskowski, B. (2022) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.1c09336 · Cited by 10 · see citing works
Frangoulis, L., Khatibi, Z., Mariano, L. A., & Lunghi, A. (2025) · JACS Au · doi:10.1021/jacsau.5c00502 · Cited by 9 · see citing works
Moving from Classical Ru-NHC to Neutral or Charged Rh-NHC Based Catalysts in Olefin Metathesis
Poater, A. (2016) · Molecules · doi:10.3390/molecules21020177 · Cited by 9 · see citing works
Steric Maps to Evaluate the Role of Steric Hindrance on the IPr NHC Ligand
Poater, A., Falivene, L., Urbina‐Blanco, C. A., Manzini, S., Nolan, S. P., & Cavallo, L. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.249 · Cited by 9 · see citing works
Marimon, L. A., Chauvin, R., & Poater, A. (2017) · Catalysts · doi:10.3390/catal7030085 · Cited by 8 · see citing works
A Quantitative Model for Alkane Nucleophilicity Based on C−H Bond Structural/Topological Descriptors
Besora, M., Olmos, A., Gava, R., Noverges, B., Asensio, G., Caballero, A., Maseras, F., & Pérez, P. J. (2019) · Angewandte Chemie · doi:10.1002/ange.201914386 · Cited by 7 · see citing works
TD-DFT based fine-tuning of molecular excitation energies using evolutionary algorithms
Abburu, S., Venkatraman, V., & Alsberg, B. K. (2015) · RSC Advances · doi:10.1039/c5ra22800j · Cited by 6 · see citing works
Self-citationToward AI/ML-assisted discovery of transition metal complexes
Jin, H., & Merz, K. M. (2024) · Annual reports in computational chemistry · doi:10.1016/bs.arcc.2024.10.003 · Cited by 5 · see citing works
von Meyenn, C. L. M., & Palkovits, S. (2023) · Energy Advances · doi:10.1039/d2ya00312k · Cited by 5 · see citing works
Ekeli, J. B., Foscato, M., Blanco, C. O., Occhipinti, G., Fogg, D. E., & Jensen, V. R. (2024) · ACS Catalysis · doi:10.1021/acscatal.4c06212 · Cited by 4 · see citing works
Self-citationTowards sustainable exploration of chemical spaces with machine learning
Sandonas, L. M., Balcells, D., Barnowsky, T., Bochkarev, A. S., Cole, J., Deringer, V. L., Dobrautz, W., Ehrenhofer, A., Frank, T., Friederich, P., et al. (2026) · Digital Discovery · doi:10.1039/d6dd00157b · Cited by 4 · see citing works
Martínez, J. P., Solà, M., & Poater, A. (2021) · Chemistry - A European Journal · doi:10.1002/chem.202100840 · Cited by 4 · see citing works
Many-body expansion based machine learning models for octahedral transition metal complexes
Meyer, R., Chu, D. B. K., & Kulik, H. J. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad9f22 · Cited by 2 · see citing works
Evolution inspector: Interactive visual analysis for evolutionary molecular design
Šoltészová, V., Foscato, M., Eliasson, S. H. H., & Jensen, V. R. (2015) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2015.7347687 · Cited by 1 · see citing works
Self-citationEvolutionary Algorithms and Workflows for De Novo Catalyst Design
Foscato, M., Ekeli, J. B., Costamagna, M., Grellscheid, D., & Jensen, V. R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-821978-2.00028-3 · Cited by 1 · see citing works
Self-citationUsing Metal Vinylidene Complexes to Probe the Partnership Between Theory and Experiment
Slattery, J. M., Lynam, J. M., & Fey, N. (2014) · Wiley eBooks · doi:10.1002/9783527678211.ch3 · Cited by 1 · see citing works
Janet, J. P. (2022) · ACS symposium series · doi:10.1021/bk-2022-1416.ch007
Moshchenkov, A. D., Ryzhako, A. S., Rozov, T. P., Otlyotov, A. A., & Minenkov, Y. V. (2026) · Journal of Computational Chemistry · doi:10.1002/jcc.70520
- Authors
- Chu, Y., Heyndrickx, W., Occhipinti, G., Jensen, V. R., & Alsberg, B. K.
- Affiliations
- Norwegian University of Science and Technology (NO)
- University of Bergen (NO)
- Corresponding author
- Jensen, Vidar R.
- Alsberg, Bjørn K.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2012-05-15
- Volume
- 134
- Issue
- 21
- Pages
- 8885-8895
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W2314360802
- Cited by
- 90
- References
- 84
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 9 · 2023: 8 · 2022: 9 · 2021: 6 · 2020: 5 · 2019: 6 · 2018: 7 · 2017: 4 · 2016: 6 · 2015: 8 · 2014: 10 · 2013: 6
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Chemical Society (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Keywords
- evolutionary algorithms, de novo design, transition metal complexes, N-heterocyclic carbene ligands, phosphine ligands, QSAR, catalytic activity
- MeSH terms
- Algorithms, Alkenes, Catalysis, Organometallic Compounds, Ruthenium, Drug Design, Transition Elements
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