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Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, and AI models capable of expediting the process would be invaluable.
“Pre-training Graph Neural Networks”
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay. Pande and Jure Leskovec · 1905
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, Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan, Pushmeet Kohli, David. Jones, David Silver, Koray Kavukcuoglu and Demis Hassabis · 1923
Earlier work this paper cites.
“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
Earlier work this paper cites.
“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, Johannes Söding, Julie Thompson and Desmond Higgins · 2011
Earlier work this paper cites.
“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
Earlier work this paper cites.
“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
Earlier work this paper cites.
“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 · 2014
Earlier work this paper cites.
URL: https://www.epa.gov/chemical-research/toxicity-forecaster-toxcasttm-data
“ToxCast and Tox21 Summary Files from invitrodb_v3”, 2015 · 2015
Earlier work this paper cites.
“NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets”
Morten Nielsen and Massimo Andreatta · 2016
Earlier work this paper cites.
“RDKit: Open-Source Cheminformatics Software”, 2016
Greg Landrum · 2016
Earlier work this paper cites.
“Chemical reactions from US patents (1976-Sep2016)”, 2017
Daniel Lowe · 2017
Earlier work this paper cites.
“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
Earlier work this paper cites.
“Learning Graph-Level Representation for Drug Discovery”, 2017
Junying Li, Deng Cai and Xiaofei He · 2017
Earlier work this paper cites.
“DeepSynergy: predicting anti-cancer drug synergy with Deep Learning”
Kristina Preuer, Richard Lewis, Sepp Hochreiter, Andreas Bender, Krishna Bulusu and Günter Klambauer · 2017
Earlier work this paper cites.
“Fitness landscape of the human immunodeficiency virus envelope protein that is targeted by antibodies”
Raymond.. Louie, Kevin. Kaczorowski, John. Barton, Arup. Chakraborty and Matthew. McKay · 2018
Earlier work this paper cites.
“Improved methods for predicting peptide binding affinity to <span style="font-variant:small-caps;">MHC</span> class <span style="font-variant:small-caps;">II</span> molecules”
Kamillaærgaard Jensen, Massimo Andreatta, Paolo Marcatili, Søren Buus, Jason. Greenbaum, Zhen Yan, Alessandro Sette, Bjoern Peters and Morten Nielsen · 2018
Earlier work this paper cites.
“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, Yvonne. Evrard, Eric. Stahlberg and Rick. Stevens · 2018
Earlier work this paper cites.
“Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials”
Ann Lin, Christopher. Giuliano, Ann Palladino, Kristen. John, Connor Abramowicz, Monet Yuan, Erin. Sausville, Devon. Lukow, Luwei Liu, Alexander. Chait, Zachary. Galluzzo, Clara Tucker and Jason. Sheltzer · 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.
“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, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen and Regina Barzilay · 2019
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, Jerry Ma and Rob Fergus · 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.
“The chemfp project”
Andrew Dalke · 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.
“Analyzing Learned Molecular Representations for Property Prediction” PMID: 31361484
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen and Regina Barzilay · 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, Zhenqin Wu, Giana Cirolia, Hera Canaj, Manuel. Leonetti, Alexander Marson, Andrew. May and James Zou · 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.
“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.
“Language Models are Few-Shot Learners”
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever and Dario Amodei · 2020
Earlier work this paper cites.
“Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li and Peter. Liu · 2020
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, Hualiang Jiang and Mingyue Zheng · 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” PMID: 32077698
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, Zohar Bloom-Ackermann, Victoria. Tran, Anush Chiappino-Pepe, Ahmed. Badran, Ian. Andrews, Emma. Chory, George. Church, Eric. Brown, Tommi. Jaakkola, Regina Barzilay and James. Collins · 2020
Earlier work this paper cites.
“ATTfold: RNA Secondary Structure Prediction With Pseudoknots Based on Attention Mechanism”
Yili Wang, Yuanning Liu, Shuo Wang, Zhen Liu, Yubing Gao, Hao Zhang and Liyan Dong · 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.
“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 · 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
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