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Therapeutic development is a costly and high-risk endeavor that is often plagued by high failure rates.
“Language models are few-shot learners”
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry and Amanda Askell · 1901
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“Language models are few-shot learners”
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry and Amanda Askell · 1901
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“Frequent activating mutations of PIK3CA in ovarian clear cell carcinoma”
Kuan-Ting Kuo, Tsui-Lien Mao, Siân Jones, Emanuela Veras, Ayse Ayhan, Tian-Li Wang, Ruth Glas, Dennis Slamon, Victor Velculescu and Robert Kuman · 2009
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“Computer-aided prediction of rodent carcinogenicity by PASS and CISOC-PSCT”
Alexey Lagunin, Dmitrii Filimonov, Alexey Zakharov, Wei Xie, Ying Huang, Fucheng Zhu, Tianxiang Shen, Jianhua Yao and Vladimir Poroikov · 2009
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“Frequent activating mutations of PIK3CA in ovarian clear cell carcinoma”
Kuan-Ting Kuo, Tsui-Lien Mao, Siân Jones, Emanuela Veras, Ayse Ayhan, Tian-Li Wang, Ruth Glas, Dennis Slamon, Victor Velculescu and Robert Kuman · 2009
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“Computer-aided prediction of rodent carcinogenicity by PASS and CISOC-PSCT”
Alexey Lagunin, Dmitrii Filimonov, Alexey Zakharov, Wei Xie, Ying Huang, Fucheng Zhu, Tianxiang Shen, Jianhua Yao and Vladimir Poroikov · 2009
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“Extended-connectivity fingerprints”
David Rogers and Mathew Hahn · 2010
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“Extended-connectivity fingerprints”
David Rogers and Mathew Hahn · 2010
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“Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega”
Fabian Sievers, Andreas Wilm, David Dineen, Toby Gibson, Kevin Karplus, Weizhong Li, Rodrigo Lopez, Hamish McWilliam, Michael Remmert and Johannes Söding · 2011
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“A machine learning-based method to improve docking scoring functions and its application to drug repurposing”
Sarah Kinnings, Nina Liu, Peter Tonge, Richard Jackson, Lei Xie and Philip Bourne · 2011
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“Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega”
Fabian Sievers, Andreas Wilm, David Dineen, Toby Gibson, Kevin Karplus, Weizhong Li, Rodrigo Lopez, Hamish McWilliam, Michael Remmert and Johannes Söding · 2011
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“A machine learning-based method to improve docking scoring functions and its application to drug repurposing”
Sarah Kinnings, Nina Liu, Peter Tonge, Richard Jackson, Lei Xie and Philip Bourne · 2011
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“Fragment based drug design: from experimental to computational approaches”
Ashutosh Kumar, Arnout Voet and Kam Zhang · 2012
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“Fragment based drug design: from experimental to computational approaches”
Ashutosh Kumar, Arnout Voet and Kam Zhang · 2012
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“Characterization of the novel and specific PI3K α \alpha inhibitor NVP-BYL719 and development of the patient stratification strategy for clinical trials”
Christine Fritsch, Alan Huang, Christian Chatenay-Rivauday, Christian Schnell, Anupama Reddy, Manway Liu, Audrey Kauffmann, Daniel Guthy, Dirk Erdmann and Alain De · 2014
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“Characterization of the novel and specific PI3K α \alpha inhibitor NVP-BYL719 and development of the patient stratification strategy for clinical trials”
Christine Fritsch, Alan Huang, Christian Chatenay-Rivauday, Christian Schnell, Anupama Reddy, Manway Liu, Audrey Kauffmann, Daniel Guthy, Dirk Erdmann and Alain De · 2014
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“Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds”
Vinicius Alves, Eugene Muratov, Denis Fourches, Judy Strickland, Nicole Kleinstreuer, Carolina Andrade and Alexander Tropsha · 2015
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“Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds”
Vinicius Alves, Eugene Muratov, Denis Fourches, Judy Strickland, Nicole Kleinstreuer, Carolina Andrade and Alexander Tropsha · 2015
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“RDKit: Open-Source Cheminformatics Software”, 2016
Greg Landrum · 2016
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“RDKit: Open-Source Cheminformatics Software”, 2016
Greg Landrum · 2016
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“Attention is all you need”
A Vaswani · 2017
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“Learning graph-level representation for drug discovery”
Junying Li, Deng Cai and Xiaofei He · 2017
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“Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets”
Alexandru Korotcov, Valery Tkachenko, Daniel Russo and Sean Ekins · 2017
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“Attention is all you need”
A Vaswani · 2017
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“Learning graph-level representation for drug discovery”
Junying Li, Deng Cai and Xiaofei He · 2017
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“Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets”
Alexandru Korotcov, Valery Tkachenko, Daniel Russo and Sean Ekins · 2017
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“Insights into the mechanism of the PIK3CA E545K activating mutation using MD simulations”
Hari Leontiadou, Ioannis Galdadas, Christina Athanasiou and Zoe Cournia · 2018
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“Predicting tumor cell line response to drug pairs with deep learning”
Fangfang Xia, Maulik Shukla, Thomas Brettin, Cristina Garcia-Cardona, Judith Cohn, Jonathan Allen, Sergei Maslov, Susan Holbeck, James Doroshow and Yvonne Evrard · 2018
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“DeepSynergy: predicting anti-cancer drug synergy with Deep Learning”
Kristina Preuer, Richard Lewis, Sepp Hochreiter, Andreas Bender, Krishna Bulusu and Günter Klambauer · 2018
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“Insights into the mechanism of the PIK3CA E545K activating mutation using MD simulations”
Hari Leontiadou, Ioannis Galdadas, Christina Athanasiou and Zoe Cournia · 2018
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“Predicting tumor cell line response to drug pairs with deep learning”
Fangfang Xia, Maulik Shukla, Thomas Brettin, Cristina Garcia-Cardona, Judith Cohn, Jonathan Allen, Sergei Maslov, Susan Holbeck, James Doroshow and Yvonne Evrard · 2018
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“DeepSynergy: predicting anti-cancer drug synergy with Deep Learning”
Kristina Preuer, Richard Lewis, Sepp Hochreiter, Andreas Bender, Krishna Bulusu and Günter Klambauer · 2018
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“The chemfp project”
Andrew Dalke · 2019
Earlier work this paper cites.
“Graph convolutional neural networks for predicting drug-target interactions”
Wen Torng and Russ Altman · 2019
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“Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism”
Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen and Hualiang Jiang · 2019
Earlier work this paper cites.
“Analyzing learned molecular representations for property prediction”
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley and Miriam Mathea · 2019
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“Text summarization with pretrained encoders”
Yang Liu and Mirella Lapata · 2019
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“BERT: Pre-training of deep bidirectional transformers for language understanding”
Jacob-Wei Kenton and Lee Toutanova · 2019
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“Unified rational protein engineering with sequence-based deep representation learning”
Ethan Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi and George Church · 2019
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“Strategies for pre-training graph neural networks”
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande and Jure Leskovec · 2019
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“BCL:: Mol2D—a robust atom environment descriptor for QSAR modeling and lead optimization”
Oanh Vu, Jeffrey Mendenhall, Doaa Altarawy and Jens Meiler · 2019
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“Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties”
Alex Lind and Peter Anderson · 2019
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“Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells”
Ryan Leenay, Amirali Aghazadeh, Joseph Hiatt, David Tse, Theodore Roth, Ryan Apathy, Eric Shifrut, Judd Hultquist, Nevan Krogan and Zhenqin Wu · 2019
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“Analyzing learned molecular representations for property prediction”
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley and Miriam Mathea · 2019
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“Predicting retrosynthetic reactions using self-corrected transformer neural networks”
Shuangjia Zheng, Jiahua Rao, Zhongyue Zhang, Jun Xu and Yuedong Yang · 2019
Earlier work this paper cites.
“The chemfp project”
Andrew Dalke · 2019
Earlier work this paper cites.
“Graph convolutional neural networks for predicting drug-target interactions”
Wen Torng and Russ Altman · 2019
Earlier work this paper cites.
“Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism”
Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen and Hualiang Jiang · 2019
Earlier work this paper cites.
“Analyzing learned molecular representations for property prediction”
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley and Miriam Mathea · 2019
Earlier work this paper cites.
“Text summarization with pretrained encoders”
Yang Liu and Mirella Lapata · 2019
Earlier work this paper cites.
“BERT: Pre-training of deep bidirectional transformers for language understanding”
Jacob-Wei Kenton and Lee Toutanova · 2019
Earlier work this paper cites.
“Unified rational protein engineering with sequence-based deep representation learning”
Ethan Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi and George Church · 2019
Earlier work this paper cites.
“Strategies for pre-training graph neural networks”
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande and Jure Leskovec · 2019
Earlier work this paper cites.
“BCL:: Mol2D—a robust atom environment descriptor for QSAR modeling and lead optimization”
Oanh Vu, Jeffrey Mendenhall, Doaa Altarawy and Jens Meiler · 2019
Earlier work this paper cites.
“Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties”
Alex Lind and Peter Anderson · 2019
Earlier work this paper cites.
“Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells”
Ryan Leenay, Amirali Aghazadeh, Joseph Hiatt, David Tse, Theodore Roth, Ryan Apathy, Eric Shifrut, Judd Hultquist, Nevan Krogan and Zhenqin Wu · 2019
Earlier work this paper cites.
“Analyzing learned molecular representations for property prediction”
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley and Miriam Mathea · 2019
Earlier work this paper cites.
“Predicting retrosynthetic reactions using self-corrected transformer neural networks”
Shuangjia Zheng, Jiahua Rao, Zhongyue Zhang, Jun Xu and Yuedong Yang · 2019
Earlier work this paper cites.
“Accelerating therapeutics for opportunities in medicine: a paradigm shift in drug discovery”
Izumi Hinkson, Benjamin Madej and Eric Stahlberg · 2020
Earlier work this paper cites.
“Measuring massive multitask language understanding”
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song and Jacob Steinhardt · 2020
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“Combining docking pose rank and structure with deep learning improves protein–ligand binding mode prediction over a baseline docking approach”
Joseph Morrone, Jeffrey Weber, Tien Huynh, Heng Luo and Wendy Cornell · 2020
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“A deep learning approach to antibiotic discovery”
Jonathan Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina Donghia, Craig MacNair, Shawn French, Lindsey Carfrae and Zohar Bloom-Ackermann · 2020
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“Improved protein structure prediction using potentials from deep learning”
Andrew Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander Nelson and Alex Bridgland · 2020
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“Dual PI3K/mTOR inhibitor PKI-402 suppresses the growth of ovarian cancer cells by degradation of Mcl-1 through autophagy”
Xiaoqing Hu, Meihui Xia, Jiabin Wang, Huimei Yu, Jiannan Chai, Zejun Zhang, Yupei Sun, Jing Su and Liankun Sun · 2020
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“DeepPurpose: a deep learning library for drug–target interaction prediction”
Kexin Huang, Tianfan Fu, Lucas Glass, Marinka Zitnik, Cao Xiao and Jimeng Sun · 2020
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“Predicting antibody developability from sequence using machine learning”
Xingyao Chen, Thomas Dougherty, Chan Hong, Rachel Schibler, Yi Zhao, Reza Sadeghi, Naim Matasci, Yi-Chieh Wu and Ian Kerman · 2020
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“MIPDH: a novel computational model for predicting microRNA–mRNA interactions by DeepWalk on a heterogeneous network”
Leon Wong, Zhu-Hong You, Zhen-Hao Guo, Hai-Cheng Yi, Zhan-Heng Chen and Mei-Yuan Cao · 2020
Earlier work this paper cites.
“Accelerating therapeutics for opportunities in medicine: a paradigm shift in drug discovery”
Izumi Hinkson, Benjamin Madej and Eric Stahlberg · 2020
Earlier work this paper cites.
“Measuring massive multitask language understanding”
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song and Jacob Steinhardt · 2020
Earlier work this paper cites.
“Combining docking pose rank and structure with deep learning improves protein–ligand binding mode prediction over a baseline docking approach”
Joseph Morrone, Jeffrey Weber, Tien Huynh, Heng Luo and Wendy Cornell · 2020
Earlier work this paper cites.
“A deep learning approach to antibiotic discovery”
Jonathan Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina Donghia, Craig MacNair, Shawn French, Lindsey Carfrae and Zohar Bloom-Ackermann · 2020
Earlier work this paper cites.
“Improved protein structure prediction using potentials from deep learning”
Andrew Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander Nelson and Alex Bridgland · 2020
Earlier work this paper cites.
“Dual PI3K/mTOR inhibitor PKI-402 suppresses the growth of ovarian cancer cells by degradation of Mcl-1 through autophagy”
Xiaoqing Hu, Meihui Xia, Jiabin Wang, Huimei Yu, Jiannan Chai, Zejun Zhang, Yupei Sun, Jing Su and Liankun Sun · 2020
Earlier work this paper cites.
“DeepPurpose: a deep learning library for drug–target interaction prediction”
Kexin Huang, Tianfan Fu, Lucas Glass, Marinka Zitnik, Cao Xiao and Jimeng Sun · 2020
Earlier work this paper cites.
“Predicting antibody developability from sequence using machine learning”
Xingyao Chen, Thomas Dougherty, Chan Hong, Rachel Schibler, Yi Zhao, Reza Sadeghi, Naim Matasci, Yi-Chieh Wu and Ian Kerman · 2020
Earlier work this paper cites.
“MIPDH: a novel computational model for predicting microRNA–mRNA interactions by DeepWalk on a heterogeneous network”
Leon Wong, Zhu-Hong You, Zhen-Hao Guo, Hai-Cheng Yi, Zhan-Heng Chen and Mei-Yuan Cao · 2020
Earlier work this paper cites.
“Measuring massive multitask language understanding”
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song and Jacob Steinhardt · 2020
Earlier work this paper cites.
“Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development”
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor Coley, Cao Xiao, Jimeng Sun and Marinka Zitnik · 2021
Earlier work this paper cites.
“Machine learning of reaction properties via learned representations of the condensed graph of reaction”
Esther Heid and William Green · 2021
Earlier work this paper 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 and Anna Potapenko · 2021
Earlier work this paper cites.
“Highly accurate protein structure prediction for the human proteome”
Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer and Agata Laydon · 2021
Earlier work this paper cites.
“Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences”
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C Zitnick and Jerry Ma · 2021
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“FDA approval summary: alpelisib plus fulvestrant for patients with HR-positive, HER2-negative, PIK3CA-mutated, advanced or metastatic breast cancer”
Preeti Narayan, Tatiana Prowell, Jennifer Gao, Laura Fernandes, Emily Li, Xiling Jiang, Junshan Qiu, Jianghong Fan, Pengfei Song and Jingyu Yu · 2021
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“CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes”
Wojciech Plonka, Conrad Stork, Martin Šícho and Johannes Kirchmair · 2021
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“TrimNet: learning molecular representation from triplet messages for biomedicine”
Pengyong Li, Yuquan Li, Chang-Yu Hsieh, Shengyu Zhang, Xianggen Liu, Huanxiang Liu, Sen Song and Xiaojun Yao · 2021
Earlier work this paper cites.
“A novel method for data fusion over entity-relation graphs and its application to protein–protein interaction prediction”
Daniele Raimondi, Jaak Simm, Adam Arany and Yves Moreau · 2021
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“Validating ADME QSAR models using marketed drugs”
Vishal Siramshetty, Jordan Williams, Ðac-Trung Nguyen, Jorge Neyra, Noel Southall, Ewy Mathé, Xin Xu and Pranav Shah · 2021
Earlier work this paper cites.
“Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations”
Jacek Haneczok and Marcin Delijewski · 2021
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“COVID-19 multi-targeted drug repurposing using few-shot learning”
Yang Liu, You Wu, Xiaoke Shen and Lei Xie · 2021
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“CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles”
Abdul Karim, Matthew Lee, Thomas Balle and Abdul Sattar · 2021
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“TITAN: T-cell receptor specificity prediction with bimodal attention networks”
Anna Weber, Jannis Born and María Rodriguezínez · 2021
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“DeepPLA: a novel deep learning-based model for protein-ligand binding affinity prediction”, 2021
Bomin Wei and Xiang Gong · 2021
Earlier work this paper cites.
“Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development”
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor Coley, Cao Xiao, Jimeng Sun and Marinka Zitnik · 2021
Earlier work this paper cites.
“Machine learning of reaction properties via learned representations of the condensed graph of reaction”
Esther Heid and William Green · 2021
Earlier work this paper 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 and Anna Potapenko · 2021
Earlier work this paper cites.
“Highly accurate protein structure prediction for the human proteome”
Kathryn Tunyasuvunakool, Jonas Adler, Zachary Wu, Tim Green, Michal Zielinski, Augustin Žídek, Alex Bridgland, Andrew Cowie, Clemens Meyer and Agata Laydon · 2021
Earlier work this paper cites.
“Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences”
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C Zitnick and Jerry Ma · 2021
Earlier work this paper cites.
“FDA approval summary: alpelisib plus fulvestrant for patients with HR-positive, HER2-negative, PIK3CA-mutated, advanced or metastatic breast cancer”
Preeti Narayan, Tatiana Prowell, Jennifer Gao, Laura Fernandes, Emily Li, Xiling Jiang, Junshan Qiu, Jianghong Fan, Pengfei Song and Jingyu Yu · 2021
Cited alongside, same era.
“CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes”
Wojciech Plonka, Conrad Stork, Martin Šícho and Johannes Kirchmair · 2021
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“TrimNet: learning molecular representation from triplet messages for biomedicine”
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Cited alongside, same era.
“A novel method for data fusion over entity-relation graphs and its application to protein–protein interaction prediction”
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“Validating ADME QSAR models using marketed drugs”
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“Experiential co-learning of software-developing agents”
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“Catastrophic forgetting in deep learning: A comprehensive taxonomy”
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“P110 α \alpha inhibitor alpelisib exhibits a synergistic effect with pyrotinib and reverses pyrotinib resistant in HER2+ breast cancer”
Hao Chen, Yuhao Si, Jialiang Wen, Chunlei Hu, Erjie Xia, Yinghao Wang and Ouchen Wang · 2023
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“First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa”
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Cited alongside, same era.
“Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations”
Jacek Haneczok and Marcin Delijewski · 2021
Cited alongside, same era.
“COVID-19 multi-targeted drug repurposing using few-shot learning”
Yang Liu, You Wu, Xiaoke Shen and Lei Xie · 2021
Cited alongside, same era.
“CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles”
Abdul Karim, Matthew Lee, Thomas Balle and Abdul Sattar · 2021
Cited alongside, same era.
“TITAN: T-cell receptor specificity prediction with bimodal attention networks”
Anna Weber, Jannis Born and María Rodriguezínez · 2021
Cited alongside, same era.
“DeepPLA: a novel deep learning-based model for protein-ligand binding affinity prediction”, 2021
Bomin Wei and Xiang Gong · 2021
Cited alongside, same era.
“Why 90% of clinical drug development fails and how to improve it?”
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“Artificial intelligence foundation for therapeutic science”
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor Coley, Cao Xiao, Jimeng Sun and Marinka Zitnik · 2022
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“Predicting a Compounds Blood-Brain-Barrier Permeability with Lantern Pharma’s AI and ML Platform, RADR”, 2023
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“Improved predictions of antigen presentation and TCR recognition with MixMHCpred2. 2 and PRIME2. 0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes”
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“Breaking the barriers of data scarcity in drug–target affinity prediction”
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In GitHub repository
Euclia · 2023
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“Are large language models superhuman chemists?”
Adrian Mirza, Nawaf Alampara, Sreekanth Kunchapu, Martino Rios-Garcia, Benedict Emoekabu, Aswanth Krishnan, Tanya Gupta, Mara Schilling-Wilhelmi, Macjonathan Okereke and Anagha Aneesh · 2024
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“Learning to Reason with LLMs” Accessed: August 24, 2026, https://openai.com/index/learning-to-reason-with-llms/ , 2024
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“TDC-2: Multimodal foundation for therapeutic science”
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“Large language models for science and medicine”
Amalio Telenti, Michael Auli, Brian Hie, Cyrus Maher, Suchi Saria and John Ioannidis · 2024
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“Tx-LLM: A Large Language Model for Therapeutics”
Juan Chaves, Eric Wang, Tao Tu, Eeshit Vaishnav, Byron Lee, S Mahdavi, Christopher Semturs, David Fleet, Vivek Natarajan and Shekoofeh Azizi · 2024
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“Gemma: Open models based on gemini research and technology”
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“Gemma 2: Improving open language models at a practical size”
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Botao Yu, Frazier Baker, Ziqi Chen, Xia Ning and Huan Sun · 2024
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“MolE: a foundation model for molecular graphs using disentangled attention”
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