Curated Papers > High-Impact Authoritative Literature on "AI Drug Discovery"

Advancing Drug Discovery via Artificial Intelligence.

Literature Information

DOI	10.1016/j.tips.2019.06.004
PMID	31320117
Journal	Trends in pharmacological sciences
Impact Factor	19.9
JCR Quartile	Q1
Publication Year	2019
Times Cited	150
Keywords	QSAR, artificial intelligence, drug design, drug discovery, planning chemical syntheses
Literature Type	Journal Article, Research Support, Non-U.S. Gov't, Review
ISSN	0165-6147
Pages	592-604
Issue	40(8)
Authors	H C Stephen Chan, Hanbin Shan, Thamani Dahoun, Horst Vogel, Shuguang Yuan

TL;DR

This paper explores the integration of artificial intelligence (AI) with novel experimental technologies to enhance the drug discovery process, which is currently hindered by high costs and lengthy timelines. By leveraging AI, the research aims to make the development of new pharmaceuticals faster, more cost-effective, and ultimately more successful, addressing a critical challenge in the pharmaceutical industry.

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QSAR · artificial intelligence · drug design · drug discovery · planning chemical syntheses

Abstract

Drug discovery and development are among the most important translational science activities that contribute to human health and wellbeing. However, the development of a new drug is a very complex, expensive, and long process which typically costs 2.6 billion USD and takes 12 years on average. How to decrease the costs and speed up new drug discovery has become a challenging and urgent question in industry. Artificial intelligence (AI) combined with new experimental technologies is expected to make the hunt for new pharmaceuticals quicker, cheaper, and more effective. We discuss here emerging applications of AI to improve the drug discovery process.

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Primary Questions Addressed

1. What specific AI techniques are currently being utilized to streamline the drug discovery process?
2. How can AI help in predicting the efficacy and safety of new drug candidates during the early stages of development?
3. What role do emerging experimental technologies play in enhancing the capabilities of AI in drug discovery?
4. In what ways can the integration of AI in drug discovery reduce the time and costs associated with clinical trials?
5. How are regulatory agencies adapting to the use of AI in drug discovery and what implications does this have for the industry?

Key Findings

Key Insights

1. Research Background and Purpose
   The process of drug discovery and development is crucial for enhancing human health and well-being. However, it is notoriously complex, costly, and time-consuming, with an average expenditure of approximately 2.6 billion USD and a timeline of around 12 years to bring a new drug to market. Given these challenges, the pharmaceutical industry faces an urgent need to find innovative solutions to decrease both the financial and temporal burdens associated with drug development. The purpose of this research is to explore how artificial intelligence (AI), in conjunction with advanced experimental technologies, can revolutionize the drug discovery process, making it more efficient and cost-effective.

2. Major Methods and Findings
   The study discusses various emerging applications of AI within the drug discovery landscape. AI techniques, such as machine learning and deep learning, have been identified as pivotal in enhancing different stages of drug development, including target identification, lead optimization, and clinical trial design. By analyzing vast datasets, AI can uncover patterns and predict molecular interactions, which traditional methods may overlook. The integration of AI with experimental technologies allows for rapid hypothesis testing and optimization, significantly accelerating the identification of potential drug candidates. The findings suggest that leveraging AI can lead to a more streamlined drug discovery pipeline, with the potential to reduce costs and timelines substantially.

3. Core Conclusions
   The research concludes that the integration of AI into drug discovery processes holds great promise for transforming the pharmaceutical industry. By utilizing AI, companies can not only enhance the speed of drug development but also improve the accuracy and effectiveness of their discoveries. This technological advancement could lead to a new paradigm in how drugs are developed, enabling faster responses to public health needs and potentially resulting in better health outcomes for patients.

4. Research Significance and Impact
   The significance of this research lies in its potential to address one of the most pressing challenges in the pharmaceutical industry: the high cost and lengthy duration of drug development. By adopting AI-driven approaches, the industry could witness a paradigm shift that allows for more efficient utilization of resources and a greater focus on innovation. Moreover, the successful application of AI in drug discovery can lead to the introduction of novel therapies at a fraction of the current cost and time, ultimately improving access to medications and enhancing the overall quality of healthcare. This impact extends beyond the pharmaceutical industry, potentially influencing economic factors, healthcare policies, and patient outcomes on a global scale.

Literatures Citing This Work

1. Advances and Challenges in Rational Drug Design for SLCs. - Rachel-Ann A Garibsingh;Avner Schlessinger - Trends in pharmacological sciences (2019)
2. Strategies for targeting the cardiac sarcomere: avenues for novel drug discovery. - Joshua B Holmes;Chang Yoon Doh;Ranganath Mamidi;Jiayang Li;Julian E Stelzer - Expert opinion on drug discovery (2020)
3. AlloSigMA 2: paving the way to designing allosteric effectors and to exploring allosteric effects of mutations. - Zhen Wah Tan;Enrico Guarnera;Wei-Ven Tee;Igor N Berezovsky - Nucleic acids research (2020)
4. Discovering Anti-Cancer Drugs via Computational Methods. - Wenqiang Cui;Adnane Aouidate;Shouguo Wang;Qiuliyang Yu;Yanhua Li;Shuguang Yuan - Frontiers in pharmacology (2020)
5. Generative Model for Proposing Drug Candidates Satisfying Anticancer Properties Using a Conditional Variational Autoencoder. - Sunghoon Joo;Min Soo Kim;Jaeho Yang;Jeahyun Park - ACS omega (2020)
6. Artificial intelligence in drug discovery and development. - Debleena Paul;Gaurav Sanap;Snehal Shenoy;Dnyaneshwar Kalyane;Kiran Kalia;Rakesh K Tekade - Drug discovery today (2021)
7. Natural outbreaks and bioterrorism: How to deal with the two sides of the same coin? - Lionel Koch;Anne-Aurelie Lopes;Avelina Maiguy;Sophie Guillier;Laurent Guillier;Jean-Nicolas Tournier;Fabrice Biot - Journal of global health (2020)
8. Machine Learning Methods in Drug Discovery. - Lauv Patel;Tripti Shukla;Xiuzhen Huang;David W Ussery;Shanzhi Wang - Molecules (Basel, Switzerland) (2020)
9. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. - Sezen Vatansever;Avner Schlessinger;Daniel Wacker;H Ümit Kaniskan;Jian Jin;Ming-Ming Zhou;Bin Zhang - Medicinal research reviews (2021)
10. Marine-Derived Biologically Active Compounds for the Potential Treatment of Rheumatoid Arthritis. - Muhammad Bilal;Maimoona Qindeel;Leonardo Vieira Nunes;Marco Thúlio Saviatto Duarte;Luiz Fernando Romanholo Ferreira;Renato Nery Soriano;Hafiz M N Iqbal - Marine drugs (2020)

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