| Data augmentation and transfer learning strategies for reaction prediction in low chemical data regimes Y Zhang, L Wang, X Wang, C Zhang, J Ge, J Tang, A Su, H Duan Organic Chemistry Frontiers 8 (7), 1415-1423, 2021 | 45 | 2021 |
| A database framework for rapid screening of structure-function relationships in PFAS chemistry A Su, K Rajan Scientific data 8 (1), 14, 2021 | 35 | 2021 |
| Immobilized cutinases: Preparation, solvent tolerance and thermal stability A Su, A Shirke, J Baik, Y Zou, R Gross Enzyme and microbial technology 116, 33-40, 2018 | 35 | 2018 |
| Comparative thermal inactivation analysis of Aspergillus oryzae and Thiellavia terrestris cutinase: Role of glycosylation AN Shirke, A Su, JA Jones, GL Butterfoss, MAG Koffas, JR Kim, RA Gross Biotechnology and Bioengineering 114 (1), 63-73, 2017 | 34 | 2017 |
| Influence of surface charge, binding site residues and glycosylation on Thielavia terrestris cutinase biochemical characteristics AN Shirke, D Basore, S Holton, A Su, E Baugh, GL Butterfoss, ... Applied microbiology and biotechnology 100, 4435-4446, 2016 | 31 | 2016 |
| Deep learning model for identifying critical structural motifs in potential endocrine disruptors A Mukherjee, A Su, K Rajan Journal of chemical information and modeling 61 (5), 2187-2197, 2021 | 26 | 2021 |
| A review on artificial intelligence enabled design, synthesis, and process optimization of chemical products for industry 4.0 C He, C Zhang, T Bian, K Jiao, W Su, KJ Wu, A Su Processes 11 (2), 330, 2023 | 21 | 2023 |
| SolvBERT for solvation free energy and solubility prediction: a demonstration of an NLP model for predicting the properties of molecular complexes J Yu, C Zhang, Y Cheng, YF Yang, YB She, F Liu, W Su, A Su Digital Discovery 2 (2), 409-421, 2023 | 20 | 2023 |
| Revealing cutinases’ capabilities as enantioselective catalysts A Su, T Tyrikos-Ergas, AN Shirke, Y Zou, AL Dooley, IV Pavlidis, RA Gross ACS Catalysis 8 (9), 7944-7951, 2018 | 14 | 2018 |
| Reproducing the invention of a named reaction: zero-shot prediction of unseen chemical reactions A Su, X Wang, L Wang, C Zhang, Y Wu, X Wu, Q Zhao, H Duan Physical Chemistry Chemical Physics 24 (17), 10280-10291, 2022 | 9 | 2022 |
| Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts A Su, X Zhang, C Zhang, D Ding, YF Yang, K Wang, YB She Physical Chemistry Chemical Physics 25 (15), 10536-10549, 2023 | 7 | 2023 |
| Study of Chemical Compositions and Anticancer Effects of Paris polyphylla var. Chinensis Leaves F Su, L Ye, Z Zhou, A Su, J Gu, Z Guo, P Zhu, W Su Molecules 27 (9), 2724, 2022 | 7 | 2022 |
| Cutinases as stereoselective catalysts: Specific activity and enantioselectivity of cutinases and lipases for menthol and its analogs A Su, S Kiokekli, M Naviwala, AN Shirke, IV Pavlidis, RA Gross Enzyme and microbial technology 133, 109467, 2020 | 7 | 2020 |
| Continuous heterogeneous synthesis of hexafluoroacetone and its machine learning-assisted optimization T Qi, G Luo, H Xue, F Su, J Chen, W Su, KJ Wu, A Su Journal of Flow Chemistry 13 (3), 337-346, 2023 | 6 | 2023 |
| Revolutionizing the structural design and determination of covalent–organic frameworks: principles, methods, and techniques Y Liu, X Liu, A Su, C Gong, S Chen, L Xia, C Zhang, X Tao, Y Li, Y Li, ... Chemical Society Reviews, 2024 | 5 | 2024 |
| Predicting band gaps of MOFs on small data by deep transfer learning with data augmentation strategies Z Zhang, C Zhang, Y Zhang, S Deng, YF Yang, A Su, YB She RSC advances 13 (25), 16952-16962, 2023 | 5 | 2023 |
| Providing direction for mechanistic inferences in radical cascade cyclization using a Transformer model J Xu, Y Zhang, J Han, A Su, H Qiao, C Zhang, J Tang, X Shen, B Sun, ... Organic Chemistry Frontiers 9 (9), 2498-2508, 2022 | 5 | 2022 |
| Exploring Deep Learning for Metalloporphyrins: Databases, Molecular Representations, and Model Architectures A Su, C Zhang, YB She, YF Yang Catalysts 12 (11), 1485, 2022 | 4 | 2022 |
| Optimizing telescoped heterogeneous catalysis with noise-resilient multi-objective Bayesian optimization G Luo, X Yang, W Su, T Qi, Q Xu, A Su | 2 | 2024 |
| Exploring Bayesian Optimization for Photocatalytic Reduction of CO2 Y Zhang, X Yang, C Zhang, Z Zhang, A Su, YB She Processes 11 (9), 2614, 2023 | 2 | 2023 |
Richard GrossRensselaer Polytechnic Institute在 rpi.edu 的电子邮件经过验证
