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Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet.
Literature Information
| DOI | 10.1016/j.drudis.2020.12.009 |
|---|---|
| PMID | 33346134 |
| Journal | Drug discovery today |
| Impact Factor | 7.5 |
| JCR Quartile | Q1 |
| Publication Year | 2021 |
| Times Cited | 77 |
| Keywords | Artificial Intelligence, Drug Discovery, Clinical Success Rate, Decision Quality, Drug Efficacy |
| Literature Type | Journal Article, Review |
| ISSN | 1359-6446 |
| Pages | 511-524 |
| Issue | 26(2) |
| Authors | Andreas Bender, Isidro Cortés-Ciriano |
TL;DR
This article highlights the limited advances of artificial intelligence in drug discovery compared to its impact in other fields, emphasizing that improvements in clinical success rates are more crucial than speed or cost for bringing new drugs to market. It argues that focusing on the selection of compounds based on clinical efficacy and safety is essential for fully harnessing AI's potential in drug development, suggesting that future research should concentrate on generating relevant data and modeling appropriate endpoints.
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Artificial Intelligence · Drug Discovery · Clinical Success Rate · Decision Quality · Drug Efficacy
Abstract
Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. This article quantifies the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost. Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.
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Primary Questions Addressed
- What specific challenges do researchers face when integrating AI into the early stages of drug discovery?
- How can AI be utilized to improve decision-making regarding which compounds to advance in clinical trials?
- What types of data are most critical for enhancing the predictive capabilities of AI in drug efficacy and safety?
- In what ways can the current methodologies in drug discovery be adapted to better leverage AI technologies?
- What are the implications of focusing on clinical success rates versus speed and cost in the context of AI-driven drug discovery?
Key Findings
1. Research Background and Objective: The integration of artificial intelligence (AI) into drug discovery has garnered significant attention due to its transformative potential in various fields, notably image recognition. However, the same level of progress has not been realized in drug discovery, which remains a complex and costly process. The objective of this research is to explore the ways in which AI can realistically impact drug discovery, to identify the barriers that have hindered its effective application, and to emphasize the importance of focusing on decision-making quality in the drug development process.
2. Main Methods and Findings: The article quantifies the different stages of drug discovery where AI could make a major impact, particularly emphasizing the importance of clinical success rates. The authors argue that improving the quality of decisions regarding which compounds to advance and how to conduct clinical trials is more critical than merely focusing on reducing time or costs. The current advancements in AI predominantly concentrate on the synthesis of compounds rather than the identification of the most promising candidates based on clinical efficacy and safety. This oversight leads to a suboptimal utilization of available data and AI capabilities, particularly regarding in vivo drug efficacy and safety evaluations. The authors highlight that current proxy measures and available datasets are insufficient to leverage AI's full potential in enhancing drug discovery outcomes.
3. Core Conclusions: The key conclusion drawn from the research is that while AI holds significant promise for improving drug discovery, its current applications are limited by a lack of focus on critical decision-making factors. The study emphasizes that enhancing clinical success rates through better-informed choices about drug candidates is paramount. To achieve this, it is essential to generate the right data and model appropriate endpoints that reflect clinical efficacy and safety, thereby facilitating more effective and informed decision-making in drug development.
4. Research Significance and Impact: The significance of this research lies in its identification of the critical gaps in the application of AI within drug discovery. It challenges the prevailing focus on speed and cost reduction and redirects attention to the importance of strategic decision-making based on robust clinical data. By advocating for a shift in the approach to AI in drug discovery, the findings have the potential to reshape research priorities and methodologies. This could lead to a more effective utilization of AI tools, ultimately resulting in higher success rates for new drug approvals and more efficient drug development processes, thereby benefiting the pharmaceutical industry and public health at large.
Literatures Citing This Work
- Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data. - Andreas Bender;Isidro Cortes-Ciriano - Drug discovery today (2021)
- Deep Learning in Virtual Screening: Recent Applications and Developments. - Talia B Kimber;Yonghui Chen;Andrea Volkamer - International journal of molecular sciences (2021)
- Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises. - Natesh Singh;Bruno O Villoutreix - Computational and structural biotechnology journal (2021)
- AI-based language models powering drug discovery and development. - Zhichao Liu;Ruth A Roberts;Madhu Lal-Nag;Xi Chen;Ruili Huang;Weida Tong - Drug discovery today (2021)
- AI in drug development: a multidisciplinary perspective. - Víctor Gallego;Roi Naveiro;Carlos Roca;David Ríos Insua;Nuria E Campillo - Molecular diversity (2021)
- Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis. - Alexander D H Kingdon;Luke J Alderwick - Computational and structural biotechnology journal (2021)
- Mechanism of activation and the rewired network: New drug design concepts. - Ruth Nussinov;Mingzhen Zhang;Ryan Maloney;Chung-Jung Tsai;Bengi Ruken Yavuz;Nurcan Tuncbag;Hyunbum Jang - Medicinal research reviews (2022)
- Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19. - Indira Mikkili;Abraham Peele Karlapudi;T C Venkateswarulu;Vidya Prabhakar Kodali;Deepika Sri Singh Macamdas;Krupanidhi Sreerama - PeerJ (2021)
- Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges. - Morgan Thomas;Andrew Boardman;Miguel Garcia-Ortegon;Hongbin Yang;Chris de Graaf;Andreas Bender - Methods in molecular biology (Clifton, N.J.) (2022)
- Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases. - Adam Bess;Frej Berglind;Supratik Mukhopadhyay;Michal Brylinski;Nicholas Griggs;Tiffany Cho;Chris Galliano;Kishor M Wasan - Drug discovery today (2022)
... (67 more literatures)
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