ReviewInformaticsArtificial intelligence in drug development: present status and future prospects
Graphical abstract
Introduction
Artificial intelligence (AI) is the simulation of the human intelligence process by computers. The process includes acquiring information, developing rules for using the information, drawing approximate or definite conclusions and self-correction. The advancement of AI can be seen as a double-edged sword: many fear that it will threaten their employment; by contrast, every advance in AI is celebrated because of the belief that it will vastly contribute to the betterment of society. AI is used in various sectors from innovating educational methods to automating business processes. The sprouting idea of adopting AI in the drug development process has shifted from hype to hope. In this review, the possible applications of AI in the drug development pipeline in drug development strategies and processes, the pharmaceutical R&D efficiency and attrition, and partnerships between AI and pharmaceutical companies are discussed.
Section snippets
AI, machine learning and deep learning
The incorporation of AI into the healthcare industry is concisely shown in Fig. 1. AI is described as the use of techniques that enable computers to mimic human behaviour (Fig. 1). AI also contains a subfield called machine learning (ML), which uses statistical methods with the ability to learn with or without being explicitly programmed 1, 2, 3. ML is categorised into supervised, unsupervised and reinforcement learning. Supervised learning comprises classification and regression methods where
Drug development process
The feedback-driven drug development process starts from existing results obtained from various sources such as high-throughput compound and fragment screening, computational modelling and information available in the literature. This process alternates between induction and deduction. This inductive–deductive cycle eventually leads to optimised hit and lead compounds. Automation of specific parts of the cycle reduces randomness and errors and improves the efficiency of drug development. De novo
Applications of AI in drug development
The tasks of finding successful new drugs is daunting and predominantly the most difficult part of drug development. This is caused by the vast size of what is known as chemical space, which is estimated to be in the order of 1060 molecules [27]. The technologies that incorporate AI have become versatile tools that can be applied ubiquitously in various stages of drug development, such as identification and validation of drug targets, designing of new drugs, drug repurposing, improving the R&D
Partnerships between the pharma industry and AI companies
With the rapid introduction of AI in healthcare, especially in the years 2016 and 2017, numerous pharmaceutical companies have made investments in and have joint ventures with AI companies in the hopes of developing better healthcare tools. These include the improvements in diagnostics or biomarkers and the identification of drug targets and designing new drugs [66]. The transition from general medicine to modern AI healthcare focuses on the basis of the data [67]. The analyses of these
Concluding remarks
Currently, there are no developed drugs that have utilised AI approaches but, based on the advances described in this review, it is likely that it will take a further 2–3 years for a drug to be developed. Interestingly, experts strongly believe that AI will permanently change the pharmaceutical industry and the way drugs are discovered. However, for an individual to be efficient in drug development using AI, the individual should know how to train algorithms, requiring domain expertise. This
Conflicts of interest
There are no funding sources to declare for the writing of this manuscript. Both authors contributed to the drafting, analysis, editing and submission of the manuscript.
Acknowledgements
The authors thank the International Medical University for the library facilities.
References (72)
The rise of deep learning in drug discovery
Drug Discov. Today
(2018)- et al.
Artificial intelligence in medicine
Metabolism
(2017) - et al.
Drug discovery and development: role of basic biological research
Alzheimers Dement.
(2017) Computational approaches in target identification and drug discovery
Comput. Struct. Biotechnol. J.
(2016)A data-driven approach to predicting successes and failures of clinical trials
Cell. Chem. Biol.
(2016)Machine intelligence decrypts β-lapachone as an allosteric 5-lipoxygenase inhibitor
Chem. Sci.
(2018)Strategies to design clinical studies to identify predictive biomarkers in cancer research
Cancer Treat. Rev.
(2017)The rise of artificial intelligence and the uncertain future for physicians
Eur. J. Intern. Med.
(2018)A new era of oncology through artificial intelligence
ESMO Open
(2017)Model-based machine learning
Philos. Trans. A Math. Phys. Eng. Sci.
(2013)
Emerging trends in geospatial artificial intelligence (geoAI): potential applications for environmental epidemiology
Environ. Health
Deep learning in medical imaging: general overview
Korean J. Radiol.
An application of machine learning to haematological diagnosis
Sci. Rep.
The rise and fall of machine learning methods in biomedical research
F1000 Res.
Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma
BMC Bioinf.
Machine learning and computer vision approaches for phenotypic profiling
J. Cell Biol.
Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy
Stroke
Artificial intelligence in healthcare: past, present and future
Stroke Vasc. Neurol.
How artificial intelligence is changing drug discovery
Nature
LigBuilder 2: a practical de novo drug design approach
J. Chem. Inf. Model.
Hit identification and optimization in virtual screening: practical recommendations based on a critical literature analysis
J. Med. Chem.
Structure-based functional design of drugs: from target to lead compound
Methods Mol. Biol.
Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery
J. Chem. Inf. Model.
Lead generation-enhancing the success of drug discovery by investing in the hit to lead process
Comb. Chem. High Throughput Screen.
The cost of drug development
N. Engl. J. Med.
Diagnosing the decline in pharmaceutical R&D efficiency
Nat. Rev. Drug Discov.
Polypharmacology: drug discovery for the future
Expert Rev. Clin. Pharmacol.
Big data in pharmaceutical R&D: creating a sustainable R&D engine
Pharm. Med.
Can the pharmaceutical industry reduce attrition rates?
Nat. Rev. Drug Discov.
Organs-on-chips at the frontiers of drug discovery
Nat. Rev. Drug Discov.
Ace revisited: a new target for structure-based drug design
Nat. Rev. Drug Discov.
Cross sectional survey of multicentre clinical databases in the United Kingdom
BMJ
Sample size calculations: basic principles and common pitfalls
Nephrol. Dial. Transplant.
Parsing clinical success rates
Nat. Rev. Drug Discov.
Generating focused molecule libraries for drug discovery with recurrent neural networks
ACS Cent. Sci.
Data mining for biomedicine and healthcare
J. Healthc. Eng.
Cited by (395)
GCNGAT: Drug–disease association prediction based on graph convolution neural network and graph attention network
2024, Artificial Intelligence in MedicineComputational resources and chemoinformatics for translational health research
2024, Advances in Protein Chemistry and Structural BiologyStrategies of Artificial intelligence tools in the domain of nanomedicine
2024, Journal of Drug Delivery Science and TechnologyExploring new horizons: Empowering computer-assisted drug design with few-shot learning
2023, Artificial Intelligence in the Life Sciences