Deep learning in drug discovery: applications and limitations

Yuxuan Tang

doi:10.54097/fcis.v3i2.7575

Authors

Yuxuan Tang

DOI:

https://doi.org/10.54097/fcis.v3i2.7575

Keywords:

Deep learning, Drug discovery, Prediction, Virtual screening, Drug toxicity, Machine learning, Artificial intelligence

Abstract

Drug discovery is a complex and challenging process that requires a significant amount of time and resources. The application of deep learning in drug discovery has the potential to revolutionize the field by offering more efficient and accurate methods for predicting drug-target interactions, designing new drugs, and predicting toxicity and side effects. However, there are also several limitations and challenges associated with the use of deep learning in drug discovery, including the lack of high-quality training data, overfitting and generalization issues, interpretability and explainability of deep learning models, and legal and ethical considerations. In this review article, we discuss the various applications of deep learning in drug discovery, provide examples of successful applications, and explore the potential benefits of using deep learning. We also discuss the limitations and challenges associated with the use of deep learning and suggest ways in which these challenges can be addressed. Furthermore, we discuss the future directions of research in this area, identify areas where more research is needed, and provide recommendations for future research. Overall, this review article highlights the potential of deep learning in drug discovery and provides insights into the challenges and opportunities associated with its use.

Downloads

Download data is not yet available.

References

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018). The rise of deep learning in drug discovery. Drug discovery today, 23(6), 1241-1250.

Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2012). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews, 64, 4-17.

Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010). How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature Reviews Drug Discovery, 9(3), 203-214.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., ... & Pande, V. S. (2018). MoleculeNet: a benchmark for molecular machine learning. Chemical science, 9(2), 513-530.

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., Svetnik, V., & Deep, J. (2015). Deep neural nets as a method for quantitative structure–activity relationships. Journal of chemical information and modeling, 55(2), 263-274.

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., ... & Collins, J. J. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688-702.

Goodfellow, Ian, et al. "Generative adversarial nets." Advances in Neural Information Processing Systems. 2014.

Kingma, Durk P., and Max Welling. "Auto-encoding variational Bayes." arXiv preprint arXiv:1312.6114 (2013).

Chen, Hongming, et al. "The rise of deep learning in drug discovery." Drug Discovery Today 23.6 (2018): 1241-1250.

Mayr, Andreas, et al. "DeepTox: toxicity prediction using deep learning." Frontiers in Environmental Science 3 (2016): 80.

Ching, Travers, et al. "Opportunities and obstacles for deep learning in biology and medicine." Journal of The Royal Society Interface 15.141 (2018): 20170387.

Ma, J., Sheridan, R.P., Liaw, A., Dahl, G.E., Svetnik, V. (2015). Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling, 55(2), 263-274.

Lavecchia, A. (2015). Machine-learning approaches in drug discovery: methods and applications. Drug Discovery Today, 20(3), 318-331.

Gao, Y., Yang, Z., & Li, Y. (2018). Protein-ligand binding site recognition via convolutional neural network. Journal of Chemical Information and Modeling, 58(4), 794-799.

Liu, S., Zhang, C., Liang, Y., Zhou, Y. (2019). Deep learning-based drug-target interaction prediction. Frontiers in Genetics, 10, 1199.

Zhang, H., Luan, S., Li, Y., Zhang, X. (2020). PPI-Predictor: a graph neural network-based model for protein-protein interaction prediction. Bioinformatics, 36(5), 1765-1771.

Öztürk, H., Ozkirimli, E. (2021). Transformer-based models for drug-target interaction prediction. Journal of Chemical Information and Modeling, 61(4), 2068-2080.

Ozturk, H., Ozkirimli, E., & Ozgur, A. (2021). Ensemble learning methods for drug-target interaction prediction. Frontiers in Genetics, 12, 743599.

Zitnik, M., Agrawal, M., & Leskovec, J. (2019). Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics, 35(11), i438-i446.

Wang, T., Zhang, J., Huang, H., Cai, Y. D., & He, Z. G. (2018). Ensemble learning with multiple graph convolutional neural networks for drug discovery. Bioinformatics, 34(13), i457-i466.

Khor, S., & Jap, J. (2021). Virtual Screening in Drug Discovery: A Review. Current computer-aided drug design, 17(4), 358-371.

Gao, H., & Sun, Y. (2020). Deep learning in virtual screening: recent progress and challenges. Expert opinion on drug discovery, 15(6), 665-677.

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., ... & Kohli, P. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583-589.

Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

Jiménez, J., Doerr, S., Martínez-Rosell, G., Rose, A. S., & De Fabritiis, G. (2017). DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics, 33(19), 3036-3042.

Li, Y., Chen, K., Li, L., & Li, Y. (2019). Fragment-based drug design for developing personalized therapy against cancer. Current topics in medicinal chemistry, 19(30), 2809-2826.

Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., & Ceulemans, H. (2016). Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chemical science, 7(11), 7161-7171.

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016). Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular pharmaceutics, 13(7), 2524-2530.

Sedykh, A., et al. (2018). Use of in vitro HTS-Derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity in rats. Environmental Health Perspectives, 126(4), 047012.

Mayr, A., et al. (2016). DeepTox: Toxicity Prediction using Deep Learning. Frontiers in Environmental Science, 3, 80.

Sun, J., et al. (2019). DeepDDI: Predicting Drug-Drug Interactions via Deep Learning. Journal of Chemical Information and Modeling, 59(8), 3369-3378.

Sakkiah, S., et al. (2017). DeepCoyote: A deep neural network based approach for prediction of acute oral toxicity in rats. Journal of Cheminformatics, 9, 51.

Krenn, M., et al. (2018). Self-Organizing Molecules in a Synthetic Feedback Loop with Machine Learning. Physical Review Letters, 120(11), 118001.

Brown, N., & Fiscato, M. (2021). De novo drug design by deep learning: current progress and future prospects. Drug discovery today, 26(5), 1265-1281.

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., ... & Aspuru-Guzik, A. (2018). Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, 4(2), 268-276.

Olivecrona, M., Blaschke, T., Engkvist, O., Chen, H., & Bjerrum, E. J. (2017). Molecular de-novo design through deep reinforcement learning. Journal of cheminformatics, 9(1), 48.

Segler, M. H., Preuss, M., & Waller, M. P. (2018). Planning chemical syntheses with deep neural networks and symbolic AI. Nature, 555(7698), 604–610. doi: 10.1038/nature25978.

Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Luu Nguyen, Q., Ho, S., Sloane, J., Wender, P., & Pande, V. (2018). Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Central Science, 4(10), 1377–1384. doi: 10.1021/acscentsci.8b00331.

Zhou, Z., Kearnes, S., Li, L., Zare, R. N., & Riley, P. (2019). Optimization of Molecules via Deep Reinforcement Learning. Scientific Reports, 9, 10752. doi: 10.1038/s41598-019-47148-x.

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., & Asadulaev, A. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040. doi: 10.1038/s41587-019-0224-x.

Li, Y., Zhang, L., Liu, Z., & Xu, H. (2018). Multi-objective de novo drug design with conditional graph generative model. Journal of Chemical Information and Modeling, 58(2).

Ma, K., Wei, J., Xie, H., & Zhang, H. (2021). One-shot ensemble learning of deep generative models for de novo drug design. Nature Machine Intelligence, 3(10), 827-836.

Wallach, I., Dzamba, M., Heifets, A. AtomNet: A Deep Learning Solution for Structure-Based Drug Discovery. arXiv:1510.02855 [q-bio.BM] (2015).

Hugo, W., Zaretsky, J. M., Sun, L., Song, C., Moreno, B. H., Hu-Lieskovan, S., ... & Ribas, A. (2016). Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell, 165(1), 35-44.