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Artificial Intelligence in Drug Design pp 383–407Cite as

Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 2390))

Abstract

The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.

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Abbreviations

ADMET:

absorption, distribution, metabolism, excretion, toxicity

AI:

artificial intelligence

ANN :

artificial neural network

AUC:

area under the curve

AUROC:

area under the receiver operating characteristic curve

BA:

balanced accuracy

BKD:

binary kernel discrimination

CCLE:

Cancer Cell Line Encyclopedia

CCLP:

COSMIC Cell Lines Project

CNN :

convolutional neural network

CV:

cross-validation

DFT :

density functional theory

DILI :

drug-induced liver injury

DL:

deep learning

DMTA :

design–make–test–analyze

DNN :

deep neural network

DRD2:

dopamine receptor D2

DTNN:

deep tensor neural network

ECFP4:

extended connectivity fingerprint of diameter 4

FDA:

Food and Drug Administration

FNN:

feed-forward neural network

GCNN:

graph convolutional neural network

GDB-7:

generic database with up to 7 heavy atoms

GDSC:

genomics in drug sensitivity in cancer

GENTRL :

generative tensorial reinforcement learning

GPU :

graphics processing unit

GSE:

general solubility equation

hERG :

human Ether-à-go-go-Related Gene

HTS :

high-throughput screening

JAK:

Janus kinase

KNN:

k-nearest neighbor

LBVS :

ligand-based virtual screening

LINCS:

library of integrated network-based cellular signatures

LSTM :

long short-term memory

MCC:

Matthews correlation coefficient

MeSH:

medical subject headings

ML:

machine learning

MT:

multitasks

MTDL:

multitask deep learning

MTNN:

multitask neural network

NCI-60:

National Cancer Institute 60 human cancer cell lines

PAINS :

pan-assay interference

PPAR:

peroxisome proliferator-activated receptors

PPI:

protein–protein interaction

QM :

quantum mechanics

QSAR :

quantitative structure–activity relationship

QSPR :

quantitative structure–property relationship

RF :

random forest

RL :

reinforcement learning

RNN :

recurrent neural network

ROC:

receiver operating characteristic

RXR:

retinoid X receptors

SA:

synthetic accessibility

SBVS :

structure-based virtual screening

SEA:

similarity ensemble approach

SMILES :

simplified molecular input line entry specification,

SOM :

site of metabolism

SVM :

support vector machine

SVR:

support vector regression

VS:

virtual screening

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Acknowledgments

The authors thank Laurianne David and Martin Kotev (Evotec (France) SAS, Toulouse, France) for their contribution to the manuscript.

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Authors and Affiliations

  1. Evotec (France) SAS, Computational Drug Discovery, Integrated Drug Discovery, Toulouse, France

    Christophe Muller, Obdulia Rabal & Constantino Diaz Gonzalez

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  1. Christophe Muller
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  2. Obdulia Rabal
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  3. Constantino Diaz Gonzalez
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Correspondence to Constantino Diaz Gonzalez .

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  1. Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK

    Alexander Heifetz

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Muller, C., Rabal, O., Diaz Gonzalez, C. (2022). Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases. In: Heifetz, A. (eds) Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1787-8_16

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  • DOI: https://doi.org/10.1007/978-1-0716-1787-8_16

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