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Review
. 2019 Nov 7;4(4):206-213.
doi: 10.1136/svn-2019-000290. eCollection 2019 Dec.

Artificial intelligence and big data facilitated targeted drug discovery

Benquan Liu  1 , Huiqin He  1 , Hongyi Luo  1 , Tingting Zhang  1 , Jingwei Jiang  1
Affiliations
Review

Artificial intelligence and big data facilitated targeted drug discovery

Benquan Liu et al. Stroke Vasc Neurol. .

Abstract

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.

Keywords: artificial intelligence; big data; targeted drug.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Schematic procedure of artificial intelligence (AI)-assisted virtual screening. Millions of structurally diverse chemical compounds are docked to a specific therapeutic target. AI scoring function is used to select the best hits from millions of docked results.
Figure 2
Figure 2
Schematic procedure of artificial intelligence (AI)-assisted reverse docking. More than 100 000 structurally diverse protein structures are reversely docked to a specific chemical compound/natural product. AI scoring function is used to select the best hits from millions of docked results.
Figure 3
Figure 3
AI-assisted ADMET properties prediction. (A) Deep learning algorithm to calculate logBB for a specific chemical compound. (B) Deep learning algorithm to calculate logPapp for a specific chemical compound. (C) PCA(Principal Component Analysis) analysis on 48 186 reverse-docked proteins for 55 FDA-approved drugs (yellow dots) and 224 FDA-withdrawn drugs (blue dots). (D) PLS-DA(Partial Least Squares Discriminant Analysis) analysis on 48 186 reverse-docked proteins for 55 FDA-approved drugs (blue dots) and 224 FDA-withdrawn drugs (yellow dots). ADMET, absorption, distribution, metabolism, excretion and toxicity; AI, artificial intelligence; FDA, Food and Drug Administration;TPSA,total polar surface area.
See this image and copyright information in PMC

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