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Activity and property prediction models are the central workhorses in drug discovery and materials sciences, but currently they have to be trained or fine-tuned for new tasks.
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Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy
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Automatic generation of complementary descriptors with molecular graph networks
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Evaluating Virtual Screening Methods: Good and Bad Metrics for the “Early Recognition” Problem
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Importance of semantic representation: dataless classification
Chang, M.-W., Ratinov, L., Roth, D., and Srikumar, V · 2008
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Zero-data learning of new tasks
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
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Farhadi, A., Endres, I., Hoiem, D., and Forsyth, D · 2009
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Novel trends in high-throughput screening
Mayr, L. M. and Bojanic, D · 2009
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Machine learning in virtual screening
Melville, J. L., Burke, E. K., and Hirst, J. D · 2009
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Zero-shot learning with semantic output codes
Palatucci, M., Pomerleau, D., Hinton, G. E., and Mitchell, T. M · 2009
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A statistical framework to evaluate virtual screening
Zhao, W., Hevener, K. E., White, S. W., Lee, R. E., and Boyett, J. M · 2009
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New Substructure Filters for Removal of Pan Assay Interference Compounds (PAINS) from Screening Libraries and for Their Exclusion in Bioassays
Baell, J. B. and Holloway, G. A · 2010
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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A question of library design
Hajduk, P. J., Galloway, W. R., and Spring, D. R · 2011
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Molecular docking: a powerful approach for structure-based drug discovery
Meng, X.-Y., Zhang, H.-X., Mezei, M., and Cui, M · 2011
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Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets
van Westen, G. J., Wegner, J. K., IJzerman, A. P., van Vlijmen, H. W., and Bender, A · 2011
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Bioassay ontology (bao): a semantic description of bioassays and high-throughput screening results
Visser, U., Abeyruwan, S., Vempati, U., Smith, R. P., Lemmon, V., and Schürer, S. C · 2011
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Selecting, acquiring, and using small molecule libraries for high-throughput screening
Dandapani, S., Rosse, G., Southall, N., Salvino, J. M., and Thomas, C. J · 2012
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ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al · 2012
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A bayesian approach to in silico blood-brain barrier penetration modeling
Martins, I. F., Teixeira, A. L., Pinheiro, L., and Falcao, A. O · 2012
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Rethinking molecular similarity: comparing compounds on the basis of biological activity
Petrone, P. M., Simms, B., Nigsch, F., Lounkine, E., Kutchukian, P., Cornett, A., Deng, Z., Davies, J. W., Jenkins, J. L., and Glick, M · 2012
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Landrum, G · 2013
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Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules
Lusci, A., Pollastri, G., and Baldi, P · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J · 2013
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Multi-objective optimization methods in drug design
Nicolaou, C. A. and Brown, N · 2013
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Time-split cross-validation as a method for estimating the goodness of prospective prediction
Sheridan, R. P · 2013
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Multi-task neural networks for QSAR predictions
Dahl, G. E., Jaitly, N., and Salakhutdinov, R · 2014
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Facing the cold start problem in recommender systems
Lika, B., Kolomvatsos, K., and Hadjiefthymiades, S · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Deep learning as an opportunity in virtual screening
Unterthiner, T., Mayr, A., Klambauer, G., Steijaert, M., Wegner, J. K., Ceulemans, H., and Hochreiter, S · 2014
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Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening
Ain, Q. U., Aleksandrova, A., Roessler, F. D., and Ballester, P. J · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Massively multitask networks for drug discovery
Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., and Pande, V · 2015
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Understanding intermediate layers using linear classifier probes
Alain, G. and Bengio, Y · 2016
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Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes
Artemov, A. V., Putin, E., Vanhaelen, Q., Aliper, A., Ozerov, I. V., and Zhavoronkov, A · 2016
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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A data-driven approach to predicting successes and failures of clinical trials
Gayvert, K. M., Madhukar, N. S., and Elemento, O · 2016
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Highly Promiscuous Small Molecules from Biological Screening Assays Include Many Pan-Assay Interference Compounds but Also Candidates for Polypharmacology
Gilberg, E., Jasial, S., Stumpfe, D., Dimova, D., and Bajorath, J · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. and Welling, M · 2016
Cited alongside, same era.
The sider database of drugs and side effects
Kuhn, M., Letunic, I., Jensen, L. J., and Bork, P · 2016
Cited alongside, same era.
Deeptox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., and Hochreiter, S · 2016
Contrastive learning for debiased candidate generation in large-scale recommender systems
Zhou, C., Ma, J., Zhang, J., Zhou, J., and Yang, H · 2020
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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, A. and Cortés-Ciriano, I · 2021
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Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries
Edwards, C., Zhai, C., and Ji, H · 2021
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Few-shot graph learning for molecular property prediction
Guo, Z., Zhang, C., Yu, W., Herr, J., Wiest, O., Jiang, M., and Chawla, N. V · 2021
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Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., and Zitnik, M · 2021
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Cited alongside, same era.
Toxcast chemical landscape: paving the road to 21st century toxicology
Richard, A. M., Judson, R. S., Houck, K. A., Grulke, C. M., Volarath, P., Thillainadarajah, I., Yang, C., Rathman, J., Martin, M. T., Wambaugh, J. F., et al · 2016
Cited alongside, same era.
Filtering promiscuous compounds in early drug discovery: is it a good idea?
Senger, M. R., Fraga, C. A. M., Dantas, R. F., and Silva, F. P · 2016
Cited alongside, same era.
Low data drug discovery with one-shot learning
Altae-Tran, H., Ramsundar, B., Pappu, A. S., and Pande, V · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
Cited alongside, same era.
Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., and Chen, H · 2017
Cited alongside, same era.
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Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models
Jiang, D., Wu, Z., Hsieh, C.-Y., Chen, G., Liao, B., Wang, Z., Shen, C., Cao, D., Wu, J., and Hou, T · 2021
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Contrastive learning for recommender system
Liu, Z., Ma, Y., Ouyang, Y., and Xiong, Z · 2021
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Supervised contrastive learning over prototype-label embeddings for network intrusion detection
Lopez-Martin, M., Sanchez-Esguevillas, A., Arribas, J. I., and Carro, B · 2021
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Relative molecule self-attention transformer
Maziarka, Ł., Majchrowski, D., Danel, T., Gaiński, P., Tabor, J., Podolak, I., Morkisz, P., and Jastrzębski, S · 2021
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Ni, J., Ábrego, G. H., Constant, N., Ma, J., Hall, K. B., Cer, D., and Yang, Y · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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The tox21 10k compound library: Collaborative chemistry advancing toxicology
Richard, A. M., Huang, R., Waidyanatha, S., Shinn, P., Collins, B. J., Thillainadarajah, I., Grulke, C. M., Williams, A. J., Lougee, R. R., Judson, R. S., et al · 2021
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Fs-mol: A few-shot learning dataset of molecules
Stanley, M., Bronskill, J. F., Maziarz, K., Misztela, H., Lanini, J., Segler, M., Schneider, N., and Brockschmidt, M · 2021
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Critical assessment of ai in drug discovery
Walters, W. P. and Barzilay, R · 2021
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Finetuned language models are zero-shot learners
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2021
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Trade-off predictivity and explainability for machine-learning powered predictive toxicology: An in-depth investigation with Tox21 data sets
Wu, L., Huang, R., Tetko, I. V., Xia, Z., Xu, J., and Tong, W · 2021
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Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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SCEHR: Supervised contrastive learning for clinical risk prediction using electronic health records
Zang, C. and Wang, F · 2021
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Large-scale distributed training of transformers for chemical fingerprinting
Abdel-Aty, H. and Gould, I. R · 2022
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Graph neural networks pretraining through inherent supervision for molecular property prediction
Benjamin, R., Singer, U., and Radinsky, K · 2022
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Improving language models by retrieving from trillions of tokens
Borgeaud, S., Mensch, A., Hoffmann, J., Cai, T., Rutherford, E., Millican, K., Van Den Driessche, G. B., Lespiau, J.-B., Damoc, B., Clark, A., et al · 2022
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Molecular structure-property co-trained foundation model for in silico chemistry
Chang, J. and Ye, J. C · 2022
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Meta-learning adaptive deep kernel gaussian processes for molecular property prediction
Chen, W., Tripp, A., and Hernández-Lobato, J. M · 2022
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BARTSmiles: Generative masked language models for molecular representations
Chilingaryan, G., Tamoyan, H., Tevosyan, A., Babayan, N., Khondkaryan, L., Hambardzumyan, K., Navoyan, Z., Khachatrian, H., and Aghajanyan, A · 2022
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Translation between Molecules and Natural Language
Edwards, C., Lai, T., Ros, K., Honke, G., and Ji, H · 2022
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Geometry-enhanced molecular representation learning for property prediction
Fang, X., Liu, L., Lei, J., He, D., Zhang, S., Zhou, J., Wang, F., Wu, H., and Wang, H · 2022
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CLOOB: Modern Hopfield networks with InfoLOOB outperform clip
Fürst, A., Rumetshofer, E., Tran, V., Ramsauer, H., Tang, F., Lehner, J., Kreil, D., Kopp, M., Klambauer, G., Bitto-Nemling, A., et al · 2022
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Multilingual molecular representation learning via contrastive pre-training
Guo, Z., Sharma, P., Martinez, A., Du, L., and Abraham, R · 2022
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Masked molecule modeling: A new paradigm of molecular representation learning for chemistry understanding
He, J., Tian, K., Luo, S., Min, Y., Zheng, S., Shi, Y., He, D., Liu, H., Yu, N., Wang, L., Wu, J., and Liu, T.-Y · 2022
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Hugging face
Jain, S. M · 2022
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Contrastive meta-learning for drug-target binding affinity prediction
Li, M., Xu, S., Cai, X., Zhang, Z., and Ji, H · 2022
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Design guidelines for prompt engineering text-to-image generative models
Liu, V. and Chilton, L. B · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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Contrastive learning of image-and structure-based representations in drug discovery
Sanchez-Fernandez, A., Rumetshofer, E., Hochreiter, S., and Klambauer, G · 2022
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LAION-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al · 2022
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Improving few-and zero-shot reaction template prediction using modern hopfield networks
Seidl, P., Renz, P., Dyubankova, N., Neves, P., Verhoeven, J., Wegner, J. K., Segler, M., Hochreiter, S., and Klambauer, G · 2022
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Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al · 2022
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3D infomax improves GNNs for molecular property prediction
Stärk, H., Beaini, D., Corso, G., Tossou, P., Dallago, C., Günnemann, S., and Liò, P · 2022
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Does GNN pretraining help molecular representation?
Sun, R · 2022
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Paradigm shift in natural language processing
Sun, T.-X., Liu, X.-Y., Qiu, X.-P., and Huang, X.-J · 2022
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Galactica: A large language model for science
Taylor, R., Kardas, M., Cucurull, G., Scialom, T., Hartshorn, A., Saravia, E., Poulton, A., Kerkez, V., and Stojnic, R · 2022
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Dual use of artificial-intelligence-powered drug discovery
Urbina, F., Lentzos, F., Invernizzi, C., and Ekins, S · 2022
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Evaluating self-supervised learning for molecular graph embeddings
Wang, H., Kaddour, J., Liu, S., Tang, J., Kusner, M., Lasenby, J., and Liu, Q · 2022
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Wu, Y., Rabe, M. N., Hutchins, D., and Szegedy, C · 2022
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Uni-mol: A universal 3d molecular representation learning framework
Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., and Ke, G · 2022
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Context-enriched molecule representations improve few-shot drug discovery
Schimunek, J., Seidl, P., Friedrich, L., Kuhn, D., Rippmann, F., Hochreiter, S., and Klambauer, G · 2023
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Sigmoid loss for language image pre-training
Zhai, X., Mustafa, B., Kolesnikov, A., and Beyer, L · 2023
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A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals
Zeng, Z., Yao, Y., Liu, Z., and Sun, M · 2041
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