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Rethinking drug design in the artificial intelligence era.
Literature Information
| DOI | 10.1038/s41573-019-0050-3 |
|---|---|
| PMID | 31801986 |
| Journal | Nature reviews. Drug discovery |
| Impact Factor | 101.8 |
| JCR Quartile | Q1 |
| Publication Year | 2020 |
| Times Cited | 234 |
| Keywords | Artificial Intelligence, Drug Discovery, Small-Molecule Drugs, Biopharma, Challenges |
| Literature Type | Journal Article, Review |
| ISSN | 1474-1776 |
| Pages | 353-364 |
| Issue | 19(5) |
| Authors | Petra Schneider, W Patrick Walters, Alleyn T Plowright, Norman Sieroka, Jennifer Listgarten, Robert A Goodnow, Jasmin Fisher, Johanna M Jansen, José S Duca, Thomas S Rush, Matthias Zentgraf, John Edward Hill, Elizabeth Krutoholow, Matthias Kohler, Jeff Blaney, Kimito Funatsu, Chris Luebkemann, Gisbert Schneider |
TL;DR
This article discusses the integration of artificial intelligence (AI) in small-molecule drug discovery, highlighting both the potential opportunities and the skepticism surrounding its impact on the biopharma industry. By presenting insights from international experts on the significant challenges posed by AI, the research underscores the need for innovative approaches to enhance drug discovery processes.
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Artificial Intelligence · Drug Discovery · Small-Molecule Drugs · Biopharma · Challenges
Abstract
Artificial intelligence (AI) tools are increasingly being applied in drug discovery. While some protagonists point to vast opportunities potentially offered by such tools, others remain sceptical, waiting for a clear impact to be shown in drug discovery projects. The reality is probably somewhere in-between these extremes, yet it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' in small-molecule drug discovery with AI and the approaches to address them.
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Primary Questions Addressed
- How can AI specifically enhance the efficiency of small-molecule drug discovery compared to traditional methods?
- What are the ethical implications of using AI in drug design, particularly in relation to patient safety and data privacy?
- In what ways can AI address the challenges of drug resistance in existing therapies during the design phase?
- How do experts foresee the integration of AI tools changing the collaborative dynamics between researchers and pharmaceutical companies?
- What metrics should be established to evaluate the success of AI-driven drug discovery initiatives in the biopharma industry?
Key Findings
Key Insights: Rethinking Drug Design in the AI Era
Research Background and Purpose
The integration of artificial intelligence (AI) tools in drug discovery marks a significant shift in the biopharmaceutical industry. This research aims to explore the dual perspectives on the impact of AI in drug design—one that is optimistic about the transformative potential of AI and another that remains cautious, emphasizing the need for tangible results. The article seeks to highlight the challenges posed by AI in the context of small-molecule drug discovery and to gather insights from a diverse group of international experts on how to navigate these challenges.Main Methods and Findings
The article synthesizes opinions and insights from a variety of experts in the field. These experts identify several "grand challenges" associated with the application of AI in drug discovery, such as data quality and availability, the interpretability of AI models, and the integration of AI tools into established workflows. The findings suggest that while AI has the potential to optimize various stages of the drug discovery process—from target identification to lead optimization—there are significant barriers that need to be addressed. These include the need for standardized data formats, robust validation methods for AI-generated predictions, and interdisciplinary collaboration to ensure that AI tools are effectively integrated into existing frameworks.Core Conclusions
The article concludes that the journey of integrating AI in drug discovery is complex and multifaceted. While AI holds promise for enhancing efficiency and innovation in drug design, the realization of this potential is contingent upon overcoming existing challenges. The skepticism surrounding AI's effectiveness highlights the necessity for rigorous evaluation of AI methodologies and their outcomes in real-world drug discovery scenarios. Moreover, the dialogue among experts underscores the importance of a collaborative approach to foster innovation while addressing the ethical and practical implications of AI in biopharma.Research Significance and Impact
This research is significant as it sheds light on the current landscape of drug discovery in the era of AI, emphasizing the need for a balanced perspective that recognizes both opportunities and challenges. The insights derived from expert opinions provide valuable guidance for biopharma professionals as they navigate the integration of AI tools into their workflows. By addressing the identified challenges and fostering collaboration among stakeholders, the industry can better harness AI's potential, ultimately leading to more effective and efficient drug discovery processes. This research not only informs future strategies in drug design but also contributes to the broader discourse on the role of AI in healthcare innovation.
References
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Literatures Citing This Work
- Similarity-Based Methods and Machine Learning Approaches for Target Prediction in Early Drug Discovery: Performance and Scope. - Neann Mathai;Johannes Kirchmair - International journal of molecular sciences (2020)
- Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients. - Justin Stebbing;Venkatesh Krishnan;Stephanie de Bono;Silvia Ottaviani;Giacomo Casalini;Peter J Richardson;Vanessa Monteil;Volker M Lauschke;Ali Mirazimi;Sonia Youhanna;Yee-Joo Tan;Fausto Baldanti;Antonella Sarasini;Jorge A Ross Terres;Brian J Nickoloff;Richard E Higgs;Guilherme Rocha;Nicole L Byers;Douglas E Schlichting;Ajay Nirula;Anabela Cardoso;Mario Corbellino; - EMBO molecular medicine (2020)
- Strategies in Translating the Therapeutic Potentials of Host Defense Peptides. - Darren Shu Jeng Ting;Roger W Beuerman;Harminder S Dua;Rajamani Lakshminarayanan;Imran Mohammed - Frontiers in immunology (2020)
- Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology. - Adrián Bazaga;Dan Leggate;Hendrik Weisser - Scientific reports (2020)
- Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. - Linlin Zhao;Heather L Ciallella;Lauren M Aleksunes;Hao Zhu - Drug discovery today (2020)
- Accelerating Therapeutics for Opportunities in Medicine: A Paradigm Shift in Drug Discovery. - Izumi V Hinkson;Benjamin Madej;Eric A Stahlberg - Frontiers in pharmacology (2020)
- Mechanisms of Action for Small Molecules Revealed by Structural Biology in Drug Discovery. - Qingxin Li;CongBao Kang - International journal of molecular sciences (2020)
- Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques. - Rodrigo A Nava Lara;Jesús A Beltrán;Carlos A Brizuela;Gabriel Del Rio - Pharmaceuticals (Basel, Switzerland) (2020)
- Enhancing scientific discoveries in molecular biology with deep generative models. - Romain Lopez;Adam Gayoso;Nir Yosef - Molecular systems biology (2020)
- Crowdsourcing and open innovation in drug discovery: recent contributions and future directions. - David C Thompson;Jörg Bentzien - Drug discovery today (2020)
... (224 more literatures)
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