Kulik, H. J. et al. Roadmap on machine learning in electronic structure. Electron. Struct. 4, 023004 (2022).Article
ADS
CAS
Google Scholar
Sadybekov, A. V. & Katritch, V. Computational approaches streamlining drug discovery. Nature 616, 673–685 (2023).Article
ADS
CAS
PubMed
Google Scholar
von Lilienfeld, O., Müller, K. & Tkatchenko, A. Exploring chemical compound space with quantum-based machine learning. Nat. Rev. Chem. 4, 347–358 (2020).Article
Google Scholar
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R. & Tkatchenko, A. Quantum-chemical insights from deep tensor neural networks. Nat. Commun. 8, 13890 (2017).Article
ADS
PubMed
PubMed Central
Google Scholar
Gao, X., Ramezanghorbani, F., Isayev, O., Smith, J. S. & Roitberg, A. E. Torchani: A free and open source pytorch-based deep learning implementation of the ani neural network potentials. J. Chem. Inf. Model. 60, 3408–3415 (2020).Article
CAS
PubMed
Google Scholar
Bigi, F., Pozdnyakov, S. N. & Ceriotti, M. Wigner kernels: body-ordered equivariant machine learning without a basis. Preprint at https://arxiv.org/abs/2303.04124 (2023).Batzner, S. et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 13, 2453 (2022).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Steinmann, S. N., Wang, Q. & Seh, Z. W. How machine learning can accelerate electrocatalysis discovery and optimization. Mater. Horiz. 10, 393–406 (2023).Article
CAS
PubMed
Google Scholar
Dreiman, G. H. S., Bictash, M., Fish, P., Griffin, L. D. & Svensson, F. Changing the hts paradigm: Ai-driven iterative screening for hit finding. Slas Discov. 26, 257–262 (2020).Article
PubMed
PubMed Central
Google Scholar
Jansen, J. et al. Biased complement diversity selection for effective exploration of chemical space in hit-finding campaigns. J. Chem. Inf. Model. 59, 1709–1714 (2019).Article
CAS
PubMed
Google Scholar
Paricharak, S. et al. Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening. Brief. Bioinforma. 19, 277–285 (2016).
Google Scholar
Riniker, S., Wang, Y., Jenkins, J. & Landrum, G. Using information from historical high-throughput screens to predict active compounds. J. Chem. Inf. Model. 54, 1880–91 (2014).Article
CAS
PubMed
Google Scholar
Ahmed, L. et al. Efficient iterative virtual screening with apache spark and conformal prediction. J. Cheminformatics 10, 8 (2018).Article
Google Scholar
Helal, K. Y., Maciejewski, M., Gregori-Puigjané, E., Glick, M. & Wassermann, A. Public domain hts fingerprints: Design and evaluation of compound bioactivity profiles from pubchem’s bioassay repository. J. Chem. Inf. Model. 56 2, 390–398 (2016).Article
Google Scholar
Beresini, M. et al. Small-molecule library subset screening as an aid for accelerating lead identification. J. Biomol. Screen. 19, 758–770 (2014).Article
PubMed
Google Scholar
Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: Generative models for matter engineering. Science 361, 360–365 (2018).Article
ADS
CAS
PubMed
Google Scholar
Zunger, A. Inverse design in search of materials with target functionalities. Nat. Rev. Chem. 2, 0121 (2018).Article
ADS
CAS
Google Scholar
Kim, K. et al. Deep-learning-based inverse design model for intelligent discovery of organic molecules. npj Comput. Mater. 4, 67 (2018).Article
ADS
Google Scholar
Chen, Y. et al. Deep generative model for drug design from protein target sequence. J. Cheminformatics 15, 38 (2023).Article
CAS
Google Scholar
Lee, J. et al. Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review. Mater. Horiz. 10, 5436–5456 (2023).Article
CAS
PubMed
Google Scholar
Moret, M. et al. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat. Commun. 14, 114 (2023).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Lin, J. et al. Machine learning accelerates the investigation of targeted mofs: Performance prediction, rational design and intelligent synthesis. Nano Today 49, 101802 (2023).Article
Google Scholar
Noh, J., Gu, G. H., Kim, S. & Jung, Y. Machine-enabled inverse design of inorganic solid materials: Promises and challenges. Chem. Sci. 11, 4871–4881 (2020).Article
CAS
PubMed
PubMed Central
Google Scholar
Nigam, A., Pollice, R., Krenn, M., Gomes, Gd. P. & Aspuru-Guzik, A. Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (stoned) algorithm for molecules using selfies. Chem. Sci. 12, 7079–7090 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Nigam, A., Pollice, R. & Aspuru-Guzik, A. Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design. Digital Discov. 1, 390–404 (2022).Article
CAS
Google Scholar
Anstine, D. M. & Isayev, O. Generative models as an emerging paradigm in the chemical sciences. J. Am. Chem. Soc. 145, 8736–8750 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Seo, S., Lim, J. & Kim, W. Y. Molecular generative model via retrosynthetically prepared chemical building block assembly. Adv. Sci. 10, 2206674 (2023).Article
CAS
Google Scholar
Dollar, O., Joshi, N., Beck, D. A. C. & Pfaendtner, J. Attention-based generative models for de novo molecular design. Chem. Sci. 12, 8362–8372 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268–276 (2018).Article
PubMed
PubMed Central
Google Scholar
De Cao, N. & Kipf, T. MolGAN: an implicit generative model for small molecular graphs. Preprint at https://arxiv.org/abs/1805.11973 (2018).Olivecrona, M., Blaschke, T., Engkvist, O. & Chen, H. Molecular de novo design through deep reinforcement learning. J. Cheminformatics 9, 48 (2017).Article
Google Scholar
Kang, S. & Cho, K. Conditional molecular design with deep generative models. J. Chem. Inf. Model. 59, 43–52 (2018).Article
PubMed
Google Scholar
Corso, G., Stärk, H., Jing, B., Barzilay, R. & Jaakkola, T. S. DiffDock: diffusion steps, twists, and turns for molecular docking. In Proc. 11th International Conference on Learning Representations https://openreview.net/forum?id=kKF8_K-mBbS (2023).Guimaraes, G. L., Sanchez-Lengeling, B., Outeiral, C., Farias, P. L. C. & Aspuru-Guzik, A. Objective-reinforced generative adversarial networks (organ) for sequence generation models. Preprint at https://arXiv.org/abs/1705.10843 (2018).Samanta, B. et al. Nevae: A deep generative model for molecular graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, 33, 1110–1117 (2019).Li, Y., Zhang, L. & ming Liu, Z. Multi-objective de novo drug design with conditional graph generative model. J. Cheminformatics 10, 33 (2018).Article
Google Scholar
Maziarka, Ł. et al. Mol-cyclegan: a generative model for molecular optimization. J. Cheminformatics 12, 2 (2019).Article
Google Scholar
Zang, C. & Wang, F. Moflow: an invertible flow model for generating molecular graphs. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 617–626 (2020).Jin, W., Barzilay, R. & Jaakkola, T. Junction tree variational autoencoder for molecular graph generation. Preprint at https://arXiv.org/abs/1802.04364 (2019).Grover, A., Zweig, A. & Ermon, S. Graphite: Iterative generative modeling of graphs. Preprint at https://arXiv.org/abs/1803.10459 (2019).Xue, D. et al. Advances and challenges in deep generative models for de novo molecule generation. WIREs Comput. Mol. Sci. 9, e1395 (2019).Article
Google Scholar
Gebauer, N. W. A., Gastegger, M., Hessmann, S. S. P., Müller, K.-R. & Schütt, K. T. Inverse design of 3d molecular structures with conditional generative neural networks. Nat. Commun. 13, 973 (2022).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Hoogeboom, E., Satorras, V. G., Vignac, C. & Welling, M. Equivariant diffusion for molecule generation in 3d. Preprint at https://arXiv.org/abs/2203.17003 (2022).Xie, T., Fu, X., Ganea, O.-E., Barzilay, R. & Jaakkola, T. S. Crystal diffusion variational autoencoder for periodic material generation. In International Conference on Learning Representations https://openreview.net/forum?id=03RLpj-tc_ (2022).Wu, L., Gong, C., Liu, X., Ye, M. & Liu, Q. Diffusion-based molecule generation with informative prior bridges. In Advances in Neural Information Processing Systems https://openreview.net/forum?id=TJUNtiZiTKE (2022).Guan, J.et al. 3d equivariant diffusion for target-aware molecule generation and affinity prediction. In The Eleventh International Conference on Learning Representations https://openreview.net/forum?id=kJqXEPXMsE0 (2023).Xu, M. et al. Geodiff: A geometric diffusion model for molecular conformation generation. In International Conference on Learning Representations https://openreview.net/forum?id=PzcvxEMzvQC (2022).Hiener, D. C. & Hutchison, G. R. Pareto optimization of oligomer polarizability and dipole moment using a genetic algorithm. J. Phys. Chem. A 126, 2750–2760 (2022).Article
CAS
PubMed
Google Scholar
Mannodi-Kanakkithodi, A., Pilania, G., Huan, T. D., Lookman, T. & Ramprasad, R. Machine learning strategy for accelerated design of polymer dielectrics. Sci. Rep. 6, 20952 (2016).Article
ADS
PubMed
PubMed Central
Google Scholar
Yuan, Q., Santana-Bonilla, A., Zwijnenburg, M. A. & Jelfs, K. E. Molecular generation targeting desired electronic properties via deep generative models. Nanoscale 12, 6744–6758 (2020).Article
CAS
PubMed
Google Scholar
Westermayr, J., Gilkes, J., Barrett, R. & Maurer, R. J. High-throughput property-driven generative design of functional organic molecules. Nat. Comput. Sci. 3, 139–148 (2023).Article
CAS
PubMed
Google Scholar
Medrano Sandonas, L. et al. “Freedom of design” in chemical compound space: towards rational in silico design of molecules with targeted quantum-mechanical properties. Chem. Sci. 14, 10702–10717 (2023).Article
CAS
PubMed
PubMed Central
Google Scholar
Góger, S., Medrano Sandonas, L., Müller, C. & Tkatchenko, A. Data-driven tailoring of molecular dipole polarizability and frontier orbital energies in chemical compound space. Phys. Chem. Chem. Phys. 25, 22211–22222 (2023).Article
PubMed
PubMed Central
Google Scholar
Hoja, J. et al. QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules. Sci. Data 8, 43 (2021).Article
CAS
PubMed
PubMed Central
Google Scholar
van der Maaten, L. & Hinton, G. Visualizing data using t-sne. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Google Scholar
Rincón, L., Alvarellos, J. E. & Almeida, R. Electron density, exchange-correlation density, and bond characterization from the perspective of the valence-bond theory. II. Numerical results. J. Chem. Phys. 122, 214103 (2005).Collins, T. C., Euwema, R. N., Stukel, D. J. & Wepfer, G. G. Valence electron density of states of znse obtained from an energy dependent exchange approximation. Int. J. Quantum Chem. 5, 77–85 (1970).Article
Google Scholar
Shao, H., Kumar, A. & Fletcher, P. T. The riemannian geometry of deep generative models. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 428–4288 (2018).Makri, S., Ortner, C. & Kermode, J. R. A preconditioning scheme for minimum energy path finding methods. J. Chem. Phys. 150, 094109 (2019).Article
ADS
PubMed
Google Scholar
Unke, O. et al. Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects. Nat. Commun. 12, 7273 (2021).Article
ADS
CAS
PubMed
PubMed Central
Google Scholar
Schreiner, M., Bhowmik, A., Vegge, T., Jørgensen, P. B. & Winther, O. Neuralneb—neural networks can find reaction paths fast. Mach. Learn.: Sci. Technol. 3, 045022 (2022).ADS
Google Scholar
Vignac, C. & Frossard, P. Top-n: Equivariant set and graph generation without exchangeability. In International Conference on Learning Representations https://openreview.net/forum?id=-Gk_IPJWvk (2022).Zhu, X., Thompson, K. & Martinez, T. Geodesic interpolation for reaction pathways. J. Chem. Phys. 150, 164103 (2019).Article
ADS
PubMed
Google Scholar
Medrano Sandonas, L. et al. Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules. Sci. Data 11, 742 (2024).Wu, Z. et al. Moleculenet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018).Article
CAS
PubMed
Google Scholar
Sorkun, M. C., Khetan, A. & Er, S. Aqsoldb, a curated reference set of aqueous solubility and 2d descriptors for a diverse set of compounds. Sci. Data 6, 143 (2019).Article
PubMed
PubMed Central
Google Scholar
Cremer, J., Medrano Sandonas, L., Tkatchenko, A., Clevert, D.-A. & De Fabritiis, G. Equivariant graph neural networks for toxicity prediction. Chem. Res. Toxicol. 36, 1561–1573 (2023).CAS
PubMed
PubMed Central
Google Scholar
Kingma, D. P. & Welling, M. Auto-encoding variational bayes. Preprint at https://arXiv.org/abs/1312.6114 (2022).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).Article
ADS
PubMed
Google Scholar
Montavon, G. et al. Machine learning of molecular electronic properties in chemical compound space. New J. Phys. 15, 095003 (2013).Article
ADS
CAS
Google Scholar
Dokmanic, I., Parhizkar, R., Ranieri, J. & Vetterli, M. Euclidean distance matrices: Essential theory, algorithms, and applications. IEEE Signal Process. Mag. 32, 12–30 (2015).Article
ADS
Google Scholar
Hoffmann, M. & Noé, F. Generating valid euclidean distance matrices. Preprint at https://arXiv.org/abs/1910.03131 (2019).O’Boyle, N. M. et al. Open babel: An open chemical toolbox. J. Cheminformatics 3, 1–14 (2011).
Google Scholar
Seifert, G., Porezag, D. & Frauenheim, T. Calculations of molecules, clusters, and solids with a simplified LCAO-DFT-LDA scheme. Int. J. Quantum Chem. 58, 185–192 (1996).Article
CAS
Google Scholar
Gaus, M., Cui, Q. & Elstner, M. DFTB3: Extension of the self-consistent-charge density-functional tight-binding method (SCC-DFTB). J. Chem. Theory Comput. 7, 931–948 (2011).Article
CAS
Google Scholar
Tkatchenko, A., DiStasio Jr, R. A., Car, R. & Scheffler, M. Accurate and efficient method for many-body van der waals interactions. Phys. Rev. Lett. 108, 236402 (2012).Article
ADS
PubMed
Google Scholar
Stöhr, M., Michelitsch, G. S., Tully, J. C., Reuter, K. & Maurer, R. J. Communication: Charge-population based dispersion interactions for molecules and materials. J. Chem. Phys. 144, 151101 (2016).Article
ADS
PubMed
Google Scholar
Perdew, J. P., Ernzerhof, M. & Burke, K. Rationale for mixing exact exchange with density functional approximations. J. Chem. Phys. 105, 9982–9985 (1996).Article
ADS
CAS
Google Scholar
Adamo, C. & Barone, V. Toward reliable density functional methods without adjustable parameters: The PBE0 model. J. Chem. Phys. 110, 6158–6170 (1999).Article
ADS
CAS
Google Scholar
Ambrosetti, A., Reilly, A. M., DiStasio Jr, R. A. & Tkatchenko, A. Long-range correlation energy calculated from coupled atomic response functions. J. Chem. Phys. 140, 18A508 (2014).Article
PubMed
Google Scholar
Havu, V., Blum, V., Havu, P. & Scheffler, M. Efficient O(N) integration for all-electron electronic structure calculation using numeric basis functions. J. Comput. Phys. 228, 8367–8379 (2009).Article
ADS
CAS
Google Scholar
Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: Visualising image classification models and saliency maps. Preprint at https://arxiv.org/abs/1312.6034 (2014).Fallani, A., Medrano Sandonas, L. & Tkatchenko, A. Inverse mapping of quantum properties to structures for chemical space of small organic molecules. ZENODO https://doi.org/10.5281/zenodo.11537048 (2024).