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There is increasing adoption of artificial intelligence in drug discovery.
“SciBERT: Pretrained Language Model for Scientific Text”
Iz Beltagy, Kyle Lo and Arman Cohan · 1903
Earlier work this paper cites.
“SciBERT: Pretrained Language Model for Scientific Text”
Iz Beltagy, Kyle Lo and Arman Cohan · 1903
Earlier work this paper cites.
“SciBERT: Pretrained Language Model for Scientific Text”
Iz Beltagy, Kyle Lo and Arman Cohan · 1903
Earlier work this paper cites.
“SciBERT: Pretrained Language Model for Scientific Text”
Iz Beltagy, Kyle Lo and Arman Cohan · 1903
Earlier work this paper cites.
“Drug discovery: a historical perspective”
Jürgen Drews · 1964
Earlier work this paper cites.
“Drug discovery: a historical perspective”
Jürgen Drews · 1964
Earlier work this paper cites.
“Hydroxylation-Induced Migration: The NIH Shift: Recent experiments reveal an unexpected and general result of enzymatic hydroxylation of aromatic compounds.”
Gordon Guroff, Jean Renson, Sidney Udenfriend, John Daly, Donald Jerina and Bernhard Witkop · 1967
Earlier work this paper cites.
“Hydroxylation-Induced Migration: The NIH Shift: Recent experiments reveal an unexpected and general result of enzymatic hydroxylation of aromatic compounds.”
Gordon Guroff, Jean Renson, Sidney Udenfriend, John Daly, Donald Jerina and Bernhard Witkop · 1967
Earlier work this paper cites.
“Hydroxylation-Induced Migration: The NIH Shift: Recent experiments reveal an unexpected and general result of enzymatic hydroxylation of aromatic compounds.”
Gordon Guroff, Jean Renson, Sidney Udenfriend, John Daly, Donald Jerina and Bernhard Witkop · 1967
Earlier work this paper cites.
“Hydroxylation-Induced Migration: The NIH Shift: Recent experiments reveal an unexpected and general result of enzymatic hydroxylation of aromatic compounds.”
Gordon Guroff, Jean Renson, Sidney Udenfriend, John Daly, Donald Jerina and Bernhard Witkop · 1967
Earlier work this paper cites.
“Partition coefficients and their uses”
Albert Leo, Corwin Hansch and David Elkins · 1971
Earlier work this paper cites.
“Partition coefficients and their uses”
Albert Leo, Corwin Hansch and David Elkins · 1971
Earlier work this paper cites.
“Synthesis and Structure-Activity Relationships of Acetylcholinesterase Inhibitors: 1-Benzyl-4-[(5,6-dimethoxy-1-oxoindan-2-yl)methyl]piperidine Hydrochloride and Related Compounds” PMID: 7490731
Hachiro Sugimoto, Youichi Iimura, Yoshiharu Yamanishi and Kiyomi Yamatsu · 1995
Earlier work this paper cites.
“Synthesis and Structure-Activity Relationships of Acetylcholinesterase Inhibitors: 1-Benzyl-4-[(5,6-dimethoxy-1-oxoindan-2-yl)methyl]piperidine Hydrochloride and Related Compounds” PMID: 7490731
Hachiro Sugimoto, Youichi Iimura, Yoshiharu Yamanishi and Kiyomi Yamatsu · 1995
Earlier work this paper cites.
“Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94”
Thomas Halgren · 1996
Earlier work this paper cites.
“Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94”
Thomas Halgren · 1996
Earlier work this paper cites.
“The use of the area under the ROC curve in the evaluation of machine learning algorithms”
Andrew Bradley · 1997
Earlier work this paper cites.
“The use of the area under the ROC curve in the evaluation of machine learning algorithms”
Andrew Bradley · 1997
Earlier work this paper cites.
“Substituted pyrazolyl benzenesulfonamides for the treatment of inflammation” US Patent 5,760,068
John Talley, Thomas Penning, Paul Collins, Donald Rogier, James Malecha, Julie Miyashiro, Stephen Bertenshaw, Ish Khanna, Matthew Graneto and Roland Rogers · 1998
Earlier work this paper cites.
“Alkynyl and azido-substituted 4-anilinoquinazolines” US Patent 5,747,498
Rodney Schnur and Lee Arnold · 1998
Earlier work this paper cites.
“Substituted pyrazolyl benzenesulfonamides for the treatment of inflammation” US Patent 5,760,068
John Talley, Thomas Penning, Paul Collins, Donald Rogier, James Malecha, Julie Miyashiro, Stephen Bertenshaw, Ish Khanna, Matthew Graneto and Roland Rogers · 1998
Earlier work this paper cites.
“Substituted pyrazolyl benzenesulfonamides for the treatment of inflammation” US Patent 5,760,068
John Talley, Thomas Penning, Paul Collins, Donald Rogier, James Malecha, Julie Miyashiro, Stephen Bertenshaw, Ish Khanna, Matthew Graneto and Roland Rogers · 1998
Earlier work this paper cites.
“Alkynyl and azido-substituted 4-anilinoquinazolines” US Patent 5,747,498
Rodney Schnur and Lee Arnold · 1998
Earlier work this paper cites.
“Substituted pyrazolyl benzenesulfonamides for the treatment of inflammation” US Patent 5,760,068
John Talley, Thomas Penning, Paul Collins, Donald Rogier, James Malecha, Julie Miyashiro, Stephen Bertenshaw, Ish Khanna, Matthew Graneto and Roland Rogers · 1998
Earlier work this paper cites.
“Unsupervised Data Base Clustering Based on Daylight’s Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets”
Darko Butina · 1999
Earlier work this paper cites.
“Unsupervised Data Base Clustering Based on Daylight’s Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets”
Darko Butina · 1999
Earlier work this paper cites.
“Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties” PMID: 11020286
Peter Ertl, Bernhard Rohde and Paul Selzer · 2000
Earlier work this paper cites.
“Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties” PMID: 11020286
Peter Ertl, Bernhard Rohde and Paul Selzer · 2000
Earlier work this paper cites.
“Scaffold hopping”
Hans-Joachim Böhm, Alexander Flohr and Martin Stahl · 2004
Earlier work this paper cites.
“Scaffold hopping”
Hans-Joachim Böhm, Alexander Flohr and Martin Stahl · 2004
Earlier work this paper cites.
“Zero-data learning of new tasks.”
Hugo Larochelle, Dumitru Erhan and Yoshua Bengio · 2008
Earlier work this paper cites.
“Zero-data learning of new tasks.”
Hugo Larochelle, Dumitru Erhan and Yoshua Bengio · 2008
Earlier work this paper cites.
“Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data” PMID: 19161251
Sebastian. Rohrer and Knut Baumann · 2009
Earlier work this paper cites.
“Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data” PMID: 19161251
Sebastian. Rohrer and Knut Baumann · 2009
Earlier work this paper cites.
“Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data” PMID: 19161251
Sebastian. Rohrer and Knut Baumann · 2009
Earlier work this paper cites.
“Maximum Unbiased Validation (MUV) Data Sets for Virtual Screening Based on PubChem Bioactivity Data” PMID: 19161251
Sebastian. Rohrer and Knut Baumann · 2009
Earlier work this paper cites.
“Targeted cancer therapies”
Saurabh Aggarwal · 2010
Earlier work this paper cites.
“AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading”
Oleg Trott and Arthur Olson · 2010
Earlier work this paper cites.
“Targeted cancer therapies”
Saurabh Aggarwal · 2010
Earlier work this paper cites.
“AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading”
Oleg Trott and Arthur Olson · 2010
Earlier work this paper cites.
“Principles of early drug discovery”
James Hughes, Stephen Rees, S Kalindjian and Karen Philpott · 2011
Earlier work this paper cites.
“Principles of early drug discovery”
James Hughes, Stephen Rees, S Kalindjian and Karen Philpott · 2011
Earlier work this paper cites.
“Principles of early drug discovery”
James Hughes, Stephen Rees, S Kalindjian and Karen Philpott · 2011
Earlier work this paper cites.
“Principles of early drug discovery”
James Hughes, Stephen Rees, S Kalindjian and Karen Philpott · 2011
Earlier work this paper cites.
“ZINC: a free tool to discover chemistry for biology”
John Irwin, Teague Sterling, Michael Mysinger, Erin Bolstad and Ryan Coleman · 2012
Earlier work this paper cites.
“Quantifying the chemical beauty of drugs”
G Bickerton, Gaia Paolini, Jérémy Besnard, Sorel Muresan and Andrew Hopkins · 2012
Earlier work this paper cites.
“Structural Characterization of Human Cytochrome P450 2C19: ACTIVE SITE DIFFERENCES BETWEEN P450s 2C8, 2C9, AND 2C19”
R Reynald, Stefaan Sansen, C Stout and Eric Johnson · 2012
Earlier work this paper cites.
“A Bayesian approach to in silico blood-brain barrier penetration modeling”
Ines Martins, Ana Teixeira, Luis Pinheiro and Andre Falcao · 2012
Earlier work this paper cites.
“ZINC: a free tool to discover chemistry for biology”
John Irwin, Teague Sterling, Michael Mysinger, Erin Bolstad and Ryan Coleman · 2012
Earlier work this paper cites.
“Quantifying the chemical beauty of drugs”
G Bickerton, Gaia Paolini, Jérémy Besnard, Sorel Muresan and Andrew Hopkins · 2012
Earlier work this paper cites.
“Structural Characterization of Human Cytochrome P450 2C19: ACTIVE SITE DIFFERENCES BETWEEN P450s 2C8, 2C9, AND 2C19”
R Reynald, Stefaan Sansen, C Stout and Eric Johnson · 2012
Earlier work this paper cites.
“A Bayesian approach to in silico blood-brain barrier penetration modeling”
Ines Martins, Ana Teixeira, Luis Pinheiro and Andre Falcao · 2012
Earlier work this paper cites.
“RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling”
Greg Landrum · 2013
Earlier work this paper cites.
“RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling”
Greg Landrum · 2013
Earlier work this paper cites.
“Tox21 Data Challenge 2014”
Tox21 Data Challenge · 2014
Earlier work this paper cites.
“Tox21 Data Challenge 2014”
Tox21 Data Challenge · 2014
Earlier work this paper cites.
“Convolutional networks on graphs for learning molecular fingerprints”
David Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik and Ryan Adams · 2015
Earlier work this paper cites.
“ZINC 15–ligand discovery for everyone”
Teague Sterling and John Irwin · 2015
Earlier work this paper cites.
“ZINC 15–ligand discovery for everyone”
Teague Sterling and John Irwin · 2015
Earlier work this paper cites.
“The SIDER database of drugs and side effects”
Michael Kuhn, Ivica Letunic, Lars Jensen and Peer Bork · 2015
Earlier work this paper cites.
“Aids antiviral screen data”, 2015
Daniel Zaharevitz · 2015
Earlier work this paper cites.
“Convolutional networks on graphs for learning molecular fingerprints”
David Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik and Ryan Adams · 2015
Earlier work this paper cites.
“ZINC 15–ligand discovery for everyone”
Teague Sterling and John Irwin · 2015
Earlier work this paper cites.
“ZINC 15–ligand discovery for everyone”
Teague Sterling and John Irwin · 2015
Earlier work this paper cites.
“The SIDER database of drugs and side effects”
Michael Kuhn, Ivica Letunic, Lars Jensen and Peer Bork · 2015
Earlier work this paper cites.
“Aids antiviral screen data”, 2015
Daniel Zaharevitz · 2015
Earlier work this paper cites.
“A data-driven approach to predicting successes and failures of clinical trials”
Kaitlyn Gayvert, Neel Madhukar and Olivier Elemento · 2016
Cited alongside, same era.
“A data-driven approach to predicting successes and failures of clinical trials”
Kaitlyn Gayvert, Neel Madhukar and Olivier Elemento · 2016
Cited alongside, same era.
“Attention is all you need”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser and Illia Polosukhin · 2017
Cited alongside, same era.
“Reproducible drug repurposing: When similarity does not suffice”
Emre Guney · 2017
Cited alongside, same era.
“Recent advances in scaffold hopping: miniperspective”
Ye Hu, Dagmar Stumpfe and Jurgen Bajorath · 2017
Cited alongside, same era.
“Attention is all you need”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser and Illia Polosukhin · 2017
“Contrastive multi-view representation learning on graphs”
Kaveh Hassani and Amir Khasahmadi · 2020
Later among the works it cites.
“Strategies for pre-training graph neural networks”
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande and Jure Leskovec · 2020
Later among the works it cites.
“Highly accurate protein structure prediction with AlphaFold”
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon.. Kohl, Andrew. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew. Senior, Koray Kavukcuoglu, Pushmeet Kohli and Demis Hassabis · 2021
Later among the works it cites.
“E (n) equivariant graph neural networks”
Victor Satorras, Emiel Hoogeboom and Max Welling · 2021
Later among the works it cites.
“Geometric deep learning on molecular representations”
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Cited alongside, same era.
“Reproducible drug repurposing: When similarity does not suffice”
Emre Guney · 2017
Cited alongside, same era.
“Recent advances in scaffold hopping: miniperspective”
Ye Hu, Dagmar Stumpfe and Jurgen Bajorath · 2017
Cited alongside, same era.
“Practical model selection for prospective virtual screening”
Shengchao Liu, Moayad Alnammi, Spencer Ericksen, Andrew Voter, Gene Ananiev, James Keck, F Hoffmann, Scott Wildman and Anthony Gitter · 2018
Cited alongside, same era.
“MoleculeNet: a benchmark for molecular machine learning”
Zhenqin Wu, Bharath Ramsundar, Evan Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh Pappu, Karl Leswing and Vijay Pande · 2018
Cited alongside, same era.
“How powerful are graph neural networks?”
Keyulu Xu, Weihua Hu, Jure Leskovec and Stefanie Jegelka · 2018
Cited alongside, same era.
“Schnet–a deep learning architecture for molecules and materials”
Kristof Schütt, Huziel Sauceda, P-J Kindermans, Alexandre Tkatchenko and K-R Müller · 2018
Cited alongside, same era.
Kenneth Atz, Francesca Grisoni and Gisbert Schneider · 2021
Later among the works it cites.
“Learning transferable visual models from natural language supervision”
Alec Radford, Jong Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin and Jack Clark · 2021
Later among the works it cites.
“Glide: Towards photorealistic image generation and editing with text-guided diffusion models”
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever and Mark Chen · 2021
Later among the works it cites.
“PubChem in 2021: new data content and improved web interfaces”
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin Shoemaker, Paul Thiessen and Bo Yu · 2021
Later among the works it cites.
“Open-vocabulary object detection via vision and language knowledge distillation”
Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo and Yin Cui · 2021
Later among the works it cites.
“Molclr: Molecular contrastive learning of representations via graph neural networks”
Yuyang Wang, Jianren Wang, Zhonglin Cao and Amir Farimani · 2021
Later among the works it cites.
“PubChem in 2021: new data content and improved web interfaces”
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin Shoemaker, Paul Thiessen and Bo Yu · 2021
Later among the works it cites.
“Molclr: Molecular contrastive learning of representations via graph neural networks”
Yuyang Wang, Jianren Wang, Zhonglin Cao and Amir Farimani · 2021
Later among the works it cites.
“Highly accurate protein structure prediction with AlphaFold”
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon.. Kohl, Andrew. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew. Senior, Koray Kavukcuoglu, Pushmeet Kohli and Demis Hassabis · 2021
Later among the works it cites.
“E (n) equivariant graph neural networks”
Victor Satorras, Emiel Hoogeboom and Max Welling · 2021
Later among the works it cites.
“Geometric deep learning on molecular representations”
Kenneth Atz, Francesca Grisoni and Gisbert Schneider · 2021
Later among the works it cites.
“Learning transferable visual models from natural language supervision”
Alec Radford, Jong Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin and Jack Clark · 2021
Later among the works it cites.
“Glide: Towards photorealistic image generation and editing with text-guided diffusion models”
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever and Mark Chen · 2021
Later among the works it cites.
“PubChem in 2021: new data content and improved web interfaces”
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin Shoemaker, Paul Thiessen and Bo Yu · 2021
Later among the works it cites.
“Open-vocabulary object detection via vision and language knowledge distillation”
Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo and Yin Cui · 2021
Later among the works it cites.
“Molclr: Molecular contrastive learning of representations via graph neural networks”
Yuyang Wang, Jianren Wang, Zhonglin Cao and Amir Farimani · 2021
Later among the works it cites.
“PubChem in 2021: new data content and improved web interfaces”
Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin Shoemaker, Paul Thiessen and Bo Yu · 2021
Later among the works it cites.
“Molclr: Molecular contrastive learning of representations via graph neural networks”
Yuyang Wang, Jianren Wang, Zhonglin Cao and Amir Farimani · 2021
Later among the works it cites.
“Has artificial intelligence impacted drug discovery?”
Atanas Patronov, Kostas Papadopoulos and Ola Engkvist · 2022
Closest in time.
“AI in small-molecule drug discovery: A coming wave”
Madura Jayatunga, Wen Xie, Ludwig Ruder, Ulrik Schulze and Christoph Meier · 2022
Closest in time.
“Chemformer: a pre-trained transformer for computational chemistry”
Ross Irwin, Spyridon Dimitriadis, Jiazhen He and Esben Bjerrum · 2022
Closest in time.
“Retrieval-based Controllable Molecule Generation”
Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard Baraniuk and Anima Anandkumar · 2022
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“GraphCG: Unsupervised Discovery of Steerable Factors in Graphs”
Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou and Jian Tang · 2022
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Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren and Ding Xue · 2022
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“Molecular geometry pretraining with se (3)-invariant denoising distance matching”
Shengchao Liu, Hongyu Guo and Jian Tang · 2022
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“Hierarchical text-conditional image generation with clip latents”
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu and Mark Chen · 2022
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“Pre-trained language models for interactive decision-making”
Shuang Li, Xavier Puig, Yilun Du, Clinton Wang, Ekin Akyurek, Antonio Torralba, Jacob Andreas and Igor Mordatch · 2022
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“Minedojo: Building open-ended embodied agents with internet-scale knowledge”
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu and Anima Anandkumar · 2022
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“A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals”
Zheni Zeng, Yuan Yao, Zhiyuan Liu and Maosong Sun · 2022
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“Pre-training Molecular Graph Representation with 3D Geometry”
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo and Jian Tang · 2022
Closest in time.
“GEOM, energy-annotated molecular conformations for property prediction and molecular generation”
Simon Axelrod and Rafael Gomez-Bombarelli · 2022
Closest in time.
“Chemformer: a pre-trained transformer for computational chemistry”
Ross Irwin, Spyridon Dimitriadis, Jiazhen He and Esben Bjerrum · 2022
Closest in time.
“Pre-training Molecular Graph Representation with 3D Geometry”
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo and Jian Tang · 2022
Closest in time.
“GEOM, energy-annotated molecular conformations for property prediction and molecular generation”
Simon Axelrod and Rafael Gomez-Bombarelli · 2022
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“Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding”
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Ghasemipour, Burcu Ayan, S Mahdavi and Rapha Lopes · 2022
Closest in time.
“GraphCG: Unsupervised Discovery of Steerable Factors in Graphs”
Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou and Jian Tang · 2022
Closest in time.
“Evaluating Self-Supervised Learning for Molecular Graph Embeddings”, 2022
Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt Kusner, Joan Lasenby and Qi Liu · 2022
Closest in time.
“Has artificial intelligence impacted drug discovery?”
Atanas Patronov, Kostas Papadopoulos and Ola Engkvist · 2022
Closest in time.
“AI in small-molecule drug discovery: A coming wave”
Madura Jayatunga, Wen Xie, Ludwig Ruder, Ulrik Schulze and Christoph Meier · 2022
Closest in time.
“Chemformer: a pre-trained transformer for computational chemistry”
Ross Irwin, Spyridon Dimitriadis, Jiazhen He and Esben Bjerrum · 2022
Closest in time.
“Retrieval-based Controllable Molecule Generation”
Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard Baraniuk and Anima Anandkumar · 2022
Closest in time.
“GraphCG: Unsupervised Discovery of Steerable Factors in Graphs”
Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou and Jian Tang · 2022
Closest in time.
Yuanfeng Ji, Lu Zhang, Jiaxiang Wu, Bingzhe Wu, Long-Kai Huang, Tingyang Xu, Yu Rong, Lanqing Li, Jie Ren and Ding Xue · 2022
Closest in time.
“Molecular geometry pretraining with se (3)-invariant denoising distance matching”
Shengchao Liu, Hongyu Guo and Jian Tang · 2022
Closest in time.
“Hierarchical text-conditional image generation with clip latents”
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu and Mark Chen · 2022
Closest in time.
“Pre-trained language models for interactive decision-making”
Shuang Li, Xavier Puig, Yilun Du, Clinton Wang, Ekin Akyurek, Antonio Torralba, Jacob Andreas and Igor Mordatch · 2022
Closest in time.
“Minedojo: Building open-ended embodied agents with internet-scale knowledge”
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu and Anima Anandkumar · 2022
Closest in time.
“A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals”
Zheni Zeng, Yuan Yao, Zhiyuan Liu and Maosong Sun · 2022
Closest in time.
“Pre-training Molecular Graph Representation with 3D Geometry”
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo and Jian Tang · 2022
Closest in time.
“GEOM, energy-annotated molecular conformations for property prediction and molecular generation”
Simon Axelrod and Rafael Gomez-Bombarelli · 2022
Closest in time.
“Chemformer: a pre-trained transformer for computational chemistry”
Ross Irwin, Spyridon Dimitriadis, Jiazhen He and Esben Bjerrum · 2022
Closest in time.
“Pre-training Molecular Graph Representation with 3D Geometry”
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo and Jian Tang · 2022
Closest in time.
“GEOM, energy-annotated molecular conformations for property prediction and molecular generation”
Simon Axelrod and Rafael Gomez-Bombarelli · 2022
Closest in time.
“Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding”
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Ghasemipour, Burcu Ayan, S Mahdavi and Rapha Lopes · 2022
Closest in time.
“GraphCG: Unsupervised Discovery of Steerable Factors in Graphs”
Shengchao Liu, Chengpeng Wang, Weili Nie, Hanchen Wang, Jiarui Lu, Bolei Zhou and Jian Tang · 2022
Closest in time.
“Evaluating Self-Supervised Learning for Molecular Graph Embeddings”, 2022
Hanchen Wang, Jean Kaddour, Shengchao Liu, Jian Tang, Matt Kusner, Joan Lasenby and Qi Liu · 2022
Closest in time.
“Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval”
Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao and Anima Anandkumar · 2023
Closest in time.
“A group symmetric stochastic differential equation model for molecule multi-modal pretraining”
Shengchao Liu, Weitao Du, Zhi-Ming Ma, Hongyu Guo and Jian Tang · 2023
Closest in time.
“Multi-modal Molecule Structure-text Model for Text-based Editing and Retrieval”
Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao and Anima Anandkumar · 2023
Closest in time.
“A group symmetric stochastic differential equation model for molecule multi-modal pretraining”
Shengchao Liu, Weitao Du, Zhi-Ming Ma, Hongyu Guo and Jian Tang · 2023
Closest in time.
“Styleclip: Text-driven manipulation of stylegan imagery”
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or and Dani Lischinski · 2094
Closest in time.
“Styleclip: Text-driven manipulation of stylegan imagery”
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or and Dani Lischinski · 2094
Closest in time.