References

Abramson, Josh, Jonas Adler, Jack Dunger, et al. 2024. “Accurate Structure Prediction of Biomolecular Interactions with AlphaFold 3.” Nature 630: 493–500. https://doi.org/10.1038/s41586-024-07487-w.

Alpaydin, E., and C. Kaynak. 1998. Optical Recognition of Handwritten Digits. Https://archive.ics.uci.edu/dataset/80; UCI Machine Learning Repository. https://doi.org/10.24432/C50P49.

Batatia, Ilyes, Philipp Benner, Yuan Chiang, et al. 2025. “A Foundation Model for Atomistic Materials Chemistry.” The Journal of Chemical Physics 163 (18): 184110. https://doi.org/10.1063/5.0297006.

Bento, A. Patrícia, Anne Hersey, Eloy Félix, et al. 2020. “An Open Source Chemical Structure Curation Pipeline Using RDKit.” Journal of Cheminformatics 12 (1). https://doi.org/10.1186/s13321-020-00456-1.

Chen, Zhaoyang, Le Zhang, Pei Zhang, et al. 2023. “Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition.” Journal of Chemical Information and Modeling 64 (7): 2528–38. https://doi.org/10.1021/acs.jcim.3c01396.

Cretu, Miruna T, Alessandra Toniato, Amol Thakkar, Amin A Debabeche, Teodoro Laino, and Alain C Vaucher. 2023. “Standardizing Chemical Compounds with Language Models.” Machine Learning: Science and Technology 4 (3): 035014. https://doi.org/10.1088/2632-2153/ace878.

Daigavane, Ameya, Balaraman Ravindran, and Gaurav Aggarwal. 2021. “Understanding Convolutions on Graphs.” Distill, ahead of print. https://doi.org/10.23915/distill.00032.

Deng, Bowen, Peichen Zhong, KyuJung Jun, et al. 2023. “CHGNet as a Pretrained Universal Neural Network Potential for Charge-Informed Atomistic Modelling.” Nature Machine Intelligence 5 (9): 1031–41. https://doi.org/10.1038/s42256-023-00716-3.

Deringer, Volker L., Albert P. Bartók, Noam Bernstein, David M. Wilkins, Michele Ceriotti, and Gábor Csányi. 2021. “Gaussian Process Regression for Materials and Molecules.” Chemical Reviews 121: 10073–141. https://doi.org/10.1021/acs.chemrev.1c00022.

Elhajj, Sarah, and Samer Gozem. 2024. “First and Second Reductions in an Aprotic Solvent: Comparing Computational and Experimental One-Electron Reduction Potentials for 345 Quinones.” Journal of Chemical Theory and Computation 20 (14): 6227–40. https://doi.org/10.1021/acs.jctc.4c00602.

Esterhuizen, Jacques A., Bryan R. Goldsmith, and Suljo Linic. 2022. “Interpretable Machine Learning for Knowledge Generation in Heterogeneous Catalysis.” Nature Catalysis 5 (3): 175–84. https://doi.org/10.1038/s41929-022-00744-z.

Garnett, Roman. 2023. Bayesian Optimization. Cambridge University Press.

Gensch, Tobias, Gabriel dos Passos Gomes, Pascal Friederich, et al. 2022. “A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis.” Journal of the American Chemical Society 144 (3): 1205–17. https://doi.org/10.1021/jacs.1c09718.

Goerigk, Lars, and Nisha Mehta. 2019. “A Trip to the Density Functional Theory Zoo: Warnings and Recommendations for the User.” Australian Journal of Chemistry 72: 563–73. https://doi.org/10.1071/CH19023.

Gómez-Bombarelli, Rafael, Jennifer N. Wei, David Duvenaud, et al. 2018. “Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.” ACS Central Science 4: 268–76. https://doi.org/10.1021/acscentsci.7b00572.

Healy, John, and Leland McInnes. 2024. “Uniform Manifold Approximation and Projection.” Nature Reviews Methods Primers 4 (1). https://doi.org/10.1038/s43586-024-00363-x.

Ho, Jonathan, Ajay Jain, and Pieter Abbeel. 2020. Denoising Diffusion Probabilistic Models. https://doi.org/10.48550/arXiv.2006.11239.

Honda, Shion, Shoi Shi, and Hiroki R. Ueda. 2019. SMILES Transformer: Pre-Trained Molecular Fingerprint for Low Data Drug Discovery. arXiv. https://doi.org/10.48550/arXiv.1911.04738.

Hoogeboom, Emiel, Víctor Garcia Satorras, Clément Vignac, and Max Welling. 2022. Equivariant Diffusion for Molecule Generation in 3D. https://doi.org/10.48550/arXiv.2203.17003.

Isert, Clemens, Kenneth Atz, and Gisbert Schneider. 2023. “Structure-Based Drug Design with Geometric Deep Learning.” Current Opinion in Structural Biology 79: 102548. https://doi.org/10.1016/j.sbi.2023.102548.

Jensen, Jan H. 2019. “A Graph-Based Genetic Algorithm and Generative Model/Monte Carlo Tree Search for the Exploration of Chemical Space.” Chemical Science 10: 3567–72. https://doi.org/10.1039/c8sc05372c.

Kingma, Diederik P., and Max Welling. 2014. Auto-Encoding Variational Bayes. https://doi.org/10.48550/arXiv.1312.6114.

Krenn, Mario, Qianxiang Ai, Senja Barthel, et al. 2022. “SELFIES and the Future of Molecular String Representations.” Patterns 3: 100588. https://doi.org/10.1016/j.patter.2022.100588.

Landrum, Gregory A., and Sereina Riniker. 2024. “Combining IC50 or Ki Values from Different Sources Is a Source of Significant Noise.” Journal of Chemical Information and Modeling 64 (5): 1560–67. https://doi.org/10.1021/acs.jcim.4c00049.

Li, Cheng-Han, and Daniel P. Tabor. 2023. “Generative Organic Electronic Molecular Design Informed by Quantum Chemistry.” Chemical Science 14: 11045–55. https://doi.org/10.1039/d3sc03781a.

Liu, Zhen, Yurii S. Moroz, and Olexandr Isayev. 2023. “The Challenge of Balancing Model Sensitivity and Robustness in Predicting Yields: A Benchmarking Study of Amide Coupling Reactions.” Chemical Science 14 (39): 10835–46. https://doi.org/10.1039/d3sc03902a.

Loeffler, Hannes H., Jiazhen He, Alessandro Tibo, et al. 2024. “Reinvent 4: Modern AI-Driven Generative Molecule Design.” Journal of Cheminformatics 16: 20. https://doi.org/10.1186/s13321-024-00812-5.

Ma, Yuchi, Jin Liang, Dongmei Zhao, Yue-Lei Chen, Jingkang Shen, and Bing Xiong. 2014. “Condensed Fukui Function Predicts Innate c–h Radical Functionalization Sites on Multi-Nitrogen Containing Fused Arenes.” RSC Advances 4: 17262–64. https://doi.org/10.1039/C4RA01853B.

Maaten, Laurens van der, and Geoffrey Hinton. 2008. “Visualizing Data Using t-SNE.” Journal of Machine Learning Research 9 (86): 2579–605. https://jmlr.org/papers/v9/vandermaaten08a.html.

Merchant, Amil, Simon Batzner, Samuel S. Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk. 2023. “Scaling Deep Learning for Materials Discovery.” Nature 624 (7990): 80–85. https://doi.org/10.1038/s41586-023-06735-9.

Nigam, AkshatKumar, Robert Pollice, and Alán Aspuru-Guzik. 2022. “Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design.” Digital Discovery 1: 390–404. https://doi.org/10.1039/D2DD00003B.

Nigam, AkshatKumar, Robert Pollice, Mario Krenn, Gabriel dos Passos Gomes, and Alán Aspuru-Guzik. 2021. “Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules Using SELFIES.” Chemical Science 12: 7079–90. https://doi.org/10.1039/D1SC00231G.

Olivecrona, Marcus, Thomas Blaschke, Ola Engkvist, and Hongming Chen. 2017. “Molecular de-Novo Design Through Deep Reinforcement Learning.” Journal of Cheminformatics 9: 48. https://doi.org/10.1186/s13321-017-0235-x.

Riebesell, Janosh. 2022. Janosh/Diagrams: A Collection of Science and Engineering Diagrams. Https://github.com/janosh/diagrams. https://doi.org/10.5281/zenodo.7486911.

Rupp, Matthias, Alexandre Tkatchenko, Klaus-Robert Müller, and O. Anatole von Lilienfeld. 2012. “Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning.” Physical Review Letters 108: 058301. https://doi.org/10.1103/PhysRevLett.108.058301.

Sanchez-Lengeling, Benjamin, and Alán Aspuru-Guzik. 2018. “Inverse Molecular Design Using Machine Learning: Generative Models for Matter Engineering.” Science 361 (6400): 360–65. https://doi.org/10.1126/science.aat2663.

Sanchez-Lengeling, Benjamin, Emily Reif, Adam Pearce, and Alex Wiltschko. 2021. “A Gentle Introduction to Graph Neural Networks.” Distill, ahead of print. https://doi.org/10.23915/distill.00033.

Segler, Marwin H. S., Thierry Kogej, Christian Tyrchan, and Mark P. Waller. 2018. “Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks.” ACS Central Science 4: 120–31. https://doi.org/10.1021/acscentsci.7b00512.

Shields, Benjamin J., Jason Stevens, Jun Li, et al. 2021. “Bayesian Reaction Optimization as a Tool for Chemical Synthesis.” Nature 590 (7844): 89–96. https://doi.org/10.1038/s41586-021-03213-y.

Trachtenberg, Alexander, and Barak Akabayov. 2025. “From Patterns to Pills: How Informatics Is Shaping Medicinal Chemistry.” Pharmaceutics 17 (5): 612. https://doi.org/10.3390/pharmaceutics17050612.

Tripp, Austin, and José Miguel Hernández-Lobato. 2023. Genetic Algorithms Are Strong Baselines for Molecule Generation. https://doi.org/10.48550/arXiv.2310.09267.

Unke, Oliver T., and Markus Meuwly. 2019. “PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges.” Journal of Chemical Theory and Computation 15: 3678–93. https://doi.org/10.1021/acs.jctc.9b00181.

Veličković, Petar. 2023. Everything Is Connected: Graph Neural Networks. https://doi.org/10.48550/arXiv.2301.08210.

Wellawatte, Geemi P., Aditi Seshadri, and Andrew D. White. 2022. “Model Agnostic Generation of Counterfactual Explanations for Molecules.” Chemical Science 13 (13): 3697–705. https://doi.org/10.1039/d1sc05259d.

Yang, Kevin, Kyle Swanson, Wengong Jin, et al. 2019. “Analyzing Learned Molecular Representations for Property Prediction.” Journal of Chemical Information and Modeling 59: 3370–88. https://doi.org/10.1021/acs.jcim.9b00237.

Yang, Weitao, and Wilfried J. Mortier. 1986. “The Use of Global and Local Molecular Parameters for the Analysis of the Gas-Phase Basicity of Amines.” Journal of the American Chemical Society 108 (19): 5708–11. https://doi.org/10.1021/ja00279a008.