Literature Information

DOI10.3390/life14020233
PMID38398742
JournalLife (Basel, Switzerland)
Impact Factor3.4
JCR QuartileQ1
Publication Year2024
Times Cited52
Keywordsartificial intelligence, deep learning, drug discovery, drug repurposing, machine learning
Literature TypeJournal Article, Review
ISSN2075-1729
Issue14(2)
AuthorsAnita Ioana Visan, Irina Negut

TL;DR

This paper examines the transformative role of artificial intelligence (AI) in drug discovery, highlighting its applications in drug design, target identification, and virtual screening, while also discussing the historical context and future directions of AI in medicine. The findings underscore AI’s potential to enhance drug delivery systems and facilitate the repositioning of existing drugs, ultimately aiming to revolutionize the pharmaceutical industry by addressing its inherent challenges.

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artificial intelligence · deep learning · drug discovery · drug repurposing · machine learning

Abstract

Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI’s role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.

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Primary Questions Addressed

  1. How can machine learning algorithms be further optimized for drug design and delivery systems?
  2. What specific challenges do AI methodologies face in the context of drug repositioning and combination therapy?
  3. In what ways can AI enhance the accuracy of target identification in drug discovery?
  4. What are the implications of integrating deep learning techniques into existing drug discovery workflows?
  5. How do historical advancements in AI inform current practices in pharmaceutical research and development?

Key Findings

Research Theme and Scope

This review discusses the transformative role of artificial intelligence (AI) in drug discovery, emphasizing its applications in drug delivery design, the identification of new drugs, and the development of novel AI techniques. The authors highlight various AI methodologies, including machine learning (ML), deep learning (DL), and natural language processing (NLP), and their impact on the pharmaceutical industry.

Major Findings and Perspectives

  1. Historical Context: The review outlines the evolution of AI in medicine, noting significant milestones from the 1950s to present, including the development of expert systems and advancements in medical imaging.

  2. AI Applications in Drug Discovery:

    • Target Identification and Validation: AI facilitates the identification of biological targets by analyzing genomic and proteomic data. ML algorithms help validate these targets for their therapeutic potential.
    • Virtual Screening and Drug Design: AI-powered tools accelerate drug design by predicting interactions between drug candidates and target proteins, optimizing lead compounds, and assessing binding affinities.
    • Prediction of Drug Properties: AI models predict physicochemical properties such as solubility and toxicity, which are critical for drug development.
    • Drug Repositioning: AI can identify new therapeutic uses for existing drugs by analyzing drug-target interactions and disease pathways.

  3. AI Techniques in Material Discovery: The review discusses how AI techniques used in materials science can be applied to drug discovery, including supervised and unsupervised learning methods, regression analysis, and clustering techniques.

Research Progress

The review summarizes advancements in AI applications within drug discovery, highlighting collaborations between pharmaceutical companies and AI providers. Notable partnerships are illustrated in a table that lists various projects, showcasing the integration of AI in developing new drugs and improving existing therapies.

Controversies and Limitations

Despite the promising advancements, the review acknowledges several challenges:

  • Data Quality: The success of AI models heavily relies on high-quality data. Incomplete or biased datasets can lead to inaccurate predictions.
  • Interpretability: Understanding how AI models make predictions remains a challenge, which is crucial for further research and regulatory approval.
  • Ethical Considerations: There are concerns regarding data privacy, the environmental impact of AI-driven processes, and the need for responsible AI use in healthcare.

Future Research Directions

The authors propose several future research directions:

  • Continued development of AI algorithms that can handle complex biological data.
  • Improved integration of AI with existing drug development processes.
  • Exploration of AI’s potential in personalized medicine, particularly in patient stratification and real-time monitoring of treatment responses.

Conclusion and Implications

The review concludes that AI holds significant potential to revolutionize drug discovery by improving efficiency, reducing costs, and enabling the design of targeted therapies. As AI technologies advance, they are expected to play an increasingly central role in pharmaceutical research, leading to more effective and personalized treatment options for patients. The authors emphasize the importance of interdisciplinary collaboration and ongoing research to address the challenges faced in the integration of AI in drug discovery.

Key Takeaways

  • AI significantly enhances various stages of drug discovery, from target identification to virtual screening and drug repositioning.
  • The field is evolving rapidly, with increasing interest and investment from pharmaceutical companies.
  • Addressing data quality, ethical considerations, and model interpretability is crucial for the successful implementation of AI in drug discovery.
  • Future advancements in AI could lead to fully autonomous drug discovery processes, improving the speed and efficacy of bringing new therapies to market.

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  1. Dental biomaterials redefined: molecular docking and dynamics-driven dental resin composite optimization. - Ravinder S Saini;Rayan Ibrahim H Binduhayyim;Vishwanath Gurumurthy;Abdulkhaliq Ali F Alshadidi;Lujain Ibrahim N Aldosari;Abdulmajeed Okshah;Mohamed Saheer Kuruniyan;Doni Dermawan;Anna Avetisyan;Seyed Ali Mosaddad;Artak Heboyan - BMC oral health (2024)
  2. The Applications of Artificial Intelligence (AI)-Driven Tools in Virus-Like Particles (VLPs) Research. - Bugude Laxmi;Palempalli Uma Maheswari Devi;Naveen Thanjavur;Viswanath Buddolla - Current microbiology (2024)
  3. Identification and Design of Novel Potential Antimicrobial Peptides Targeting Mycobacterial Protein Kinase PknB. - Hemchandra Deka;Atul Pawar;Monishka Battula;Ayman A Ghfar;Mohamed E Assal;Rupesh V Chikhale - The protein journal (2024)
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  7. Artificial intelligence as a tool in drug discovery and development. - Maria Kokudeva;Mincho Vichev;Emilia Naseva;Dimitrina Georgieva Miteva;Tsvetelina Velikova - World journal of experimental medicine (2024)
  8. Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions. - Tuan D Pham;Muy-Teck Teh;Domniki Chatzopoulou;Simon Holmes;Paul Coulthard - Current oncology (Toronto, Ont.) (2024)
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  10. Empowering precision medicine: regenerative AI in breast cancer. - Sudip Bhattacharya;Sheikh Mohd Saleem;Alok Singh;Sukhpreet Singh;Shailesh Tripathi - Frontiers in oncology (2024)

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