Phase II publications
Phase II Publications (since Sept 2022)
Note: Center PI names are in bold. Industry collaborator names are underlined
121. Sun, F., Huang, Z., Cao, Y., Luo, X., Wang, W., Sun, Y. DoMiNO: Decomposing Molecular Dynamics with Mult-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 ASAP. 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 Catalysis 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 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 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 ASAP 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. AI 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. 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://doi.org/10.1038/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. Nature Comp. 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. Nature Mach. Intel. 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. Dig. Disc. 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. Theor. Comp. 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. 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 doi.org/10.1021/acs.joc.5c01682
DOI of dataset(s): 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. 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 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) 2025 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., 2025. 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. 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. 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 Nature Mach. Intl. 2025, 7, 771-781 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 Nature Comp. Sci. 2025, 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. Dig. Disc, 2025, 4, 1232-1238 doi.org/10.1039/d4dd00412d
DOI of dataset(s): https://doi.org/10.5281/zenodo.15652034 and https://doi.org/10.5281/zenodo.15652073
77. Yu, K.; Roh, J.; Li, Z.; Gao, W.; Wang, R.; Coley, C. W. Double-ended synthesis planning with goal-constrained bidirectional search. Adv. Neur. Inf. Proc. Sys. (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. Neur. Inf. Proc. Sys. (NeurIPS) 2025, 73000-73035. https://proceedings.neurips.cc
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. doi.org/10.1021/jacs.4c15902
DOI of dataset(s): 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): 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 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. doi.org/10.1021/jacs.4c11482
DOI of dataset(s): doi.org/10.5281/zenodo.15652221
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 doi.org/10.1016/j.chempr.2024.11.006
DOI of dataset(s): 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 doi.org/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. Dig. Disc. 2025, 4, 222-233. doi.org/10.1039/D4DD00284A
DOI of dataset(s): 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. doi.org/10.1021/jacs.4c11252
DOI of dataset(s): 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. Neur. Inf. Proc. Sys. (NeurIPS24) 2024, 134721-134746. https://nips.cc/virtual/2024/poster/97472
DOI of dataset(s): 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 doi.org/10.1145/3627673.3679874
DOI of dataset(s): 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. doi.org/10.1021/jacs.4c09667
DOI of dataset(s): 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 doi.org/10.1021/acs.chemmater.4c01792
DOI of dataset(s): 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. doi:10.1038/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. doi.org/10.1039/d4sc04885g
DOI of dataset(s): 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. doi.org/10.1002-ejoc.202400428
DOI of dataset(s): 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. Intl. Joint Conf AI (IJCAI-24) 2024, 33, 8048-8057. 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. 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. 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. Mod. 2024, 64, 5796–5805 doi.org/10.1021/acs.jcim.4c00370
DOI of dataset(s): doi.org/10.5281/zenodo.15652135
55. Ma, Y.; Huang, X.; Nan, B.; Moniz, N. Zhang, X.; Wiest, O.; Chawla, N.V. “Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective” Proc. ACM WebConf (WWW’24) 2024, 790-793. doi.org/10.1145/3589335.3651470
54. de Moraes, L.S.; Burch, J.E.; Delgadillo, D.A.; Rodriguez, I.H.; Mai, H.; Smith, A.G.; Caille, S.; Walker, S.D.; Wurz, R.P.; Cee, V.J.; Rodriguez, J.A.; Gostovic, D.; Quasdorf, K.; Nelson, H.M. Structural Elucidation and Absolute Stereochemistry for Pharma Compounds Using MicroED. Org. Lett. 2024, 26, 6944–6949 doi.org/10.1021/acs.orglett.4c01865
DOI of dataset(s): doi.org/10.5281/zenodo.15652244
53. Wiesler, S.; Sennari, G.; Popescu, M.V.; Gardner, K.E.; Aida, K.; Paton, R.S.; Sarpong, R. Late-Stage Benzenoid-to-Troponoid” Skeletal Modification of the Cephalotanes Exemplified by the Total Synthesis of Harringtonolide. Nat. Commun. 2024, 15, 4125. doi.org/10.1038/s41467-024-48586-6
52. Keto, A.; Guo, T.; Zhang, X.; Krenske, E.; Wiest, O. “Data-Efficient, Chemistry-Aware Machine Learning Predictions of Diels-Alder Reactions” J. Am. Chem. Soc. 2024 146¸ 16052–16061. doi.org/10.1021/jacs.4c03131
DOI of dataset(s): doi.org/10.5281/zenodo.15652185
51. Romer, N.P.; Min, D.S.; Wang, J.Y.; Walroth, R.C.; Mack, K.A.; Sirois, L.E.; Gosselin, F.; Zell, D.; Doyle, A.G.; Sigman, M.S., 2024. Data Science Guided Multiobjective Optimization of a Stereoconvergent Nickel-Catalyzed Reduction of Enol Tosylates to Access Trisubstituted Alkenes. ACS Catalysis, 2024, 14, 4699-4708. doi.org/10.1021/acscatal.4c00650
50. Chen, J.; Guo, K.; Liu, Z.; Isayev, O.; Zhang, X. Uncertainty-Aware Yield Prediction with Multimodal Molecular Features. Proc. AAAI Conf. AI (AAAI’24) 2024 38, 8274-8282. doi.org/10.1609/aaai.v38i8.28668
49. Sigmund, L.M.; Sowndarya S.V., S.; Albers. A.; Erdmann, P.; Paton, R.S.; Greb. L. Predicting Lewis Acidity: Machine‐Learning the Fluoride Ion Affinity of p‐Block‐Atom‐based Molecules. Angew. Chem. Intl. Ed. 2024, 63, e202401084. doi.org/10.1002/anie.202401084
48. Wang, J.Y.; Stevens, J.M.; Kariofillis, S.K.; Tom, M.J.; Golden, D.L.; Li, J.; Tabora, J.E.; Parasram, M.; Shields, B,J.; Primer, D.N.; Hao, B. DelValle, D.; DiSomma, S.; Furman, A.; Zipp, G.G.; Melnikov, S.; Paulson, J.; Doyle, A.G. Identifying general reaction conditions by bandit optimization. Nature 2024, 626, 1025-1033. doi.org/10.1038/s41586-024-07021-y
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