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AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making the search for new pharmaceuticals quicker, cheaper and more efficient.
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
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Concepts and applications of molecular similarity
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Qsar based on multiple linear regression and pls methods for the anti-hiv activity of a large group of hept derivatives
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Robust qsar models using bayesian regularized neural networks
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The nature of statistical learning theory
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The multiplicity of serotonin receptors: uselessly diverse molecules or an embarrassment of riches?
Roth, Bryan L, Lopez, Estelle, Patel, Shamil, and Kroeze, Wesley K · 2000
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Random forests
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Logistic regression
Kleinbaum, David G, Dietz, K, Gail, M, Klein, Mitchel, and Klein, Mitchell · 2002
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Random forest: A classification and regression tool for compound classification and qsar modeling
Svetnik, Vladimir, Liaw, Andy, Tong, Christopher, Culberson, J. Christopher, Sheridan, Robert P., and Feuston, Bradley P · 2003
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Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to adme
Glen, Robert C, Bender, Andreas, Arnby, Catrin H, Carlsson, Lars, Boyer, Scott, and Smith, James · 2006
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Relating protein pharmacology by ligand chemistry
Keiser, Michael J., Roth, Bryan L., Armbruster, Blaine N., Ernsberger, Paul, Irwin, John J., and Shoichet, Brian K · 2007
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Learning from noisy labels with deep neural networks: A survey
Song, Hwanjun, Kim, Minseok, Park, Dongmin, and Lee, Jae-Gil · 2007
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Domain generalization with optimal transport and metric learning
Zhou, Fan, Jiang, Zhuqing, Shui, Changjian, Wang, Boyu, and Chaib-draa, Brahim · 2007
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Assessment of programs for ligand binding affinity prediction
Kim, Ryangguk and Skolnick, Jeffrey · 2008
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A survey on transfer learning
Pan, Sinno Jialin and Yang, Qiang · 2009
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Influence relevance voting: an accurate and interpretable virtual high throughput screening method
Swamidass, S Joshua, Azencott, Chloé-Agathe, Lin, Ting-Wan, Gramajo, Hugo, Tsai, Shiou-Chuan, and Baldi, Pierre · 2009
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Pubchem: a public information system for analyzing bioactivities of small molecules
Wang, Yanli, Xiao, Jewen, Suzek, Tugba O, Zhang, Jian, Wang, Jiyao, and Bryant, Stephen H · 2009
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A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking
Ballester, Pedro J. and Mitchell, John B. O · 2010
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Extended-connectivity fingerprints
Rogers, David and Hahn, Mathew · 2010
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Trott, Oleg and Olson, Arthur J · 2010
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A call to arms: what you can do for computational drug discovery, 2011
Carlson, Heather A and Dunbar Jr, James B · 2011
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Retroxpert: Decompose retrosynthesis prediction like a chemist
Yan, Chaochao, Ding, Qianggang, Zhao, Peilin, Zheng, Shuangjia, Yang, Jinyu, Yu, Yang, and Huang, Junzhou · 2011
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The experimental uncertainty of heterogeneous public k i data
Kramer, Christian, Kalliokoski, Tuomo, Gedeck, Peter, and Vulpetti, Anna · 2012
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Fang, Chen, Xu, Ye, and Rockmore, Daniel N · 2013
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Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
Koes, David Ryan, Baumgartner, Matthew P, and Camacho, Carlos J · 2013
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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling, 2013
Landrum, Greg · 2013
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Computational methods in drug discovery
Sliwoski, Gregory, Kothiwale, Sandeepkumar, Meiler, Jens, and Lowe Jr., Edward W · 2013
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UniProt: a hub for protein information
Consortium, The UniProt · 2014
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Multi-task neural networks for qsar predictions, 2014
Dahl, George E., Jaitly, Navdeep, and Salakhutdinov, Ruslan · 2014
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Pdb-wide collection of binding data: current status of the pdbbind database
Liu, Zhihai, Li, Yan, Han, Li, Li, Jie, Liu, Jie, Zhao, Zhixiong, Nie, Wei, Liu, Yuchen, and Wang, Renxiao · 2014
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Deep domain confusion: Maximizing for domain invariance, 2014
Tzeng, Eric, Hoffman, Judy, Zhang, Ning, Saenko, Kate, and Darrell, Trevor · 2014
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Chembl web services: streamlining access to drug discovery data and utilities
Davies, Mark, Nowotka, Michał, Papadatos, George, Dedman, Nathan, Gaulton, Anna, Atkinson, Francis, Bellis, Louisa, and Overington, John P · 2015
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Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, David, Maclaurin, Dougal, Aguilera-Iparraguirre, Jorge, Gómez-Bombarelli, Rafael, Hirzel, Timothy, Aspuru-Guzik, Alán, and Adams, Ryan P · 2015
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Domain generalization for object recognition with multi-task autoencoders
Ghifary, Muhammad, Kleijn, W Bastiaan, Zhang, Mengjie, and Balduzzi, David · 2015
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Classification with noisy labels by importance reweighting
Liu, Tongliang and Tao, Dacheng · 2015
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Deep learning face attributes in the wild
Liu, Ziwei, Luo, Ping, Wang, Xiaogang, and Tang, Xiaoou · 2015
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Learning transferable features with deep adaptation networks
Long, Mingsheng, Cao, Yue, Wang, Jianmin, and Jordan, Michael · 2015
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Massively multitask networks for drug discovery
Ramsundar, Bharath, Kearnes, Steven, Riley, Patrick, Webster, Dale, Konerding, David, and Pande, Vijay · 2015
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An embarrassingly simple approach to zero-shot learning
Romera-Paredes, Bernardino and Torr, Philip · 2015
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Training convolutional networks with noisy labels
Sukhbaatar, Sainbayar, Bruna, Joan, Paluri, Manohar, Bourdev, Lubomir, and Fergus, Rob · 2015
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Learning with symmetric label noise: The importance of being unhinged
van Rooyen, Brendan, Menon, Aditya, and Williamson, Robert · 2015
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Wallach, Izhar, Dzamba, Michael, and Heifets, Abraham · 2015
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Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data
Aliper, Alexander, Plis, Sergey, Artemov, Artem, Ulloa, Alvaro, Mamoshina, Polina, and Zhavoronkov, Alex · 2016
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Auxiliary image regularization for deep cnns with noisy labels
Azadi, Samaneh, Feng, Jiashi, Jegelka, Stefanie, and Darrell, Trevor · 2016
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How consistent are publicly reported cytotoxicity data? large-scale statistical analysis of the concordance of public independent cytotoxicity measurements
Cortés-Ciriano, Isidro and Bender, Andreas · 2016
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Domain-adversarial training of neural networks
Ganin, Yaroslav, Ustinova, Evgeniya, Ajakan, Hana, Germain, Pascal, Larochelle, Hugo, Laviolette, Francois, Marchand, Mario, and Lempitsky, Victor · 2016
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Bindingdb in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology
Gilson, Michael K, Liu, Tiqing, Baitaluk, Michael, Nicola, George, Hwang, Linda, and Chong, Jenny · 2016
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Large-scale prediction of drug-target interactions from deep representations
Hu, Peng-Wei, Chan, Keith CC, and You, Zhu-Hong · 2016
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, Steven, McCloskey, Kevin, Berndl, Marc, Pande, Vijay, and Riley, Patrick · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, Thomas N and Welling, Max · 2016
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Can you teach old drugs new tricks?
Nosengo, Nicola · 2016
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An ensemble model of qsar tools for regulatory risk assessment
Pradeep, Prachi, Povinelli, Richard J, White, Shannon, and Merrill, Stephen J · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Sun, Baochen and Saenko, Kate · 2016
Cited alongside, same era.
Qsar modeling and prediction of drug-drug interactions
Zakharov, Alexey V, Varlamova, Ekaterina V, Lagunin, Alexey A, Dmitriev, Alexander V, Muratov, Eugene N, Fourches, Denis, Kuz’min, Victor E, Poroikov, Vladimir V, Tropsha, Alexander, and Nicklaus, Marc C · 2016
Cited alongside, same era.
In silico prediction of drug induced liver toxicity using substructure pattern recognition method
Zhang, Chen, Cheng, Feixiong, Li, Weihua, Liu, Guixia, Lee, Philip W, and Tang, Yun · 2016
Cited alongside, same era.
Computer-assisted retrosynthesis based on molecular similarity
Invariant rationalization
Chang, Shiyu, Zhang, Yang, Yu, Mo, and Jaakkola, Tommi · 2020
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Retro*: learning retrosynthetic planning with neural guided a* search
Chen, Binghong, Li, Chengtao, Dai, Hanjun, and Song, Le · 2020
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Improvement in admet prediction with multitask deep featurization
Feinberg, Evan N, Joshi, Elizabeth, Pande, Vijay S, and Cheng, Alan C · 2020
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Deep learning in protein structural modeling and design
Gao, Wenhao, Mahajan, Sai Pooja, Sulam, Jeremias, and Gray, Jeffrey J · 2020
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Model patching: Closing the subgroup performance gap with data augmentation
Goel, Karan, Gu, Albert, Li, Yixuan, and Ré, Christopher · 2020
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Coley, Connor W, Rogers, Luke, Green, William H, and Jensen, Klavs F · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, Justin, Schoenholz, Samuel S, Riley, Patrick F, Vinyals, Oriol, and Dahl, George E · 2017
Cited alongside, same era.
Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties
Goh, Garrett B, Hodas, Nathan O, Siegel, Charles, and Vishnu, Abhinav · 2017
Cited alongside, same era.
Temporal ensembling for semi-supervised learning
Laine, Samuli and Aila, Timo · 2017
Cited alongside, same era.
Deeper, broader and artier domain generalization
Li, Da, Yang, Yongxin, Song, Yi-Zhe, and Hospedales, Timothy M · 2017
Cited alongside, same era.
Profile-qsar 2.0: Kinase virtual screening accuracy comparable to four-concentration ic50s for realistically novel compounds
Martin, Eric J., Polyakov, Valery R., Tian, Li, and Perez, Rolando C · 2017
Cited alongside, same era.
Optimizing distributions over molecular space. an objective-reinforced generative adversarial network for inverse-design chemistry (organic)
Sanchez-Lengeling, Benjamin, Outeiral, Carlos, Guimaraes, Gabriel L, and Aspuru-Guzik, Alán · 2017
Cited alongside, same era.
Gulrajani, Ishaan and Lopez-Paz, David · 2020
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A survey of label-noise representation learning: Past, present and future
Han, Bo, Yao, Quanming, Liu, Tongliang, Niu, Gang, Tsang, Ivor W, Kwok, James T, and Sugiyama, Masashi · 2020
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KinaseMD: kinase mutations and drug response database
Hu, Ruifeng, Xu, Haodong, Jia, Peilin, and Zhao, Zhongming · 2020
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Open Graph Benchmark: Datasets for Machine Learning on Graphs
Hu, Weihua, Fey, Matthias, Zitnik, Marinka, Dong, Yuxiao, Ren, Hongyu, Liu, Bowen, Catasta, Michele, and Leskovec, Jure · 2020
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Out-of-distribution generalization with maximal invariant predictor
Koyama, Masanori and Yamaguchi, Shoichiro · 2020
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Dividemix: Learning with noisy labels as semi-supervised learning
Li, Junnan, Socher, Richard, and Hoi, Steven CH · 2020
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Does label smoothing mitigate label noise?
Lukasik, Michal, Bhojanapalli, Srinadh, Menon, Aditya Krishna, and Kumar, Sanjiv · 2020
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Multi-view graph neural networks for molecular property prediction
Ma, Hehuan, Bian, Yatao, Rong, Yu, Huang, Wenbing, Xu, Tingyang, Xie, Weiyang, Ye, Geyan, and Huang, Junzhou · 2020
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Qsar without borders
Muratov, Eugene N, Bajorath, Jürgen, Sheridan, Robert P, Tetko, Igor V, Filimonov, Dmitry, Poroikov, Vladimir, Oprea, Tudor I, Baskin, Igor I, Varnek, Alexandre, Roitberg, Adrian, et al · 2020
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Learning to learn single domain generalization
Qiao, Fengchun, Zhao, Long, and Peng, Xi · 2020
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Self-supervised graph transformer on large-scale molecular data
Rong, Yu, Bian, Yatao, Xu, Tingyang, Xie, Weiyang, Wei, Ying, Huang, Wenbing, and Huang, Junzhou · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Sagawa, Shiori, Raghunathan, Aditi, Koh, Pang Wei, and Liang, Percy · 2020
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A deep learning approach to antibiotic discovery
Stokes, Jonathan M, Yang, Kevin, Swanson, Kyle, Jin, Wengong, Cubillos-Ruiz, Andres, Donghia, Nina M, MacNair, Craig R, French, Shawn, Carfrae, Lindsey A, Bloom-Ackermann, Zohar, et al · 2020
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Heterogeneous domain generalization via domain mixup
Wang, Yufei, Li, Haoliang, and Kot, Alex C · 2020
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Adversarial domain adaptation with domain mixup
Xu, Minghao, Zhang, Jian, Ni, Bingbing, Li, Teng, Wang, Chengjie, Tian, Qi, and Zhang, Wenjun · 2020
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Target identification among known drugs by deep learning from heterogeneous networks
Zeng, Xiangxiang, Zhu, Siyi, Lu, Weiqiang, Liu, Zehui, Huang, Jin, Zhou, Yadi, Fang, Jiansong, Huang, Yin, Guo, Huimin, Li, Lang, et al · 2020
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Coping with label shift via distributionally robust optimisation
Zhang, Jingzhao, Menon, Aditya, Veit, Andreas, Bhojanapalli, Srinadh, Kumar, Sanjiv, and Sra, Suvrit · 2020
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Maximum-entropy adversarial data augmentation for improved generalization and robustness
Zhao, Long, Liu, Ting, Peng, Xi, and Metaxas, Dimitris · 2020
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A comprehensive survey on transfer learning
Zhuang, Fuzhen, Qi, Zhiyuan, Duan, Keyu, Xi, Dongbo, Zhu, Yongchun, Zhu, Hengshu, Xiong, Hui, and He, Qing · 2020
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Accurate prediction of protein structures and interactions using a 3-track network
Baek, Minkyung, DiMaio, Frank, Anishchenko, Ivan, Dauparas, Justas, Ovchinnikov, Sergey, Lee, Gyu Rie, Wang, Jue, Cong, Qian, Kinch, Lisa N, Schaeffer, R Dustin, et al · 2021
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Deepbsp—a machine learning method for accurate prediction of protein–ligand docking structures
Bao, Jingxiao, He, Xiao, and Zhang, John ZH · 2021
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Artificial intelligence in drug discovery: what is realistic, what are illusions? part 1: ways to make an impact, and why we are not there yet
Bender, Andreas and Cortés-Ciriano, Isidro · 2021
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Proteinbert: A universal deep-learning model of protein sequence and function
Brandes, Nadav, Ofer, Dan, Peleg, Yam, Rappoport, Nadav, and Linial, Michal · 2021
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Fair mixup: Fairness via interpolation
Chuang, Ching-Yao and Mroueh, Youssef · 2021
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Artificial intelligence in drug discovery: applications and techniques
Deng, Jianyuan, Yang, Zhibo, Ojima, Iwao, Samaras, Dimitris, and Wang, Fusheng · 2021
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Network medicine framework for identifying drug-repurposing opportunities for covid-19
Gysi, Deisy Morselli, Do Valle, Ítalo, Zitnik, Marinka, Ameli, Asher, Gan, Xiao, Varol, Onur, Ghiassian, Susan Dina, Patten, JJ, Davey, Robert A, Loscalzo, Joseph, et al · 2021
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Towards non-iid image classification: A dataset and baselines
He, Yue, Shen, Zheyan, and Cui, Peng · 2021
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Therapeutics data commons: machine learning datasets and tasks for therapeutics
Huang, Kexin, Fu, Tianfan, Gao, Wenhao, Zhao, Yue, Roohani, Yusuf, Leskovec, Jure, Coley, Connor W, Xiao, Cao, Sun, Jimeng, and Zitnik, Marinka · 2021
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Machine and deep learning approaches for cancer drug repurposing
Issa, Naiem T, Stathias, Vasileios, Schürer, Stephan, and Dakshanamurthy, Sivanesan · 2021
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InteractionGraphNet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions
Jiang, Dejun, Hsieh, Chang-Yu, Wu, Zhenxing, Kang, Yu, Wang, Jike, Wang, Ercheng, Liao, Ben, Shen, Chao, Xu, Lei, Wu, Jian, Cao, Dongsheng, and Hou, Tingjun · 2021
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Highly accurate protein structure prediction with alphafold
Jumper, John, Evans, Richard, Pritzel, Alexander, Green, Tim, Figurnov, Michael, Ronneberger, Olaf, Tunyasuvunakool, Kathryn, Bates, Russ, Zidek, Augustin, Potapenko, Anna, et al · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Koh, Pang Wei, Sagawa, Shiori, Marklund, Henrik, Xie, Sang Michael, Zhang, Marvin, Balsubramani, Akshay, Hu, Weihua, Yasunaga, Michihiro, Phillips, Richard Lanas, Gao, Irena, Lee, Tony, David, Etienne, Stavness, Ian, Guo, Wei, Earnshaw, Berton, Haque, Imran, Beery, Sara M, Leskovec, Jure, Kundaje, Anshul, Pierson, Emma, Levine, Sergey, Finn, Chelsea, and Liang, Percy · 2021
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When is invariance useful in an out-of-distribution generalization problem ?, 2021
Koyama, Masanori and Yamaguchi, Shoichiro · 2021
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Quantifying sources of uncertainty in drug discovery predictions with probabilistic models
Lazic, Stanley E and Williams, Dominic P · 2021
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A review on compound-protein interaction prediction methods: Data, format, representation and model
Lim, Sangsoo, Lu, Yijingxiu, Cho, Chang Yun, Sung, Inyoung, Kim, Jungwoo, Kim, Youngkuk, Park, Sungjoon, and Kim, Sun · 2021
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Gnina 1.0: molecular docking with deep learning
McNutt, Andrew T, Francoeur, Paul, Aggarwal, Rishal, Masuda, Tomohide, Meli, Rocco, Ragoza, Matthew, Sunseri, Jocelyn, and Koes, David Ryan · 2021
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Pre-training of deep bidirectional protein sequence representations with structural information
Min, Seonwoo, Park, Seunghyun, Kim, Siwon, Choi, Hyun-Soo, Lee, Byunghan, and Yoon, Sungroh · 2021
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Artificial intelligence in drug discovery and development
Paul, Debleena, Sanap, Gaurav, Shenoy, Snehal, Kalyane, Dnyaneshwar, Kalia, Kiran, and Tekade, Rakesh K · 2021
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A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to covid-19 drug repurposing
Pham, Thai-Hoang, Qiu, Yue, Zeng, Jucheng, Xie, Lei, and Zhang, Ping · 2021
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Extending the wilds benchmark for unsupervised adaptation
Sagawa, Shiori, Koh, Pang Wei, Lee, Tony, Gao, Irena, Xie, Sang Michael, Shen, Kendrick, Kumar, Ananya, Hu, Weihua, Yasunaga, Michihiro, Marklund, Henrik, et al · 2021
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E(n) equivariant normalizing flows for molecule generation in 3d
Satorras, Victor Garcia, Hoogeboom, Emiel, Fuchs, Fabian B, Posner, Ingmar, and Welling, Max · 2021
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Open domain generalization with domain-augmented meta-learning
Shu, Yang, Cao, Zhangjie, Wang, Chenyu, Wang, Jianmin, and Long, Mingsheng · 2021
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FS-mol: A few-shot learning dataset of molecules
Stanley, Megan, Bronskill, John F, Maziarz, Krzysztof, Misztela, Hubert, Lanini, Jessica, Segler, Marwin, Schneider, Nadine, and Brockschmidt, Marc · 2021
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Chemical-reaction-aware molecule representation learning
Wang, Hongwei, Li, Weijiang, Jin, Xiaomeng, Cho, Kyunghyun, Ji, Heng, Han, Jiawei, and Burke, Martin D · 2021
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Ood-bench: Benchmarking and understanding out-of-distribution generalization datasets and algorithms
Ye, Nanyang, Li, Kaican, Hong, Lanqing, Bai, Haoyue, Chen, Yiting, Zhou, Fengwei, and Li, Zhenguo · 2021
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Do transformers really perform bad for graph representation?
Ying, Chengxuan, Cai, Tianle, Luo, Shengjie, Zheng, Shuxin, Ke, Guolin, He, Di, Shen, Yanming, and Liu, Tie-Yan · 2021
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Improving out-of-distribution robustness via selective augmentation, 2022
Yao, Huaxiu, Wang, Yu, Li, Sai, Zhang, Linjun, Liang, Weixin, Zou, James, and Finn, Chelsea · 2022
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