Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Neural multi-task learning in drug design

Nature Machine Intelligence volume 6, pages 124–137 (2024)Cite this article

Subjects

Abstract

Multi-task learning (MTL) is a machine learning paradigm that aims to enhance the generalization of predictive models by leveraging shared information across multiple tasks. The recent breakthroughs achieved by deep neural network models in various domains have sparked hope for similar advances in the chemical sciences. In this Perspective, we provide insights into the current state and future potential of neural MTL models applied to computer-assisted drug design. In the context of drug discovery, one prominent application of MTL is protein–ligand binding affinity prediction, in which individual proteins are considered tasks. Here we introduce the fundamental principles of MTL and propose a framework for categorizing MTL models on the basis of their architecture. This framework enables us to present a comprehensive overview and comparison of a selection of MTL models that have been successfully utilized in drug design. Subsequently, we delve into the current challenges associated with the applications of MTL. One of the key challenges lies in defining suitable representations of the molecular entities under investigation and the respective machine learning tasks.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Pipeline for pair-input MTL models.
Fig. 2: Illustrative representations of molecules and proteins.

Similar content being viewed by others

References

  1. Kirkpatrick, P. & Ellis, C. Chemical space. Nature 432, 823–823 (2004).

    Article  ADS  CAS  Google Scholar 

  2. Reymond, J.-L. The chemical space project. Acc. Chem. Res. 48, 722–730 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Ertl, P. Cheminformatics analysis of organic substituents: identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J. Chem. Inf. Comput. Sci. 43, 374–380 (2003).

    Article  CAS  PubMed  Google Scholar 

  4. Bohacek, R. S., McMartin, C. & Guida, W. C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev. 16, 3–50 (1996).

    Article  CAS  PubMed  Google Scholar 

  5. Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25 (1997).

    Article  CAS  Google Scholar 

  6. Schneider, G. De novo Molecular Design (John Wiley and Sons, 2013).

  7. Sadybekov, A. V. & Katritch, V. Computational approaches streamlining drug discovery. Nature 616, 673–685 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Schneider, P. et al. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19, 353–364 (2019).

    Article  PubMed  Google Scholar 

  9. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Schmidhuber, J. Deep learning in neural networks: an overview. Neural Networks 61, 85–117 (2015).

    Article  PubMed  Google Scholar 

  11. Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1106–1114 (2012).

  12. Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 5998–6008 (2017).

  13. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Jiménez-Luna, J., Grisoni, F., Weskamp, N. & Schneider, G. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin. Drug Discov. 16, 949–959 (2021).

    Article  PubMed  Google Scholar 

  15. Reker, D., Schneider, P., Schneider, G. & Brown, J. Active learning for computational chemogenomics. Future Med. Chem. 9, 381–402 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Caruana, R. Multitask learning. Mach. Learn. 28, 41–75 (1997).

    Article  Google Scholar 

  17. Zhang, Y. & Yang, Q. An overview of multi-task learning. Natl Sci. Rev. 5, 30–43 (2017).

    Article  Google Scholar 

  18. Borchani, H., Varando, G., Bielza, C. & Larrañaga, P. A survey on multi-output regression. WIREs Data Min. Knowl. Discovery 5, 216–233 (2015).

    Article  Google Scholar 

  19. Waegeman, W., DembczyÅ„ski, K. & Hüllermeier, E. Multi-target prediction: A unifying view on problems and methods. Data Min. Knowl. Discov. 33, 293–324 (2019).

  20. Xu, Y., Ma, J., Liaw, A., Sheridan, R. & Svetnik, V. Demystifying multi-task deep neural networks for quantitative structure-activity relationships. J. Chem. Inf. Model. 57, 2490–2504 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Thrun, S. & Pratt, L. Learning to Learn: Introduction and Overview (Springer, 1998).

  22. Hospedales, T., Antoniou, A., Micaelli, P. & Storkey, A. Meta-learning in neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44, 5149–5169 (2022).

    PubMed  Google Scholar 

  23. Bayoudh, K., Knani, R., Hamdaoui, F. & Abdellatif, M. A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets. Visual Comput. 38, 2939–2970 (2022).

    Article  Google Scholar 

  24. Stahlschmidt, S. R., Ulfenborg, B. & Synnergren, J. Multimodal deep learning for biomedical data fusion: a review. Briefings Bioinf. 23, bbab569 (2022).

    Article  Google Scholar 

  25. Kline, A. et al. Multimodal machine learning in precision health: a scoping review. npj Digit. Med. 5, 171 (2022).

  26. Tang, X. et al. Explainable multi-task learning for multi-modality biological data analysis. Nat. Commun. 14, 2546 (2023).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  27. Rosenbaum, L., Dörr, A., Bauer, M., Boeckler, F. & Zell, A. Inferring multi-target QSAR models with taxonomy-based multi-task learning. J. Cheminform. 5, 33 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Erhan, D., L’Heureux, P.-J., Yue, S. & Bengio, Y. Collaborative filtering on a family of biological targets. J. Chem. Inf. Model. 46, 626–35 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Jin, B. et al. Multitask dyadic prediction and its application in prediction of adverse drug-drug interaction. In Conference on Artificial Intelligence Vol. 31, 1367–1373 (AAAI Press, 2017).

  30. Li, R. et al. Inductive matrix completion for predicting adverse drug reactions (ADRs) integrating drug-target interactions. Chemom. Intell. Lab. Syst. 144, 71–79 (2015).

    Article  CAS  Google Scholar 

  31. Simm, J. et al. Macau: Scalable Bayesian factorization with high-dimensional side information using MCMC. In International Workshop on Machine Learning for Signal Processing (IEEE, 2017).

  32. McCabe, P. G., Ortega-Martorell, S. & Olier, I. Benchmarking multi-task learning in predictive models for drug discovery. 2019 International Joint Conference on Neural Networks 1–7 (IEEE, 2019).

  33. Pliakos, K., Vens, C. & Tsoumakas, G. Predicting drug-target interactions with multi-label classification and label partitioning. IEEE/ACM Trans. Comput. Biol. Bioinform. 18, 1596–1607 (2021).

    Article  CAS  PubMed  Google Scholar 

  34. Simões, R. S., Maltarollo, V. G., Oliveira, P. R. & Honorio, K. M. Transfer and multi-task learning in QSAR modeling: advances and challenges. Front. Pharmacol. 9, 74 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Sosnin, S. et al. A survey of multi-task learning methods in chemoinformatics. Mol. Inf. 38, 1800108 (2019).

    Article  Google Scholar 

  36. Dahl, G., Jaitly, N. & Salakhutdinov, R. Multi-task neural networks for QSAR predictions. Preprint at https://doi.org/10.48550/arXiv.1406.1231 (2014).

  37. Ma, J., Sheridan, R., Liaw, A., Dahl, G. & Svetnik, V. Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model. 55, 263–274 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. Ramsundar, B. et al. Is multitask deep learning practical for pharma? J. Chem. Inf. Model. 57, 2068–2076 (2017).

    Article  CAS  PubMed  Google Scholar 

  39. Unterthiner, T. et al. Deep learning as an opportunity in virtual screening. In Deep Learning and Representation Learning Workshop, NIPS (2014).

  40. Ramsundar, B. et al. Massively multitask networks for drug discovery. Preprint at https://doi.org/10.48550/arXiv.1502.02072 (2015).

  41. Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742–754 (2010).

    Article  CAS  PubMed  Google Scholar 

  42. Rosenblatt, F. The Perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).

    Article  CAS  PubMed  Google Scholar 

  43. Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning internal representations by error propagation. In Parallel Distributed Processing: Explorations in the Microstructure of Cognition 318–362 (MIT Press, 1986).

  44. Öztürk, H., Özgür, A. & Ozkirimli, E. DeepDTA: deep drug–target binding affinity prediction. Bioinformatics 34, i821–i829 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Nguyen, T. et al. GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37, 1140–1147 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Yang, Z., Zhong, W., Zhao, L. & Yu-Chian Chen, C. MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction. Chem. Sci. 13, 816–833 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Liu, Z. et al. Docking-based virtual screening with multi-task learning. In 2021 IEEE International Conference on Bioinformatics and Biomedicine 381–385 (IEEE, 2021).

  48. Tsubaki, M., Tomii, K. & Sese, J. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics 35, 309–318 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. Withnall, M., Lindelöf, E., Engkvist, O. & Chen, H. Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction. J. Cheminform. 12, 1 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J. & Koes, D. R. Protein–ligand scoring with convolutional neural networks. J. Chem. Inf. Model. 57, 942–957 (2017).

  51. Jones, D. et al. Improved protein–ligand binding affinity prediction with structure-based deep fusion inference. J. Chem. Inf. Model. 61, 1583–1592 (2021).

  52. Martínez Mora, A., Subramanian, V. & Miljković, F. Multi-task convolutional neural networks for predicting in vitro clearance endpoints from molecular images. J. Comput. Aided Mol. Des. 36, 443–457 (2022).

    Article  ADS  PubMed  Google Scholar 

  53. Wu, Z. et al. MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018).

  54. Hughes, T., Dang, N., Miller, G. & Swamidass, S. J. Modeling reactivity to biological macromolecules with a deep multitask network. ACS Cent. Sci. 2, 529–537 (2017).

    Article  Google Scholar 

  55. Xiong, Z. et al. Pushing the boundaries of molecular representation for drug discovery with graph attention mechanism. J. Med. Chem. 63, 8749–8760 (2019).

  56. Öztürk, H., Olmez, E. O. & Özgür, A. WideDTA: Prediction of drug-target binding affinity. Preprint at https://doi.org/10.48550/arXiv.1902.04166 (2019).

  57. Bao, L. et al. Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach. Acta Pharm. Sin. B 13, 54–67 (2022).

  58. Quan, Z. et al. A system for learning atoms based on long short-term memory recurrent neural networks. In 2018 IEEE International Conference on Bioinformatics and Biomedicine 728–733 (IEEE, 2018).

  59. Asgari, E. & Mofrad, M. R. K. Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS ONE 10, e0141287 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Mikolov, T., Chen, K., Corrado, G. & Dean, J. Efficient estimation of word representations in vector space. In International Conference on Learning Representations (ICLR, 2013).

  61. Lin, X. et al. DeepGS: deep representation learning of graphs and sequences for drug-target binding affinity prediction. In 24th European Conference on Artificial Intelligence Vol. 325, 1301–1308 (European Conference on Artificial Intelligence, 2020).

  62. Tian, Q. et al. Predicting drug-target affinity based on recurrent neural networks and graph convolutional neural networks. Comb. Chem. High Throughput Screening 25, 634–641 (2022).

  63. Abbasi, K. et al. DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks. Bioinformatics 36, 4633–4642 (2020).

  64. Tran, H. N. T., Thomas, J. J. & Malim, N. H. A. H. DeepNC: a framework for drug-target interaction prediction with graph neural networks. PeerJ 10, e13163 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Zhu, J. et al. DAEM: deep attributed embedding based multi-task learning for predicting adverse drug-drug interaction. Expert Syst. Appl. 215, 119312 (2023).

  66. Weininger, D. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36 (1988).

    Article  CAS  Google Scholar 

  67. O’Boyle, N. M. & Dalke, A. DeepSMILES: an adaptation of SMILES for use in machine-learning of chemical structures. Preprint at https://doi.org/10.26434/chemrxiv.7097960.v1 (2018).

  68. Lin, S., Shi, C. & Chen, J. GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown drug discovery. BMC Bioinf. 23, 367 (2022).

    Article  CAS  Google Scholar 

  69. Mauri, A., Consonni, V. & Todeschini, R. in Handbook of Computational Chemistry (Springer, 2016).

  70. David, L., Thakkar, A., Mercado, R. & Engkvist, O. Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminform. 12, 56 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Jiménez-Luna, J., Škalič, M., Martínez-Rosell, G. & Fabritiis, G. KDEEP: protein-ligand absolute binding affinity prediction via 3D-convolutional neural networks. J. Chem. Inf. Model. 58, 287–296 (2018).

    Article  Google Scholar 

  72. Feinberg, E. et al. PotentialNet for molecular property prediction. ACS Cent. Sci. 4, 1520–1530 (2018).

  73. Schmitt, S., Kuhn, D. & Klebe, G. A new method to detect related function among proteins independent of sequence or fold homology. J. Mol. Biol. 323, 387–406 (2002).

    Article  CAS  PubMed  Google Scholar 

  74. Volkov, M. et al. On the frustration to predict binding affinities from protein-ligand structures with deep neural networks. J. Med. Chem. 65, 7946–7958 (2022).

  75. Krasoulis, A., Antonopoulos, N., Pitsikalis, V. & Theodorakis, S. DENVIS: scalable and high-throughput virtual screening using graph neural networks with atomic and surface protein pocket features. J. Chem. Inf. Model. 62, 4642–4659 (2022).

    Article  CAS  PubMed  Google Scholar 

  76. Wang, D. D., Chan, M.-T. & Yan, H. Structure-based protein–ligand interaction fingerprints for binding affinity prediction. Comput. Structural Biotechnol. J. 19, 6291–6300 (2021).

  77. Deng, Z., Chuaqui, C. & Singh, J. Structural Interaction Fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. J. Med. Chem. 47, 337–344 (2004).

    Article  CAS  PubMed  Google Scholar 

  78. Ballester, P. J. & Mitchell, J. B. O. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26, 1169–1175 (2010).

    Article  CAS  PubMed  Google Scholar 

  79. Carhart, R. E., Smith, D. H. & Venkataraghavan, R. Atom pairs as molecular features in structure-activity studies: definition and applications. J. Chem. Inf. Comput. Sci. 25, 64–73 (1985).

    Article  CAS  Google Scholar 

  80. Schneider, G., Neidhart, W., Giller, T. & Schmid, G. ‘Scaffold-hopping’ by topological pharmacophore search: a contribution to virtual screening. Angew. Chgem. Int. Ed. 38, 2894–2896 (1999).

    Article  CAS  Google Scholar 

  81. Zheng, L., Fan, J. & Mu, Y. OnionNet: a multiple-layer intermolecular-contact-based convolutional neural network for protein–ligand binding affinity prediction. ACS Omega 4, 15956–15965 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Wang, Z. et al. OnionNet-2: a convolutional neural network model for predicting protein-ligand binding affinity based on residue-atom contacting shells. Front. Chem. 9, 753002 (2021).

  83. Hu, B., Wang, H., Wang, L. & Yuan, W. Adverse drug reaction predictions using stacking deep heterogeneous information network embedding approach. Molecules 23, 3193 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Ma, T., Xiao, C., Zhou, J. & Wang, F. Drug similarity integration through attentive multi-view graph auto-encoders. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18) 3477–3483 (AAAI Press, 2018).

  85. Liu, S., Qu, M., Zhang, Z., Cai, H. & Tang, J. Structured multi-task learning for molecular property prediction. In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS) Vol. 151, 8906–8920 (IEEE, 2022).

  86. Jacot, A., Gabriel, F. & Hongler, C. Neural tangent kernel: convergence and generalization in neural networks. Adv. Neural Inf. Process. Syst. 31, 8580–8589 (2018).

  87. Radhakrishnan, A., Stefanakis, G., Belkin, M. & Uhler, C. Simple, fast, and flexible framework for matrix completion with infinite width neural networks. Proc. Natl Acad. Sci. USA 119, e2115064119 (2022).

    Article  MathSciNet  CAS  PubMed  PubMed Central  Google Scholar 

  88. Kramer, M. Autoassociative neural networks. Comput. Chem. Eng. 16, 313–328 (1992).

    Article  CAS  Google Scholar 

  89. LeCun, Y. & Bengio, Y. in The Handbook of Brain Theory and Neural Networks 255–258 (MIT Press, 1998).

  90. Weng, Y., Lin, C., Zeng, X. & Liang, Y. Drug target interaction prediction using multi-task learning and co-attention. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 528–533 (IEEE, 2019).

  91. Cho, K. et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Conference on Empirical Methods in Natural Language Processing 1724–1734 (Association for Computational Linguistics, 2014).

  92. Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).

    Article  CAS  PubMed  Google Scholar 

  93. Duvenaud, D. et al. Convolutional networks on graphs for learning molecular fingerprints. Adv. Neural Inf. Process. Syst. 28, 2224–2232 (2015).

  94. Kearnes, S., McCloskey, K., Berndl, M., Pande, V. & Riley, P. Molecular graph convolutions: Moving beyond fingerprints. J. Comput. Aided Mol. Des. 30, 595–608 (2016).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  95. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning Vol. 70, 1263–1272 (PMLR, 2017).

  96. Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR, 2017).

  97. Veličković, P. et al. Graph attention networks. In International Conference on Learning Representations (ICLR, 2018).

  98. Xu, K., Hu, W., Leskovec, J. & Jegelka, S. How powerful are graph neural networks? In International Conference on Learning Representations (ICLR, 2019).

  99. Li, G., Xiong, C., Thabet, A. & Ghanem, B. DeeperGCN: training deeper GCNs with generalized aggregation functions. In International Conference on Learning Representations 13024–13034 (ICLR, 2021).

  100. Bai, S., Zhang, F. & Torr, P. H. S. Hypergraph convolution and hypergraph attention. Pattern Recognit. 110, 107637 (2021).

    Article  Google Scholar 

  101. Bronstein, M. M., Bruna, J., Cohen, T. & Veličković, P. Geometric deep learning: grids, groups, graphs, geodesics, and gauges. Preprint at https://doi.org/10.48550/arXiv.2104.13478 (2021).

  102. Gomes, J., Ramsundar, B., Feinberg, E. N. & Pande, V. S. Atomic convolutional networks for predicting protein-ligand binding affinity. Preprint at https://doi.org/10.48550/arXiv.1703.10603 (2017).

  103. Monti, F. et al. Geometric deep learning on graphs and manifolds using mixture model CNNs. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 5425–5434 (IEEE, 2017).

  104. Atz, K., Grisoni, F. & Schneider, G. Geometric deep learning on molecular representations. Nat. Mach. Intell. 3, 1023–1032 (2021).

    Article  Google Scholar 

  105. Isert, C., Atz, K. & Schneider, G. Structure-based drug design with geometric deep learning. Curr. Opin. Struct. Biol. 79, 102548 (2023).

    Article  CAS  PubMed  Google Scholar 

  106. LeCun, Y., Chopra, S. & Hadsell, R. in Predicting Structured Data (MIT Press, 2006).

  107. Kendall, A., Gal, Y. & Cipolla, R. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 7482–7491 (IEEE, 2018).

  108. Sevilla, J. et al. Compute trends across three eras of machine learning. In 2022 International Joint Conference on Neural Networks (IJCNN) 1–8 (IEEE, 2022).

  109. Brampton, C. K. Nominalism and the law of parsimony. Mod. Schoolman 41, 273–281 (1964).

    Article  Google Scholar 

  110. Bubeck, S. & Sellke, M. A universal law of robustness via isoperimetry. J. ACM 70, 10 (2023).

    Article  MathSciNet  Google Scholar 

  111. Schweidtmann, A. M. et al. Physical pooling functions in graph neural networks for molecular property prediction. Comput. Chem. Eng. 172, 108202 (2023).

    Article  CAS  Google Scholar 

  112. Hirschfeld, L., Swanson, K., Yang, K., Barzilay, R. & Coley, C. W. Uncertainty quantification using neural networks for molecular property prediction. J. Chem. Inf. Model. 60, 3770–3780 (2020).

    Article  CAS  PubMed  Google Scholar 

  113. Jiménez-Luna, J., Grisoni, F. & Schneider, G. Drug discovery with explainable artificial intelligence. Nat. Mach. Intell. 2, 573–584 (2020).

    Article  Google Scholar 

  114. Jiménez-Luna, J., Skalic, M., Weskamp, N. & Schneider, G. Coloring molecules with explainable artificial intelligence for preclinical relevance assessment. J. Chem. Inf. Model. 61, 1083–1094 (2021).

    Article  PubMed  Google Scholar 

  115. Plowright, A. et al. Hypothesis driven drug design: improving quality and effectiveness of the design-make-test-analyse cycle. Drug Discov. Today 17, 56–62 (2011).

    Article  PubMed  Google Scholar 

  116. Koren, Y., Bell, R. & Volinsky, C. Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009).

    Article  Google Scholar 

  117. Chen, Z. & Wang, S. A review on matrix completion for recommender systems. Knowl. Inf. Syst. 64, 1–34 (2022).

    Article  Google Scholar 

  118. Gogna, A. & Majumdar, A. Matrix completion incorporating auxiliary information for recommender system design. Expert Syst. Appl. 42, 5789–5799 (2015).

    Article  Google Scholar 

  119. Weisfeiler, B. Y. & Lehman, A. A. A reduction of a graph to a canonical form and an algebra arising during this reduction [Russian]. Nauchno-Technicheskaya Informatsia 2, 12–16 (1968).

    Google Scholar 

Download references

Acknowledgements

M. Hilleke, I. Pachón Angona, L. Cotos Muñoz, A. Sotiropoulou, J. Ledergerber, H. Wetton, M. Iff, C. Schiebroek, F. Lohmann, A. Ilnicka, P. Schneider, C. Isert and K. Atz are thanked for helpful discussions. This research was supported by the Swiss National Science Foundation (grants 205321_182176, 1-007655-000 and P500PT_214430).

Author information

Authors and Affiliations

  1. ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland

    Stephan Allenspach, Jan A. Hiss & Gisbert Schneider

Authors
  1. Stephan Allenspach
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan A. Hiss
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gisbert Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gisbert Schneider.

Ethics declarations

Competing interests

G.S. declares a potential financial conflict of interest as co-founder of inSili.com, Zurich, and in his role as scientific consultant to the pharmaceutical industry. The other authors declare no competing interests.

Peer review

Peer review information

Nature Machine Intelligence thanks Oliver Wieder, Paul Wrede and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Allenspach, S., Hiss, J.A. & Schneider, G. Neural multi-task learning in drug design. Nat Mach Intell 6, 124–137 (2024). https://doi.org/10.1038/s42256-023-00785-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-023-00785-4

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics