Phase II publications

Phase II Publications (since Sept 2022)

Note: Center PI names are in bold. Industry collaborator names are underlined

127. Anstine, D. M.; Zubatyuk, R.; Gallegos, L. C.; Paton, R. S.; Wiest, O.; Nebgen, B.; Jones, T.; Gomes, G.; Tretiak, S.; Isayev, O. Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions Nat. Comput. Sci. 2026, 6, accepted for publication

126. Huang, Y.; Gao, C.; Wu, S.; Wang, H.; Wang, X.; Zhou, Y.; Wang, Y.; Ye, J.; Shi, J.; Zhang, Q.; Li, Y.; Bao, H.; Liu, Z.; Guan, T.; Wang, P.; Zhuang, H.; Chen, D.; Guo, K.; Zou, A.; Kuen-Yew, B. H.; Xiong, C.; Stengel-Eskin, E.; Zhang, H.; Yin, H.; Zhang, H.; Yao, H.; Yoon, J.; Zhang, J.; Shu, K.; Krishna, R.; Swayamdipta, S.; Shi, W.; Li, X.; Li, Y.; Hao, Y.; Jia, Z.; Li, Z.; Chen, X.; Tu, Z.; Hu, Z.; Zhou, T.; Zhao, J.; Sun, L.; Huang, F.; Sasson, O. C.; Sattigeri, P.; Reuel, A.; Lamparth, M.; Zhao, Y.; Dziri, N.; Su, Y.; Sun, H.; Ji, H.; Xiao, C.; Bansal, M.; Chawla, N. V.; Pei, J.; Gao, J.; Backes, M.; Yu, P. S.; Gong, N. Z.; Chen, P.-Y.; Li, B.; Song, D.; Zhang, X.; TrustGen: A Platform of Dynamic Benchmarking on the Trustworthiness of Generative Foundation Models Int. Conf. Learn. Represent. (ICLR’26) 2026, accepted

125. MacKnight, R.; Novitskiy, I. M.; Radadiya, R.; Gomes, G. Provenance Grounds Trust in Autonomous Science Nat. Comput. Sci. 2026, 6, accepted for publication

124. Huang, Y.; Hua, H.; Zhou, Y.; Jing, P.; Nagireddy, M.; Padhi, I.; Dolcetti, G.; Xu, Z.; Chaudhury, S.; Rawat, A.; Nedoshivina, L.; Chen, P.-Y.; Sattigeri, P.; Zhang, X. Building a Foundational Guardrail for General Agentic Systems via Synthetic Data. Int. Conf. Learn. Represent. (ICLR’26) 2026, accepted

123. Shen, Y.; Zhang, X. Driving Reaction Trajectories via Latent Flow Matching ACM SIGKDD Intl. Conf. Knowl. Disc. Data(KDD’26) 2026, accepted. https://doi.org/10.1145/3770855.3819042

122. Bennin, E.; O’Connell, R. J.; Zavala, C.; Gonzáles-Montiel, G. A.; Wiest, O.; Darko, A. Strategic Ligand Design for Modulating Axial Sites in Dirhodium Catalysts: Towards Enhanced Asymmetric Cyclopropanation Dalton Trans. 2026, accepted for publication

121. Sun, F.; Huang, Z.; Cao, Y.; Luo, X.; Wang, W.; Sun, Y. DoMiNO: Decomposing Molecular Dynamics with Multi-Scale Neural Graph Ordinary Differential Equations. ACM Trans. Knowl. Discov. Data 2026, accepted. https://doi.org/10.1145/3822365

120. Zhang, J. J.; Ha, S. K.; Roh, J.; Tu, Z.; Verna, P.; Coley, C. W.; Jensen, K. F. Pathway-Aware Template-Based Retrosynthesis. J. Chem. Inf. Model. 2026, 66, 7489–7500. https://doi.org/10.1021/acs.jcim.6c01458

119. Yu, J.; Wang, W.; Huang, Y.; Wang, Y.; Zhou, Z.; Chen, X.; Liu, Y.; Wang, W.; Zhang, X. RISKLAB: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems. Proc. 64th Ann. Meet. ACL, 2026 167–177. https://doi.org/10.18653/v1/2026.acl-demo.17

118. Tran, S. B.; Roh, J.; Coley, C. W. Quantifying the Failure Modes of Current One-Step Retrosynthesis Models. Chem. Sci. 2026, 17, https://doi.org/10.1039/d6sc01323f

117. Gutiérrez-Valencia, N. E.; Schleinitz, J.; Wild, T. H.; Williams, W. L.; Mantin, D. Doyle, A. G.; Reisman, S. E.; Sigman, M. S. BUNNY: An N, N-Bidentate Nitrogen Ligand Descriptor Library. Development and Application to Modeling of Ni-Catalyzed Asymmetric Cross-Electrophile Coupling Reactions ACS Catal. 2026, 16, 11532–11547. https://doi.org/10.1021/acscatal.6c02585

116. Joung, J. F.; Casetti, N.; Raghavan, P. Coley, C. W. An Overview of reaction outcome prediction with physics-based and data-driven methods Chem. Soc. Rev. 2026, 55, 6768–6813. https://doi.org/10.1039/D6CS00079G

115. Rago, A. J.; Raghavan, P.; Santiago-Capeles, L.; Zhang, R.; Wang Y.; Coley, C. W. A Machine Learning-Based workflow for transaminase selection Chem. Sci. 2026, 17, 12334–12345. https://doi.org/10.1039/d6sc00852f

114. Huang, X.; Ma, Y.; Gurajapu, A.; Schleinitz, J.; Guo, Z.; Hua, T.; Reisman, S. E.; Chawla, N. V. ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery ACM SIGKDD Intl. Conf. Knowl. Disc. Data 2026, accepted

113. Nogueira, B.; Gonzales-Montiel, G. A.; Jiang, M.; Chawla, N. V.; Moniz, N. SPECTRA: Spectral Domain-Aware Graph Generation for Imbalanced Molecular Property Regression ACM SIGKDD Intl. Conf. Knowl. Disc. Data (KDD’26) 2026, accepted.

112. Chawla, N. V.; González-Montiel, G. A.; Guo, K.; Guo, T.; Hua, T.; Huang, X.; Inae, E.; Jiang, M.; Le, K.; Liu, G.; Maier, J. C.; Moniz, N.; Nogueira, B.; Pan, D.; Piguave, B. V.; Savoie, B. M.; Schofield, A. B.; Shen, Y.; Taylor, A.; Zhang, X. Zhu, Y.; Wiest, O. Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms Chem. Rev. 2026, 126, 7587–7635. https://doi.org/10.1021/acs.chemrev.5c01081

111. LeSueur, A.; Bianchi, P.; Gallarati, S.; Lefave, S. J.; Sigman, M. S.. Best Practices and Considerations for Applying Multiple Linear Regression in Organic Chemistry Research. J. Org. Chem. 2026, 91, 5733–5744. https://doi.org/10.1021/acs.joc.5c03206

110. Parmar, K. S.; Bawel, S.; Popescu, M. V.; Mai, B. K.; Timmerman, J. C.; Altundas, B.; Paton, R. S.; Denmark, S. E. The Atroposelective Iodination of 2-Amino-6-arylpyridines Catalyzed by Chiral Disulfonimides Actually Proceeds via Brønsted Base Catalysis: A Combined Experimental, Computational, and Machine-Learning Study. J. Am. Chem. Soc. 2026, 148, 2175–2190. https://doi.org/10.1021/jacs.5c10238

109. Le, K.; Hua, T.; Chawla, N. V. AgentDrug: Utilizing Large Language Models in an Agentic Workflow for Zero-Shot Molecular Editing Conf. Emp. Meth. Natural Lang. Proc (EMNLP2025) 2025 24448–24458. https://aclanthology.org/anthology-files/anthology-files/pdf/findings/2025.findings-emnlp.1328.pdf

108. Roh, J.; Joung, J. F.; Yu, K.; Tu, Z.; Bartholomew, G. L.; Santiago-Reyes, O. A.; Fong, M. H.; Sarpong, R.; Reisman, S. E.; Coley, C. W. Higher-level strategies for computer-aided retrosynthesis. ACS Cent. Sci.2026, 12, 345–357. https://doi.org/10.1021/acscentsci.5c02014

DOI of dataset(s):https://doi.org/10.5281/zenodo.19560196

107. Jin, T.; Sass, J. I.; Gao, W.; Hurst, A. A.; Coley, C. W.; Alexander-Katz, A., 2026. Polymerized Short Sequences as a Template for Protein Folding and Evolution. Nano Lett. 2026, 26, 4471–4479. https://doi.org/10.1021/acs.nanolett.6c00527

106. Tian, Y.; Zhang, C.; Kou, Z.; Liu, Z.; Zhang, X.; Chawla, N. V. Adaptive and Context-rich Generative Self- supervised Learning on Graphs. Proc. AAAI Conf. Artif. Intell. 2026, 40, 25923–25931. https://doi.org/10.1609/aaai.v40i31.39792

105. Fadul, A.; Cundari, T.; Bertke, J.; Toledo, S. A. Tetrad or triad? insights from a versatile Fe(II) structural and functional model of the 3-histidine 1-carboxylate tetrad in C–C bond cleaving dioxygenase enzymes. RSC Adv. 2026, 16, 8695.16A. https://doi.org/10.1039/D5RA09716A

104. Gallarati, S.; Bucci, E. M.; Doyle, A. G.; Sigman, M. S. Transferable enantioselectivity models from sparse data. Nature 2026, 651, 637–646. https://www.nature.com/articles/s41586-026-10239-7

DOI of code/dataset(s):https://doi.org/10.5281/zenodo.18356432

103. Smith, A. L.; Toste, F. D. Stereoselective Generalizations over Diverse Sets of Chiral Acids Enabled by Buried Volume. J. Am. Chem. Soc. 2026, 148, 2792–2800. https://doi.org/10.1021/jacs.5c20342

102. Kalita, B.; Gokcan, H.; Isayev, O. Machine learning interatomic potentials at the centennial crossroads of quantum mechanics. Nat. Comput. Sci. 2025, 5, 1120–1132. https://doi.org/10.1038/s43588-025-00930-6

101. Zhou, Y.; Yang, J.; Huang, Y.; Guo, K.; Emory, Z.; Ghosh, B.; Bedar, A.; Shekar, S.; Liang, Z.; Chen, P. Y.; Gao, T.; Geyer, W.; Moniz, N.; Chawla, N. V.; Zhang, X. Benchmarking LLMs on safety issues in scientific labs. Nat. Mach. Intell. 2026, 8, 20–31. https://doi.org/10.1038/s42256-025-01152-1

100. Eremin, D. B.; Jha, K. K.; Delgadillo, D. A.; Zhang, H.; Foxman, S. H.; Johnson, S. N.; Vlahakis, N. W.; Cascio, D.; Lavallo, V.; Rodriguez, J. A.; Nelson, H. M. Spatially Aware Defraction Mapping Enables Fully Autonomous MicroED. J. Am. Chem. Soc. 2025, 147, 4299–42310. https://doi.org/10.1021/jacs.5c10751

DOI of dataset(s):https://doi.org/10.5281/zenodo.19560192

99.Guo, T.; Ma, G.; Guo, K.; Chen, X.; Nan, B.; Pi, S.; Chawla, N.; Wiest, O.; Zhang, X. ReactionTeam: Teaming Experts for Divergent Thinking Beyond Typical Reaction Patterns IEEE BigData2025 2025 10.1109/BigData66926.2025.11402365.

DOI of dataset(s):https://doi.org/10.5281/zenodo.19560190

98. Zhu, Y.; Shi, Y.; Chen, Y.; Sun, F. Sun, Y.; Wang, W. Symmetry-Preserving Conformer Ensemble Networks for Molecular Representation Learning NeurIPS 2025, 39.

DOI of dataset(s):https://doi.org/10.5281/zenodo.19560170

97. Gusev, F.; Kline, B. C.; Quinn, R.; Xu, A.; Smith, B.; Frezza, B.; Isayev, O., Machine Learning anomaly detection of automated HPLC experiments in the Cloud Laboratory. Digit. Discovery 2025, 4, 3445–3454. https://doi.org/10.1039/D5DD00253B

96. Mele, L.; Engel, P. D.; Cadge, J. A.; Peciukenas, V.; Choi, H.; Sigman, M. S.; Cornella, J. Ligand-Controlled Chemodivergent Bismuth Catalysis. J. Am. Chem. Soc. 2025, 147, 42406–42415. https://doi.org/10.1021/jacs.5c11854

DOI of dataset(s):https://doi.org/10.5281/zenodo.19560132

95. Cadge, J.; Hart, S. D.; Walroth, R. C.; Sigman, M. S.; Mack, K. Bisphosphine ligand conformer selection to enhance descriptor database representation: improving statistical modelling outcomes. Chem. Sci. 2025, 16, 20473–20485. https://doi.org/10.1039/D5SC04691B

DOI of dataset(s):https://doi.org/10.5281/zenodo.19560184

94. Casetti, N.; Anstine, D.; Isayev, O.; Coley, C. W. Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials J. Chem. Theory Comput. 2025, 21, 10362–10372. https://doi.org/10.1021/acs.jctc.5c01161

93. Liu, Z.; Vinkus, J.; Fu, Y.; Liu, P.; Noonan, K. J. T.; Isayev, O. Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions J. Am. Chem. Soc. Au 2025, 5, 4750–4761. https://doi.org/10.1021/jacsau.5c00667

92. Huang, Y.; Jiang, Z.; Luo, X.; Guo, K.; Zhuang, H.; Zhou, Y.; Yuan, Z.; Sun, X.; Schleinitz, J.; Wang, Y.; Zhang, S.; Surve, M.; Chawla, N. V.; Wiest, O.; Zhang, X. ChemOrch: Empowering LLMs with Chemical Intelligence via Groundbreaking Synthetic Instructions NeurIPS 2025, 39, 6578

DOI of dataset(s):https://doi.org/10.5281/zenodo.19560256

91. Zacate, S. B.; Dantas, J. A.; Lin, S.; Doyle, A. G.; Sigman, M. S. Considerations in Pursuing Reaction Scope Generality. Angew. Chem. Int. Ed. 2025 e202511091.202511091 https://doi.org/10.1002/anie.202511091

90. Hall, J. R.; Romer, N. P.; Spiller, T.; Sigman, M. S.; Sanford, M. S., 2025. Pd-Catalyzed Desulfonylative Fluorination of Electron Deficient (Hetero) Aryl Sulfonyl Fluorides. Chem. Sci. 2025, 16, 18936–18941. https://doi.org/10.1039/D5SC00912J

DOI of dataset(s):https://doi.org/10.5281/zenodo.19560180

89. Stenfors, B. A.; Cadge, J. A.; Aikonen, S.; Luchini, G.; Wahlers, J.; Koh, K. H.; Murronen, M.; Menche, M.; Pfeifle, M.; Keto, A.; Paton, R.; Sigman, M. S.; Wiest, O. “Conformation Dependent Features of Bisphosphine Ligand” J. Org. Chem. 2025, 90, 13874–13884. https://doi.org/10.1021/acs.joc.5c01682

DOI of dataset(s): https://doi.org/10.5281/zenodo.17086568

88. Cadge, J. A.; Lozano, C.; Merriman, M. T.; Oblad, P.; Sigman, M. S.; Reisman, S. E. A Data Science-Guided Approach for the Development of Nickel-Catalyzed Homo-Diels–Alder Reactions. J. Am. Chem. Soc. 2025, 147, 31175–31186. https://doi.org/10.1021/jacs.5c09948

87. Novicki, J. R.; Teeter, M. D.; Baldwin, N. J.; Am Ende, C. W.; Puleo, T. R.; Richardson, A. D.; Ball, N. D. Sulfur fluoride exchange with carbon pronucleophiles. Chem. Sci. 2025, 16, 16063–16069. https://doi.org/10.1039/D5SC03893F

86. Guo, K.; Liu, Z.; Guo, Z.; Nan, B.; Isayev, O.; Chawla, N. V.; Wiest, O.; Zhang, X. Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions 34th ACM Intl. Conf. Inf. Knowl. Man. (CIKM’25) 2025, 791–805. https://doi.org/10.1145/3746252.3761323

DOI of dataset(s):https://doi.org/10.5281/zenodo.19560174

85. Shen, Y. Tian, Y.; Ju, C.-W.; Wiest, O.; Zhang, X. Towards Few-shot Chemical Reaction Outcome Prediction 34th ACM Intl. Conf. Inf. Knowl. Man. (CIKM’25) 2025, 2599. https://doi.org/10.1145/3746252.3761236

84. Le, K.; Guo, Z.; Dong, K.; Huang, X.; Nan, B.; Iyer, R.; Zhang, X.; Wiest, O.; Wang, W.; Hua, T.; Chawla, N. V. MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension. Proc. 2025 ACM SIGKDD Intl. Conf. Knowl. Disc. Data Min (MLoG-GenAI@KDD ’25). https://doi.org/10.48550/arXiv.2406.06777

83. Ickes, A. R.; Liles, J. P.; Borlinghaus, N.; Henle, J.; Swiatowiec, R., Prakash Kaushik, N.; Braje, W. M.; Harper, K. C.; Shekhar, S. Sigman, M. S. Leveraging Data Science to Elucidate Ligand Features for Pd-Catalyzed Enantioretentive N-Arylations of Cyclic α-Substituted Amines in Aqueous Media. J. Am. Chem. Soc. 2025, 147, 28981–28992. https://doi.org/10.1021/jacs.5c07224

82. Ma, Y.; Tian, Y.; Moniz, N.; Chawla, N. V. Class-imbalanced learning on graphs: A survey. ACM Comp. Surv., 2025, 57, 1–16. https://doi.org/10.1145/3718734

81. Bartholomew, G. L.; Karas, L. J.; Eason, R. M.; Yeung, C. S.; Sigman, M. S.; Sarpong, R. Cheminformatic Analysis of Core-Atom Transformations in Pharmaceutically Relevant Heteroaromatics. J. Med. Chem. 2025, 68, 6027–6040. https://doi.org/10.1021/acs.jmedchem.4c02839

80. Boiko, D. A.; Reschützegger, T.; Sachez-Lengeling, B.; Blau, S. M.; Gomes, G. Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs Nat. Mach. Intell. 2025, 7, 771–781. https://doi.org/10.1038/s42256-025-01031-9

79. MacKnight, R.; Boiko, D. A.; Regio J. E.; Gallegos, L. C.; Neukomm, T. A, Gomes, G. Rethinking chemical research in the age of large language models Nat. Comput. Sci. 2025. https://doi.org/10.1038/s43588-025-00811-y

78. Keto, A.; Guo, T.; Gonnheimer, N.; Zhang, X.; Krenske, E. H.; Wiest, O. Improving reaction prediction through chemically aware transfer learning. Digit. Discovery 2025, 4, 1232–1238. https://pubs.rsc.org/en/content/articlelanding/2025/dd/d4dd00412d

DOI of dataset(s): and. https://doi.org/10.5281/zenodo.15652034 https://doi.org/10.5281/zenodo.15652034

77. Yu, K.; Roh, J.; Li, Z.; Gao, W.; Wang, R.; Coley, C. W. Double-ended synthesis planning with goal-constrained bidirectional search. Adv. Neural Inf. Process. Syst. (NeurIPS) 2025, 112919–112949. https://nips.cc/virtual/2024/poster/95604

DOI of dataset(s):https://doi.org/10.5281/zenodo.15652114

76. Dong, K.; Guo, Z.; Chawla, N. V. Pure message passing can estimate common neighbor for link prediction. Adv. Neural Inf. Process. Syst. (NeurIPS) 2025, 73000–73035. https://proceedings.neurips.cc/paper_files/paper/2024/file/85970f7bbc821852c1d17052b88c2451-Paper-Conference.pdf

75. Schleinitz, J.; Carreteri-Cerdan, A.; Gurajapu, A.; Harnik, Y.; Lee, G.; Pandey, A.; Milo, A.; Reisman, S. E. Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates. J. Am. Chem. Soc. 2025, 147, 7476–7484. https://doi.org/10.1021/jacs.4c15902

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652232

74. Bartholomew, G. L.; Kim, S. F.; Oyamada, Y.; Sbordone, F.; Carroll, J. A.; Jurczyk, J. E.; Yeung, C. S.; Barner-Kowoliik, C.; Sarpong, R. Phototransposition of Indazoles to Benzimidazoles: Tautomer-Dependent Reactivity, Wavelength Dependence, and Continuous Flow Studies. Angew. Chem. Int. Ed. 2025, 64, e202423803. https://doi.org/10.1002/anie.202423803

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652175

73. Cook R.; Berente, N.; Schecter, A. Closing Time: The Impact of Transitivity on Organizational Instant Messaging Proc. 58th Hawaii Intl Conf. Sys. Sci. 2025, 58, 5798. https://doi.org/10.24251/hicss.2025.695

72. Casetti, N.; Nevatia, P.; Chen, J.; Schwaller, P.; Coley, C. W. Comment on "Molecular hypergraph neural networks” J. Chem. Phys. 2024, 161, 207101. https://doi.org/10.1063/5.0239722

71. Treacy, S. M.; Smith, A. L.; Bergman, R. G.; Raymond, K. N.; Toste, F. D. Supramolecular Catalyzed Cascade Reduction of Azaarenes Interrogated via Data Science. J. Am. Chem. Soc. 2024, 146, 29792–29800. https://doi.org/10.1021/jacs.4c11482

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652211

70. Heafner, E. D.; Smith, A. L.; Craescu, C. V.; Raymond, K. N.; Berman, R. G.; Toste, F. D. Probing enantioinduction in confined chiral spaces through asymmetric oxime reductions. Chem 2025, 11, 102368. https://doi.org/10.1016/j.chempr.2024.11.006

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652163

69. Haas, B. C.; Kalyani, D.; Sigman, M. S. Applying statistical modeling strategies to sparse datasets in synthetic chemistry. Sci. Adv. 2025, 11, 18936. https://www.science.org/doi/epdf/10.1126/sciadv.adt3013

68. Haas, B.; Hardy, M. A.; Sowndarya, S. S.; Adams, K.; Coley, C. W.; Paton, R. S.; Sigman, M. S. Rapid Prediction of Conformationally-Dependent DFT-Level Descriptors using Graph Neural Networks for Carboxylic Acids and Alkyl Amines. Digit. Discovery 2025, 4, 222–233. https://doi.org/10.1039/D4DD00284A

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652211

67. Wright, B. A.; Okada, T.; Regni, A.; Luchini, G.; Sowndarya S. S. V.; Chaisan, N.; Kölbl, S.; Kim, S. F.; Paton, R. S.; Sarpong, R. Molecular Complexity-Inspired Synthetic Strategies toward the Calyciphylline A-Type Daphniphyllum Alkaloids Himalensine A and Daphenylline. J. Am. Chem. Soc. 2024, 146, 33130–33148. https://doi.org/10.1021/jacs.4c11252

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652266

66. Guo, K.; Nan, B.; Zhou, Y.; Guo, T.; Guo, Z.; Surve, M.; Liang, Z.; Chawla, N. V.; Wiest, O.; Zhang, X. Can LLMs Solve Molecular Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation. Adv. Neural Inf. Process. Syst. (NeurIPS24) 2024, 134721–134746. https://proceedings.neurips.cc/paper_files/paper/2024/file/f2b9e8e7a36d43ddfd3d55113d56b1e0-Paper-Datasets_and_Benchmarks_Track.pdf

DOI of dataset(s):https://doi.org:/10.5281/zenodo.15652061

65. Huang, X.; Surve, M.; Liu, Y.; Luo, T.; Wiest, O.; Zhang, X.; Chawla, N. V. Application of large language models in chemistry reaction, data extraction, and cleaning. CIKM’24: Proc.33rd ACM Intl.Conf. Inf. Knowl. Manag. 2024, 33, 3797–3801. https://doi.org/10.1145/3627673.3679874

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652049

64. Feng, K.; Raguram, E. R.; Howard, J. R.; Peters, E.; Liu, C.; Sigman, M. S.; Buchwald, S. L. Development of a Deactivation-Resistant Dialkylbiarylphosphine Ligand for Pd-Catalyzed Arylation of Secondary Amines. J. Am. Chem. Soc. 2024, 146, 26609–26615. https://pubs.acs.org/doi/10.1021/jacs.4c09667

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652127

63. Park, Y.; Slshafei, F. H.; Silva De Moraes, L.; Hernandez Rodriguez, I.; Deem, M. W.; Nelson, H. M.; Davis, M. E.; High-Silica, Enantiomerically Enriched STW-Type Molecular Sieves. Chem. Mater. 2024, 36, 10552–10559. https://pubs.acs.org/doi/full/10.1021/acs.chemmater.4c01792

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652199

62. Wright, B. A.; Sarpong, R. Molecular Complexity as a Driving Force for the Advancement of Organic Synthesis. Nat. Rev. Chem. 2024, 8, 776–792. https://www.nature.com/articles/s41570-024-00645-8

61. Gardner, K. E.; De Lescure, L.; Hardy, M. A.; Tan, J.; Sigman, M. S.; Paton, R. S.; Sarpong, R. Modular synthesis of aryl amines from 3-alkynyl-2-pyrones. Chem. Sci. 2024, 15, 15632–15638. https://pubs.rsc.org/en/content/articlelanding/2024/sc/d4sc04885g

DOI of dataset(s): https://doi.org/10.5281/zenodo.15652256

60. LeSueur, A.; Tao, N.; Doyle, A.; Sigman, M. Multi-Threshold Analysis for Chemical Space Mapping of Ni-Catalyzed Suzuki-Miyaura Couplings. Eur. J. Org. Chem. 2024, 27, e202400428. https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/ejoc.202400428

DOI of dataset(s): https://doi.org:/10.5281/zenodo.15652094

59. Guo, T.; Chen, X.; Wang, Y.; Chang, R.; Pei, S.; Chawla, N. V.; Wiest, O.; Zhang, X. Large Language Model based Multi-Agents: A Survey of Progress and Challenges. Int. Joint Conf. Artif. Intell. (IJCAI-24) 2024, 33, 8048–8057. 2024/890. https://doi.org/10.24963/ijcai.2024/890

58. Ma, C.; Guo, T.; Yang, Q.; Chen, X.; Gao, X.’ Liang, S.; Chawla, N. V.; Zhang, X. A Property-Guided Diffusion model for Generating Molecular Graphs. IEEE Intl. Conf. Acoustics, Speech Sign Proc. (ICASSP) 2024, 2365–2369. https://doi.org/10.1109/ICASSP48485.2024.10447350

57. Raghavan, P.; Rago, A. J.; Verma, P.; Hassan, M. M.; Goshu, G. M.; Dombrowski, A. W.; Pandey, A.; Coley, C. W.; Wang, Y. Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie’s 15-Year Parallel Library Data Set. J. Am. Chem. Soc. 2024, 146, 15070–15084. https://doi.org/10.1021/jacs.4c00098

56. Wiest, O.; Bauer, C.; Helquist, P.; Norrby, P. O.; Genheden, S. Finding relevant retrosynthetic disconnections for stereocontrolled reactions. J. Chem. Inf. Model. 2024, 64, 5796–5805. https://doi.org/10.1021/acs.jcim.4c00370

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