Learning to Discover Medicines

Overview

Journal Int J Data Sci Anal

Date 2022 Nov 28

PMID 36440369

Authors

Minh-Tri Nguyen

Thin Nguyen

Truyen Tran

Affiliations

Soon will be listed here.

Abstract

Discovering new medicines is the hallmark of the human endeavor to live a better and longer life. Yet the pace of discovery has slowed down as we need to venture into more wildly unexplored biomedical space to find one that matches today's high standard. Modern AI-enabled by powerful computing, large biomedical databases, and breakthroughs in deep learning offers a new hope to break this loop as AI is rapidly maturing, ready to make a huge impact in the area. In this paper, we review recent advances in AI methodologies that aim to crack this challenge. We organize the vast and rapidly growing literature on AI for drug discovery into three relatively stable sub-areas: (a) over molecular sequences and geometric graphs; (b) where we predict molecular properties and their binding, optimize existing compounds, generate molecules, and plan the synthesis of target molecules; and (c) where we discuss the construction and reasoning over biomedical knowledge graphs. We will also identify open challenges and chart possible research directions for the years to come.

References

Yi H, You Z, Cheng L, Zhou X, Jiang T, Li X . Learning distributed representations of RNA and protein sequences and its application for predicting lncRNA-protein interactions. Comput Struct Biotechnol J. 2020; 18:20-26. PMC: 6926125. DOI: 10.1016/j.csbj.2019.11.004. View

Nguyen T, Nguyen T, Le T, Tran T . GEFA: Early Fusion Approach in Drug-Target Affinity Prediction. IEEE/ACM Trans Comput Biol Bioinform. 2021; 19(2):718-728. DOI: 10.1109/TCBB.2021.3094217. View

He T, Heidemeyer M, Ban F, Cherkasov A, Ester M . SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. J Cheminform. 2017; 9(1):24. PMC: 5395521. DOI: 10.1186/s13321-017-0209-z. View

Huang K, Xiao C, Glass L, Sun J . MolTrans: Molecular Interaction Transformer for drug-target interaction prediction. Bioinformatics. 2020; 37(6):830-836. PMC: 8098026. DOI: 10.1093/bioinformatics/btaa880. View

Zhao Q, Zhao H, Zheng K, Wang J . HyperAttentionDTI: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism. Bioinformatics. 2021; 38(3):655-662. DOI: 10.1093/bioinformatics/btab715. View

Berdigaliyev N, Aljofan M . An overview of drug discovery and development. Future Med Chem. 2020; 12(10):939-947. DOI: 10.4155/fmc-2019-0307. View

Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W . A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun. 2017; 8(1):573. PMC: 5603535. DOI: 10.1038/s41467-017-00680-8. View

Thafar M, Raies A, Albaradei S, Essack M, Bajic V . Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities. Front Chem. 2019; 7:782. PMC: 6879652. DOI: 10.3389/fchem.2019.00782. View

Altae-Tran H, Ramsundar B, Pappu A, Pande V . Low Data Drug Discovery with One-Shot Learning. ACS Cent Sci. 2017; 3(4):283-293. PMC: 5408335. DOI: 10.1021/acscentsci.6b00367. View

10.

Bickerton G, Paolini G, Besnard J, Muresan S, Hopkins A . Quantifying the chemical beauty of drugs. Nat Chem. 2012; 4(2):90-8. PMC: 3524573. DOI: 10.1038/nchem.1243. View

11.

El-Gebali S, Mistry J, Bateman A, Eddy S, Luciani A, Potter S . The Pfam protein families database in 2019. Nucleic Acids Res. 2018; 47(D1):D427-D432. PMC: 6324024. DOI: 10.1093/nar/gky995. View

12.

Winter R, Montanari F, Steffen A, Briem H, Noe F, Clevert D . Efficient multi-objective molecular optimization in a continuous latent space. Chem Sci. 2019; 10(34):8016-8024. PMC: 6836962. DOI: 10.1039/c9sc01928f. View

13.

Ozturk H, Ozgur A, Ozkirimli E . DeepDTA: deep drug-target binding affinity prediction. Bioinformatics. 2018; 34(17):i821-i829. PMC: 6129291. DOI: 10.1093/bioinformatics/bty593. View

14.

Kundu I, Paul G, Banerjee R . A machine learning approach towards the prediction of protein-ligand binding affinity based on fundamental molecular properties. RSC Adv. 2022; 8(22):12127-12137. PMC: 9079328. DOI: 10.1039/c8ra00003d. View

15.

Suzek B, Wang Y, Huang H, McGarvey P, Wu C . UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics. 2014; 31(6):926-32. PMC: 4375400. DOI: 10.1093/bioinformatics/btu739. View

16.

Zhou Z, Kearnes S, Li L, Zare R, Riley P . Optimization of Molecules via Deep Reinforcement Learning. Sci Rep. 2019; 9(1):10752. PMC: 6656766. DOI: 10.1038/s41598-019-47148-x. View

17.

Li J, Sun Y, Johnson R, Sciaky D, Wei C, Leaman R . BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database (Oxford). 2016; 2016. PMC: 4860626. DOI: 10.1093/database/baw068. View

18.

Stepniewska-Dziubinska M, Zielenkiewicz P, Siedlecki P . Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics. 2018; 34(21):3666-3674. PMC: 6198856. DOI: 10.1093/bioinformatics/bty374. View

19.

Sandberg M, Eriksson L, Jonsson J, Sjostrom M, Wold S . New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J Med Chem. 1998; 41(14):2481-91. DOI: 10.1021/jm9700575. View

20.

Durant J, Leland B, Henry D, Nourse J . Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci. 2002; 42(6):1273-80. DOI: 10.1021/ci010132r. View