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ORCIDConnor W. Coley
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2020 – today
- 2023
[j23]Yujie Qian
, Jiang Guo, Zhengkai Tu, Zhening Li, Connor W. Coley, Regina Barzilay:
MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation. J. Chem. Inf. Model. 63(7): 1925-1934 (2023)- [j22]Yujie Qian, Jiang Guo, Zhengkai Tu, Connor W. Coley, Regina Barzilay:
RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing. J. Chem. Inf. Model. 63(13): 4030-4041 (2023) - [j21]Rocío Mercado, Steven M. Kearnes, Connor W. Coley:
Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data. J. Chem. Inf. Model. 63(14): 4253-4265 (2023) - [j20]Jenna C. Fromer, Connor W. Coley:
Computer-aided multi-objective optimization in small molecule discovery. Patterns 4(2): 100678 (2023) - [c13]Keir Adams, Connor W. Coley:
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design. ICLR 2023 - [i35]Samuel Goldman, John Bradshaw, Jiayi Xin, Connor W. Coley:
Prefix-tree Decoding for Predicting Mass Spectra from Molecules. CoRR abs/2303.06470 (2023) - [i34]Yujie Qian, Jiang Guo, Zhengkai Tu, Connor W. Coley, Regina Barzilay:
RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing. CoRR abs/2305.11845 (2023) - [i33]Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR abs/2307.08423 (2023) - 2022
- [j19]Min Htoo Lin, Zhengkai Tu, Connor W. Coley:
Improving the performance of models for one-step retrosynthesis through re-ranking. J. Cheminformatics 14(1): 15 (2022) - [j18]Jiang Guo, A. Santiago Ibanez-Lopez, Hanyu Gao, Victor Quach, Connor W. Coley, Klavs F. Jensen, Regina Barzilay:
Automated Chemical Reaction Extraction from Scientific Literature. J. Chem. Inf. Model. 62(9): 2035-2045 (2022) - [j17]Katherine S. Lim, Andrew G. Reidenbach, Bruce K. Hua, Jeremy W. Mason, Christopher J. Gerry, Paul A. Clemons, Connor W. Coley:
Machine Learning on DNA-Encoded Library Count Data Using an Uncertainty-Aware Probabilistic Loss Function. J. Chem. Inf. Model. 62(10): 2316-2331 (2022) - [j16]Zhengkai Tu, Connor W. Coley:
Permutation Invariant Graph-to-Sequence Model for Template-Free Retrosynthesis and Reaction Prediction. J. Chem. Inf. Model. 62(15): 3503-3513 (2022) - [j15]David E. Graff, Matteo Aldeghi, Joseph A. Morrone, Kirk E. Jordan, Edward O. Pyzer-Knapp, Connor W. Coley:
Self-Focusing Virtual Screening with Active Design Space Pruning. J. Chem. Inf. Model. 62(16): 3854-3862 (2022) - [j14]Matteo Aldeghi, David E. Graff, Nathan C. Frey, Joseph A. Morrone, Edward O. Pyzer-Knapp, Kirk E. Jordan, Connor W. Coley:
Roughness of Molecular Property Landscapes and Its Impact on Modellability. J. Chem. Inf. Model. 62(19): 4660-4671 (2022) - [j13]David E. Graff, Connor W. Coley:
pyscreener: A Python Wrapper for Computational Docking Software. J. Open Source Softw. 7(71): 3950 (2022) - [j12]Samuel Goldman, Ria Das, Kevin K. Yang, Connor W. Coley:
Machine learning modeling of family wide enzyme-substrate specificity screens. PLoS Comput. Biol. 18(2) (2022) - [c12]Keir Adams, Lagnajit Pattanaik, Connor W. Coley:
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations. ICLR 2022 - [c11]Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W. Coley, Jimeng Sun:
Differentiable Scaffolding Tree for Molecule Optimization. ICLR 2022 - [c10]Wenhao Gao, Rocío Mercado, Connor W. Coley:
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design. ICLR 2022 - [c9]Wenhao Gao, Tianfan Fu, Jimeng Sun, Connor W. Coley:
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization. NeurIPS 2022 - [c8]Tianfan Fu, Wenhao Gao, Connor W. Coley, Jimeng Sun:
Reinforced Genetic Algorithm for Structure-based Drug Design. NeurIPS 2022 - [i32]David E. Graff, Matteo Aldeghi, Joseph A. Morrone, Kirk E. Jordan, Edward O. Pyzer-Knapp, Connor W. Coley:
Self-focusing virtual screening with active design space pruning. CoRR abs/2205.01753 (2022) - [i31]Matteo Aldeghi, Connor W. Coley:
A graph representation of molecular ensembles for polymer property prediction. CoRR abs/2205.08619 (2022) - [i30]Yujie Qian, Zhengkai Tu, Jiang Guo, Connor W. Coley, Regina Barzilay:
Robust Molecular Image Recognition: A Graph Generation Approach. CoRR abs/2205.14311 (2022) - [i29]Wenhao Gao, Tianfan Fu, Jimeng Sun, Connor W. Coley:
Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization. CoRR abs/2206.12411 (2022) - [i28]Keir Adams, Connor W. Coley:
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design. CoRR abs/2210.04893 (2022) - [i27]Jenna C. Fromer, Connor W. Coley:
Computer-Aided Multi-Objective Optimization in Small Molecule Discovery. CoRR abs/2210.07209 (2022) - [i26]Divya Nori, Connor W. Coley, Rocío Mercado:
De novo PROTAC design using graph-based deep generative models. CoRR abs/2211.02660 (2022) - [i25]Tianfan Fu, Wenhao Gao, Connor W. Coley, Jimeng Sun:
Reinforced Genetic Algorithm for Structure-based Drug Design. CoRR abs/2211.16508 (2022) - 2021
- [j11]Hanyu Gao, Jean Pauphilet, Thomas J. Struble, Connor W. Coley, Klavs F. Jensen:
Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries. J. Chem. Inf. Model. 61(1): 493-504 (2021) - [j10]Jiang Guo, A. Santiago Ibanez-Lopez, Hanyu Gao, Victor Quach, Connor W. Coley, Klavs F. Jensen, Regina Barzilay:
Correction to Automated Chemical Reaction Extraction from Scientific Literature. J. Chem. Inf. Model. 61(8): 4124 (2021) - [j9]Esther Heid, Samuel Goldman, Karthik Sankaranarayanan, Connor W. Coley, Christoph Flamm, William H. Green Jr.:
EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates. J. Chem. Inf. Model. 61(10): 4949-4961 (2021) - [c7]Hangrui Bi, Hengyi Wang, Chence Shi, Connor W. Coley, Jian Tang, Hongyu Guo:
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction. ICML 2021: 904-913 - [c6]Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik:
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development. NeurIPS Datasets and Benchmarks 2021 - [c5]Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, Regina Barzilay:
Learning Graph Models for Retrosynthesis Prediction. NeurIPS 2021: 9405-9415 - [c4]Octavian Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green Jr., Tommi S. Jaakkola:
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles. NeurIPS 2021: 13757-13769 - [i24]Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W. Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik:
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics. CoRR abs/2102.09548 (2021) - [i23]Shuangjia Zheng, Tao Zeng, Chengtao Li, Binghong Chen, Connor W. Coley, Yuedong Yang, Ruibo Wu:
BioNavi-NP: Biosynthesis Navigator for Natural Products. CoRR abs/2105.13121 (2021) - [i22]Hangrui Bi, Hengyi Wang, Chence Shi, Connor W. Coley, Jian Tang, Hongyu Guo:
Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction. CoRR abs/2106.07801 (2021) - [i21]Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W. Coley, Regina Barzilay, Klavs F. Jensen, William H. Green Jr., Tommi S. Jaakkola:
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles. CoRR abs/2106.07802 (2021) - [i20]Katherine S. Lim, Andrew G. Reidenbach, Bruce K. Hua, Jeremy W. Mason, Christopher J. Gerry, Paul A. Clemons, Connor W. Coley:
Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function. CoRR abs/2108.12471 (2021) - [i19]Samuel Goldman, Ria Das, Kevin K. Yang, Connor W. Coley:
Machine learning modeling of family wide enzyme-substrate specificity screens. CoRR abs/2109.03900 (2021) - [i18]Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W. Coley, Jimeng Sun:
Differentiable Scaffolding Tree for Molecular Optimization. CoRR abs/2109.10469 (2021) - [i17]Keir Adams, Lagnajit Pattanaik, Connor W. Coley:
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations. CoRR abs/2110.04383 (2021) - [i16]Wenhao Gao, Rocío Mercado, Connor W. Coley:
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design. CoRR abs/2110.06389 (2021) - [i15]Zhengkai Tu, Connor W. Coley:
Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction. CoRR abs/2110.09681 (2021) - [i14]Nathan C. Frey, Siddharth Samsi, Joseph McDonald, Lin Li, Connor W. Coley, Vijay Gadepally:
Scalable Geometric Deep Learning on Molecular Graphs. CoRR abs/2112.03364 (2021) - [i13]Nathan C. Frey, Siddharth Samsi, Bharath Ramsundar, Connor W. Coley, Vijay Gadepally:
Bringing Atomistic Deep Learning to Prime Time. CoRR abs/2112.04977 (2021) - 2020
- [j8]Michael E. Fortunato, Connor W. Coley, Brian C. Barnes, Klavs F. Jensen:
Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning. J. Chem. Inf. Model. 60(7): 3398-3407 (2020) - [j7]Lior Hirschfeld, Kyle Swanson, Kevin Yang, Regina Barzilay, Connor W. Coley:
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction. J. Chem. Inf. Model. 60(8): 3770-3780 (2020) - [j6]Wenhao Gao, Connor W. Coley:
The Synthesizability of Molecules Proposed by Generative Models. J. Chem. Inf. Model. 60(12): 5714-5723 (2020) - [c3]Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Simon Blackburn, Karam M. J. Thomas, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio:
Learning to Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. ICML 2020: 3668-3679 - [i12]Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song:
Retrosynthesis Prediction with Conditional Graph Logic Network. CoRR abs/2001.01408 (2020) - [i11]Wenhao Gao, Connor W. Coley:
The Synthesizability of Molecules Proposed by Generative Models. CoRR abs/2002.07007 (2020) - [i10]Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen:
Autonomous discovery in the chemical sciences part I: Progress. CoRR abs/2003.13754 (2020) - [i9]Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen:
Autonomous discovery in the chemical sciences part II: Outlook. CoRR abs/2003.13755 (2020) - [i8]Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio:
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning. CoRR abs/2004.12485 (2020) - [i7]Lior Hirschfeld, Kyle Swanson, Kevin Yang, Regina Barzilay, Connor W. Coley:
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction. CoRR abs/2005.10036 (2020) - [i6]Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, Regina Barzilay:
Learning Graph Models for Template-Free Retrosynthesis. CoRR abs/2006.07038 (2020) - [i5]Lagnajit Pattanaik, Octavian-Eugen Ganea, Ian Coley, Klavs F. Jensen, William H. Green Jr., Connor W. Coley:
Message Passing Networks for Molecules with Tetrahedral Chirality. CoRR abs/2012.00094 (2020) - [i4]David E. Graff, Eugene I. Shakhnovich, Connor W. Coley:
Accelerating high-throughput virtual screening through molecular pool-based active learning. CoRR abs/2012.07127 (2020)
2010 – 2019
- 2019
- [j5]Connor W. Coley, William H. Green Jr., Klavs F. Jensen:
RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application. J. Chem. Inf. Model. 59(6): 2529-2537 (2019) - [j4]Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay:
Analyzing Learned Molecular Representations for Property Prediction. J. Chem. Inf. Model. 59(8): 3370-3388 (2019) - [j3]Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay:
Correction to Analyzing Learned Molecular Representations for Property Prediction. J. Chem. Inf. Model. 59(12): 5304-5305 (2019) - [c2]Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song:
Retrosynthesis Prediction with Conditional Graph Logic Network. NeurIPS 2019: 8870-8880 - [i3]John S. Schreck, Connor W. Coley, Kyle J. M. Bishop:
Learning retrosynthetic planning through self-play. CoRR abs/1901.06569 (2019) - [i2]Kevin Yang, Kyle Swanson, Wengong Jin, Connor W. Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi S. Jaakkola, Klavs F. Jensen, Regina Barzilay:
Are Learned Molecular Representations Ready For Prime Time? CoRR abs/1904.01561 (2019) - 2018
- [j2]Connor W. Coley, Luke Rogers, William H. Green Jr., Klavs F. Jensen:
SCScore: Synthetic Complexity Learned from a Reaction Corpus. J. Chem. Inf. Model. 58(2): 252-261 (2018) - 2017
- [j1]Connor W. Coley, Regina Barzilay, William H. Green Jr., Tommi S. Jaakkola, Klavs F. Jensen:
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction. J. Chem. Inf. Model. 57(8): 1757-1772 (2017) - [c1]Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola:
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. NIPS 2017: 2607-2616 - [i1]Wengong Jin, Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola:
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. CoRR abs/1709.04555 (2017)
Coauthor Index
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last updated on 2023-10-02 00:36 CEST by the dblp team
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