Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Neural Networks: A Comprehensive Foundation

Haykin, S. (1998) · Cited by 30,070 · see citing works

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works

Proceedings of the 24th international conference on Machine learning

(2007) · doi:10.1145/1273496 · Cited by 11,869 · see citing works

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works

Natural Language Processing (almost) from ScratchPreprint

Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,882 · see citing works

Pattern Recognition and Neural Networks

Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,446 · see citing works

A survey of transfer learning

Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260 · see citing works

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works

A unified architecture for natural language processing: deep neural networks with multitask learning

Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

Svetnik, V., Liaw, A. I., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034160g · Cited by 3,613 · see citing works

Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research

Agatonović-Kuštrin, S., & Beresford, R. (2000) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/s0731-7085(99)00272-1 · Cited by 1,987 · see citing works

Boosting for transfer learning

Dai, W., Yang, Q., Xue, G.-R., & Yu, Y. H. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273521 · Cited by 1,788 · see citing works

Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition

Bridle, J. S. (1990) · Springer eBooks · doi:10.1007/978-3-642-76153-9_28 · Cited by 1,278 · see citing works

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159 · see citing works

Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,128 · see citing works

Atom pairs as molecular features in structure-activity studies: definition and applications

Carhart, R. E., Smith, D. H., & Venkataraghavan, R. (1985) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00046a002 · Cited by 1,120 · see citing works

Bayesian Regularization of Neural Networks

Burden, F. R., & Winkler, D. A. (2008) · Methods in molecular biology · doi:10.1007/978-1-60327-101-1_3 · Cited by 679 · see citing works

Multitask Learning

Caruana, R. (1998) · Learning to Learn · doi:10.1007/978-1-4615-5529-2_5 · Cited by 577 · see citing works

Massively Multitask Networks for Drug DiscoveryPreprint

Ramsundar, B., Kearnes, S. M., Riley, P., Webster, D. R., Konerding, D. E., & Pande, V. S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.02072 · Cited by 383 · see citing works

Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.

Sheridan, R. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400084k · Cited by 376 · see citing works

Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR

Sheridan, R. P., Feuston, B. P., Maiorov, V. N., & Kearsley, S. K. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049782w · Cited by 298 · see citing works

Multi-class pattern classification using neural networks

Ou, G., & Murphey, Y. L. (2006) · Pattern Recognition · doi:10.1016/j.patcog.2006.04.041 · Cited by 289 · see citing works

Chemical Similarity Using Physiochemical Property Descriptors

Kearsley, S. K., Sallamack, S. J., Fluder, E. M., Andose, J. D., Mosley, R. T., & Sheridan, R. P. (1996) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci950274j · Cited by 267 · see citing works

Robust QSAR Models Using Bayesian Regularized Neural Networks

Burden, F. R., & Winkler, D. A. (1999) · Journal of Medicinal Chemistry · doi:10.1021/jm980697n · Cited by 242 · see citing works

Multi-task Neural Networks for QSAR PredictionsPreprint

Dahl, G. E., Jaitly, N., & Salakhutdinov, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.1231 · Cited by 217 · see citing works

Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR

Winkler, D. A., & Le, T. C. (2016) · Molecular Informatics · doi:10.1002/minf.201600118 · Cited by 122 · see citing works

Neural Networks as Robust Tools in Drug Lead Discovery and Development

Winkler, D. A. (2004) · Molecular Biotechnology · doi:10.1385/mb:27:2:139 · Cited by 90 · see citing works

Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients

Varnek, A., Gaudin, C., Marcou, G., Baskin, I., Pandey, A. K., & Tetko, I. V. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8002914 · Cited by 86 · see citing works

Associative Neural Network

Tetko, I. V. (2002) · Neural Processing Letters · doi:10.1023/a:1019903710291 · Cited by 83 · see citing works

CUDAMat: a CUDA-based matrix class for Python

Mnih, V. (2009) · Cited by 68 · see citing works

Transfer Learning with an Ensemble of Background Tasks

Rosenstein, M. T., Marx, Z., Dietterich, T. G., & Kaelbling, L. P. (2005) · neural information processing systems · Cited by 66 · see citing works

An Optimal Self‐Pruning Neural Network and Nonlinear Descriptor Selection in QSAR

Burden, F. R., & Winkler, D. A. (2009) · QSAR & Combinatorial Science · doi:10.1002/qsar.200810202 · Cited by 65 · see citing works

New QSAR Methods Applied to Structure−Activity Mapping and Combinatorial Chemistry

Burden, F. R., & Winkler, D. A. (1998) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci980070d · Cited by 63 · see citing works

Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots

(1995) · doi:10.1109/iros.1995 · Cited by 50 · see citing works

Neural Networks and Genome Informatics

Gaasterland, T. (2001) · Computers & Chemistry · doi:10.1016/s0097-8485(01)00077-8 · Cited by 49 · see citing works

Modeling Industrial ADMET Data with Multitask NetworksPreprint

Kearnes, S. M., Goldman, B., & Pande, V. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.08793 · Cited by 39 · see citing works

Computational chemogenomics: Is it more than inductive transfer?

Brown, J. B., Okuno, Y., Marcou, G., Varnek, A. A., & Horvath, D. (2014) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-014-9743-1 · Cited by 33 · see citing works

Sensor fusion for autonomous outdoor navigation using neural networks

Davis, I. L., & Stentz, A. (2002) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.1995.525906 · Cited by 23 · see citing works

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works

QSAR without borders

Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works

Strategies for Pre-training Graph Neural NetworksPreprint

Hu, W., Liu, B., Gomes, J., Žitnik, M., Liang, P., Pande, V. S., & Leskovec, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12265 · Cited by 1,079 · see citing works

Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

Stepniewska-Dziubinska, M. M., Zielenkiewicz, P., & Siedlecki, P. S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty374 · Cited by 665 · see citing works

Advancing drug discovery via artificial intelligence

Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.06.004 · Cited by 654 · see citing works

A Critical Review of Machine Learning of Energy Materials

Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works

Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values

Rodríguez-Pérez, R., & Bajorath, J. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b01101 · Cited by 520 · see citing works

Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra

Ng, W., Minasny, B., Montazerolghaem, M., Padarian, J., Ferguson, R., Bailey, S., & McBratney, A. B. (2019) · Geoderma · doi:10.1016/j.geoderma.2019.06.016 · Cited by 473 · see citing works

Accelerating materials discovery using artificial intelligence, high performance computing and robotics

Pyzer-Knapp, E. O., Pitera, J. W., Staar, P. W. J., Takeda, S., Laino, T., Sanders, D. P., Sexton, J., Smith, J. R., & Curioni, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00765-z · Cited by 465 · see citing works

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B. (2021) · Medicinal Research Reviews · doi:10.1002/med.21764 · Cited by 429 · see citing works

Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR

Tropsha, A., Isayev, O., Varnek, A. A., Schneider, G., & Cherkasov, A. R. (2023) · Nature Reviews Drug Discovery · doi:10.1038/s41573-023-00832-0 · Cited by 423 · see citing works

Artificial Intelligence in Drug Design

Heßler, G., & Baringhaus, K. H. (2018) · Molecules · doi:10.3390/molecules23102520 · Cited by 387 · see citing works

Transfer Learning for Drug Discovery

Cai, C., Wang, S., Xu, Y., Zhang, W., Tang, K., Ouyang, Q., Lai, L., & Pei, J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02147 · Cited by 364 · see citing works

Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks

Ghasemi, F., Mehridehnavi, A., Pérez-Garrido, A., & Pérez‐Sánchez, H. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.06.016 · Cited by 224 · see citing works

Learning Molecular Representations for Medicinal Chemistry

Chuang, K. V., Gunsalus, L. M., & Keiser, M. J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c00385 · Cited by 227 · see citing works

Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets

Wenzel, J., Matter, H., & Schmidt, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00785 · Cited by 227 · see citing works

A Review of In Silico Tools as Alternatives to Animal Testing: Principles, Resources and Applications

Madden, J. C., Enoch, S. J., Paini, A., & Cronin, M. (2020) · Alternatives to Laboratory Animals · doi:10.1177/0261192920965977 · Cited by 215 · see citing works

Machine learning and feature selection for drug response prediction in precision oncology applications

Ali, M., & Aittokallio, T. A. (2018) · Biophysical Reviews · doi:10.1007/s12551-018-0446-z · Cited by 207 · see citing works

Improvement in ADMET Prediction with Multitask Deep Featurization

Feinberg, E. N., Joshi, E., Pande, V. S., & Cheng, A. C. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02187 · Cited by 203 · see citing works

Chemi-Net: A Molecular Graph Convolutional Network for Accurate Drug Property Prediction

Liu, K., Sun, X., Jia, L., Ma, J., Xing, H., Wu, J., Gao, H., Sun, Y., Boulnois, F., & Fan, J. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20143389 · Cited by 197 · see citing works

Enhancing preclinical drug discovery with artificial intelligence

Vijayan, R., Kihlberg, J. O., Cross, J. B., & Poongavanam, V. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.11.023 · Cited by 184 · see citing works

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information

Heyndrickx, W., Mervin, L., Morawietz, T., Sturm, N., Friedrich, L., Zalewski, A., Pentina, A., Humbeck, L., Oldenhof, M., Niwayama, R., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00799 · Cited by 174 · see citing works

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

Hemmerich, J., & Ecker, G. F. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1475 · Cited by 169 · see citing works

Artificial intelligence in chemistry and drug design

Brown, N., Ertl, P., Lewis, R., Luksch, T., Reker, D., & Schneider, N. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00317-x · Cited by 165 · see citing works

Molecular property prediction: recent trends in the era of artificial intelligence

Shen, J., & Nicolaou, C. A. (2019) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2020.05.001 · Cited by 157 · see citing works

A review on machine learning methods for in silico toxicity prediction

Idakwo, G., Luttrell, J., Chen, M., Hong, H., Zhou, Z., Gong, P., & Zhang, C. (2018) · Journal of Environmental Science and Health Part C · doi:10.1080/10590501.2018.1537118 · Cited by 146 · see citing works

Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT

Li, X., & Fourches, D. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00430-x · Cited by 145 · see citing works

Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting

Buterez, D., Janet, J. P., Kiddle, S. J., Oglić, D., & Lió, P. (2024) · Nature Communications · doi:10.1038/s41467-024-45566-8 · Cited by 139 · see citing works

Advances in Artificial Intelligence (AI)-assisted approaches in drug screening

Singh, S., Gupta, H., Sharma, P., & Sahi, S. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100039 · Cited by 144 · see citing works

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

Kim, H., Kim, E., Lee, I., Bae, B., Park, M., & Nam, H. (2020) · Biotechnology and Bioprocess Engineering · doi:10.1007/s12257-020-0049-y · Cited by 138 · see citing works

Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network

Li, X., Xu, Y., Lai, L., & Pei, J. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00110 · Cited by 130 · see citing works

Recent applications of machine learning in medicinal chemistry

Panteleev, J., Gao, H., & Jia, L. (2018) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2018.06.046 · Cited by 129 · see citing works

Opportunities and challenges using artificial intelligence in ADME/Tox

Bhhatarai, B., Walters, W. P., Hop, C. E. C. A., Lanza, G., & Ekins, S. (2019) · Nature Materials · doi:10.1038/s41563-019-0332-5 · Cited by 117 · see citing works

Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods

Jain, S., Siramshetty, V. B., Alves, V. M., Muratov, E. N., Kleinstreuer, N., Tropsha, A., Nicklaus, M. C., Simeonov, A. M., & Zakharov, A. V. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01164 · Cited by 114 · see citing works

Critical assessment of AI in drug discovery

Walters, W. P., & Barzilay, R. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1915982 · Cited by 108 · see citing works

Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space

Sosnin, S., Karlov, D., Tetko, I. V., & Fedorov, M. V. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00685 · Cited by 102 · see citing works

Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach

Morrone, J. A., Weber, J. K., Huynh, T., Luo, H., & Cornell, W. D. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00927 · Cited by 97 · see citing works

Applications of Artificial Intelligence in Drug Repurposing

Wan, Z., Sun, X., Li, Y., Chu, T., Hao, X., Cao, Y., & Zhang, P. (2025) · Advanced Science · doi:10.1002/advs.202411325 · Cited by 95 · see citing works

Deep Learning Approaches in Predicting ADMET Properties

Cáceres, E. L., Tudor, M., & Cheng, A. C. (2020) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0259 · Cited by 94 · see citing works

Deep Learning Enhancing Kinome-Wide Polypharmacology Profiling: Model Construction and Experiment Validation

Li, X., Li, Z. S., Wu, X., Xiong, Z., Yang, T., Fu, Z., Liu, X., Tan, X., Zhong, F., Wan, X., et al. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b00855 · Cited by 93 · see citing works

Applying convolutional neural networks (CNN) for end-to-end soil analysis based on laser-induced breakdown spectroscopy (LIBS) with less spectral preprocessing

Xu, X., Ma, F., Zhou, J., & Du, C. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107171 · Cited by 87 · see citing works

Prediction of antioxidant peptides using a quantitative structure−activity relationship predictor (AnOxPP) based on bidirectional long short-term memory neural network and interpretable amino acid descriptors

Qin, D., Jiao, L., Wang, R., Zhao, Y. C., Hao, Y., & Liang, G. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106591 · Cited by 87 · see citing works

Recent Advances in In Silico Target Fishing

Galati, S., Stefano, M. D., Martinelli, E., Poli, G., & Tuccinardi, T. (2021) · Molecules · doi:10.3390/molecules26175124 · Cited by 85 · see citing works

A Survey of Multi‐task Learning Methods in Chemoinformatics

Sosnin, S., Vashurina, M., Withnall, M., Karpov, P., Fedorov, M. V., & Tetko, I. V. (2018) · Molecular Informatics · doi:10.1002/minf.201800108 · Cited by 82 · see citing works

Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective

Fang, C., Wang, Y., Grater, R., Kapadnis, S., Black, C., Trapa, P. E., & Sciabola, S. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00160 · Cited by 79 · see citing works

Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets

Zhou, Y., Cahya, S., Combs, S. A., Nicolaou, C. A., Wang, J., Desai, P. V., & Shen, J. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00671 · Cited by 78 · see citing works

Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models

Zakharov, A. V., Zhao, T., Nguyễn, Ð.-T., Peryea, T., Sheils, T. K., Yasgar, A., Huang, R., Southall, N. T., & Simeonov, A. M. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00526 · Cited by 79 · see citing works

The power of deep learning to ligand-based novel drug discovery

Baskin, I. I. (2020) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2020.1745183 · Cited by 75 · see citing works

De Novo Molecule Design by Translating from Reduced Graphs to SMILES

Pogány, P., Arad, N., Genway, S., & Pickett, S. D. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00626 · Cited by 75 · see citing works

A fingerprints based molecular property prediction method using the BERT model

Wen, N., Liu, G., Zhang, J., Zhang, R., Fu, Y., & Han, X. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00650-3 · Cited by 74 · see citing works

Imputation of Assay Bioactivity Data Using Deep Learning

Whitehead, T. M., Irwin, B., Hunt, P. A., Segall, M., & Conduit, G. J. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00768 · Cited by 71 · see citing works

Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review

Tripathi, N., Goshisht, M. K., Sahu, S. K., & Arora, C. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10237-z · Cited by 74 · see citing works

Neural multi-task learning in drug design

Allenspach, S., Hiss, J. A., & Schneider, G. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-023-00785-4 · Cited by 70 · see citing works

Multitask Machine Learning for Classifying Highly and Weakly Potent Kinase Inhibitors

Rodríguez-Pérez, R., & Bajorath, J. (2019) · ACS Omega · doi:10.1021/acsomega.9b00298 · Cited by 66 · see citing works

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets

Zorn, K. M., Lane, T. R., Russo, D. P., Clark, A. M., Makarov, V., & Ekins, S. (2019) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b01297 · Cited by 64 · see citing works

BRADSHAW: a system for automated molecular design

Green, D. V. S., Pickett, S. D., Luscombe, C. N., Senger, S., Marcus, D., Meslamani, J. E., Brett, D., Powell, A., & Masson, J. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-019-00234-8 · Cited by 62 · see citing works

Application of machine learning models for property prediction to targeted protein degraders

Peteani, G., Huynh, M. T. D., Gerebtzoff, G., & Rodríguez-Pérez, R. (2024) · Nature Communications · doi:10.1038/s41467-024-49979-3 · Cited by 61 · see citing works

Interpretation of ANN‐based QSAR models for prediction of antioxidant activity of flavonoids

Žuvela, P., David, J., & Wong, M. W. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25168 · Cited by 61 · see citing works

Convolutional neural network with near-infrared spectroscopy for plastic discrimination

Xia, J., Huang, Y., Li, Q., Xiong, Y., & Min, S. (2021) · Environmental Chemistry Letters · doi:10.1007/s10311-021-01240-9 · Cited by 60 · see citing works

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches

Ciallella, H. L., Russo, D. P., Aleksunes, L. M., Grimm, F. A., & Zhu, H. (2020) · Laboratory Investigation · doi:10.1038/s41374-020-00477-2 · Cited by 60 · see citing works

Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data

Irwin, B., Levell, J. R., Whitehead, T. M., Segall, M., & Conduit, G. J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00443 · Cited by 59 · see citing works

Artificial intelligence in the pharmaceutical sector: current scene and future prospect

Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., Tambe, V., & Tekade, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814455-8.00003-7 · Cited by 61 · see citing works

Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure–Activity Relationship Models Based on Deep Neural Networks?

Liu, R., Wang, H., Glover, K., Feasel, M. G., & Wallqvist, A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00348 · Cited by 58 · see citing works

FP2VEC: a new molecular featurizer for learning molecular properties

Jeon, W., & Kim, D. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz307 · Cited by 50 · see citing works

Industry-scale application and evaluation of deep learning for drug target prediction

Sturm, N., Mayr, A., Van, T. L., Chupakhin, V., Ceulemans, H., Wegner, J. K., Dzib, J. F. G., Jeliazkova, N., Vandriessche, Y., Böhm, S., et al. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00428-5 · Cited by 51 · see citing works

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

Playe, B., & Stoven, V. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-0413-0 · Cited by 50 · see citing works

iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding

Nguyen-Vo, T., Trinh, Q. H., Nguyen, L. K., Nguyen-Hoang, P., Nguyen, T., Nguyen, D., Nguyen, B. P., & Le, L. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00628 · Cited by 45 · see citing works

Machine Learning in Chemoinformatics and Medicinal Chemistry

Rodríguez-Pérez, R., Miljković, F. N., & Bajorath, J. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122120-124216 · Cited by 48 · see citing works

Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development

Jung, W. J., Goo, S., Hwang, T., Lee, H., Kim, Y., Chae, J., Yun, H., & Jung, S. (2024) · Pharmaceuticals · doi:10.3390/ph17030382 · Cited by 51 · see citing works

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine

Ambreen, S., Umar, M., Noor, A., Jain, H., & Ali, R. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.117164 · Cited by 50 · see citing works

Application of Deep Neural Network Models in Drug Discovery Programs

Grebner, C., Matter, H., Kofink, D., Wenzel, J., Schmidt, F., & Heßler, G. (2021) · ChemMedChem · doi:10.1002/cmdc.202100418 · Cited by 45 · see citing works

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning

Tan, X., Jiang, X., Yang, H., Zhong, F., Li, X., Xiong, Z., Li, Z., Liu, X., Chen, C., Zhao, Q., et al. (2020) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2020.112572 · Cited by 45 · see citing works

MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property PredictionPreprint

Sanyal, S., Balachandran, J., Yadati, N., Kumar, A., Rajagopalan, P., Sanyal, S., & Talukdar, P. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.05660 · Cited by 45 · see citing works

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

Djoumbou-Feunang, Y., Wilmot, J. T., Kinney, J., Chanda, P., Yu, P., Sader, C. A., Sharifi, M., Smith, S., Ou, J., Hu, J. K., et al. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1292027 · Cited by 41 · see citing works

In silico off-target profiling for enhanced drug safety assessment

Liu, J., Gui, Y., Rao, J., Sun, J., Wang, G., Ren, Q., Qu, N., Niu, B., Chen, Z., Sheng, X., et al. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.03.002 · Cited by 41 · see citing works

Chemi-net: a graph convolutional network for accurate drug property predictionPreprint

Liu, K., Sun, X., Jia, L., Ma, J., Xing, H., Wu, J., Gao, H., Sun, Y., Boulnois, F., & Fan, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06236 · Cited by 39 · see citing works

Effect of missing data on multitask prediction methods

de León, A. D. L. V., Chen, B., & Gillet, V. J. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0281-z · Cited by 38 · see citing works

Prediction of hERG K+ channel blockage using deep neural networks

Zhang, Y., Zhao, J., Wang, Y., Fan, Y., Zhu, L., Yan, Y., Chen, X., Lu, T., Chen, Y., & Liu, H. (2019) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13600 · Cited by 38 · see citing works

A Turing Test for Molecular Generators

Bush, J. T., Pogány, P., Pickett, S. D., Barker, M., Baxter, A., Campos, S., Cooper, A. W. J., Hirst, D. J., Inglis, G. G. A., Nadin, A. J., et al. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c01148 · Cited by 37 · see citing works

Deep Neural Networks for QSAR

Xu, Y. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_10 · Cited by 37 · see citing works

Quantitative structure-activity relationship (QSAR) models and their applicability domain analysis on HIV-1 protease inhibitors by machine learning methods

Tian, Y., Zhang, S., Yin, H., & Yan, A. (2019) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2019.103888 · Cited by 37 · see citing works

Virtual Screening Meets Deep Learning

Pérez-Sianes, J., Pérez‐Sánchez, H., & Díaz, F. (2018) · Current Computer - Aided Drug Design · doi:10.2174/1573409914666181018141602 · Cited by 38 · see citing works

Application of Bioactivity Profile-Based Fingerprints for Building Machine Learning Models

Sturm, N., Sun, J., Vandriessche, Y., Mayr, A., Klambauer, G., Carlsson, L., Engkvist, O., & Chen, H. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00550 · Cited by 36 · see citing works

Machine Learning for In Silico ADMET Prediction

Jia, L., & Gao, H. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_20 · Cited by 35 · see citing works

Machine learning models for phase transition and decomposition temperature of ionic liquids

Makarov, D. M., Fadeeva, Y. A., Shmukler, L. E., & Tetko, I. V. (2022) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2022.120247 · Cited by 35 · see citing works

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)‐based small molecule structure elucidation

Liu, Y., Vijlder, T. D., Bittremieux, W., Laukens, K., & Heyndrickx, W. (2021) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.9120 · Cited by 34 · see citing works

Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge

Lenselink, E. B., & Stouten, P. F. W. (2021) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-021-00405-6 · Cited by 34 · see citing works

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction

Cáceres, E. L., Mew, N. C., & Keiser, M. J. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00565 · Cited by 33 · see citing works

Advances in machine intelligence‐driven virtual screening approaches for big‐data

Kumar, N., & Acharya, V. (2023) · Medicinal Research Reviews · doi:10.1002/med.21995 · Cited by 35 · see citing works

Impact of Artificial Intelligence (AI) on Drug Discovery and Product Development

Narayanan, R. R., Durga, N., & Nagalakshmi, S. (2022) · Indian Journal of Pharmaceutical Education and Research · doi:10.5530/ijper.56.3s.146 · Cited by 36 · see citing works

Simultaneous estimation of multiple soil properties from vis-NIR spectra using a multi-gate mixture-of-experts with data augmentation

Wang, X., Zhang, M., Zhou, Y., Wang, L., Zeng, L.-T., Cui, Y.-P., & Sun, X. (2024) · Geoderma · doi:10.1016/j.geoderma.2024.117127 · Cited by 32 · see citing works

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

Agu, P. C., & Obulose, C. N. (2024) · Drug Development Research · doi:10.1002/ddr.22159 · Cited by 33 · see citing works

Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines

Haas, B. C., Hardy, M. A., V., S. S. S., Adams, K., Coley, C. W., Paton, R. S., & Sigman, M. S. (2024) · Digital Discovery · doi:10.1039/d4dd00284a · Cited by 30 · see citing works

Prediction of Compound Profiling Matrices, Part II: Relative Performance of Multitask Deep Learning and Random Forest Classification on the Basis of Varying Amounts of Training Data

Rodríguez-Pérez, R., & Bajorath, J. (2018) · ACS Omega · doi:10.1021/acsomega.8b01682 · Cited by 30 · see citing works

Introduction to the BioChemical Library (BCL): An Application-Based Open-Source Toolkit for Integrated Cheminformatics and Machine Learning in Computer-Aided Drug Discovery

Brown, B. P., Vu, O., Geanes, A. R., Kothiwale, S., Butkiewicz, M., Lowe, E. W., Mueller, R., Pape, R., Mendenhall, J., & Meiler, J. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.833099 · Cited by 30 · see citing works

Artificial intelligence and nanotechnology for cervical cancer treatment: Current status and future perspectives

Kour, S., Biswas, I., Sheoran, S., Arora, S., Sheela, P., Duppala, S. K., Murthy, D. K., Pawar, S. C., Singh, H., Kumar, D., et al. (2023) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2023.104392 · Cited by 32 · see citing works

A multitask GNN-based interpretable model for discovery of selective JAK inhibitors

Wang, Y., Gu, Y., Lou, C., Gong, Y., Wu, Z., Li, W., Tang, Y., & Liu, G. (2022) · Journal of Cheminformatics · doi:10.1186/s13321-022-00593-9 · Cited by 27 · see citing works

Application of Machine Learning and Reaction Optimization for the Iterative Improvement of Enantioselectivity of Cinchona-Derived Phase Transfer Catalysts

Lexa, K. W., Belyk, K. M., Henle, J. J., Xiang, B., Sheridan, R. P., Denmark, S. E., Ruck, R. T., & Sherer, E. C. (2021) · Organic Process Research & Development · doi:10.1021/acs.oprd.1c00155 · Cited by 26 · see citing works

Multi-task learning with a natural metric for quantitative structure activity relationship learning

Sadawi, N., Olier, I., Vanschoren, J., van Rijn, J. N., Besnard, J., Bickerton, R., Groşan, C., Soldatova, L., & King, R. D. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0392-1 · Cited by 26 · see citing works

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

Suay‐García, B., Bueso-Bordils, J. I., Falcó, A., Pérez‐Gracia, M. T., Antón-Fos, G. M., & Aleman, P. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1472 · Cited by 26 · see citing works

Predicting molecular activity on nuclear receptors by multitask neural networks

Valsecchi, C., Collarile, M., Grisoni, F., Todeschini, R., Ballabio, D., & Consonni, V. (2020) · Journal of Chemometrics · doi:10.1002/cem.3325 · Cited by 26 · see citing works

Multitask Learning for Quantitative Structure–Activity Relationships: A Tutorial

Valsecchi, C., Grisoni, F., Consonni, V., Ballabio, D., & Todeschini, R. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-20730-3_8 · Cited by 2 · see citing works

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159 · see citing works

Multi-task Neural Networks for QSAR PredictionsPreprint

Dahl, G. E., Jaitly, N., & Salakhutdinov, R. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.1231 · Cited by 217 · see citing works

Multi-task neural networks for QSAR predictions; 2014; http://arxiv.org/abs/1406.1231; arXiv:1406.1231 [stat.MLPreprint

Dahl, G. E., Jaitly, N., & Salakhutdinov, R. (2016)