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Curated Papers > High-impact authoritative literature on "AI Drug Discovery"

Artificial intelligence in drug discovery and development. ​

Literature Information ​

DOI10.1016/j.drudis.2020.10.010
PMID33099022
JournalDrug discovery today
Impact Factor7.5
JCR QuartileQ1
Publication Year2021
Times Cited356
KeywordsArtificial Intelligence, Drug Discovery, Drug Development, Pharmaceutical Industry, Challenges
Literature TypeJournal Article, Research Support, Non-U.S. Gov't, Review
ISSN1359-6446
Pages80-93
Issue26(1)
AuthorsDebleena Paul, Gaurav Sanap, Snehal Shenoy, Dnyaneshwar Kalyane, Kiran Kalia, Rakesh K Tekade

TL;DR ​

This paper explores the transformative impact of artificial intelligence on drug discovery and development within the pharmaceutical industry, highlighting various integration areas, employed tools, and techniques. It also addresses ongoing challenges in the field and suggests potential solutions, emphasizing the significance of AI in enhancing pharmaceutical innovation and efficiency.

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Artificial Intelligence · Drug Discovery · Drug Development · Pharmaceutical Industry · Challenges

Abstract ​

Artificial intelligence-integrated drug discovery and development has accelerated the growth of the pharmaceutical sector, leading to a revolutionary change in the pharma industry. Here, we discuss areas of integration, tools, and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them.

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Primary Questions Addressed ​

  1. What specific AI tools and techniques are currently being used in drug discovery and how do they differ from traditional methods?
  2. How does the integration of AI in drug development impact the timelines and costs associated with bringing a new drug to market?
  3. What are the ethical considerations and regulatory challenges that arise with the use of AI in the pharmaceutical industry?
  4. In what ways can AI help in identifying potential side effects and improving drug safety during the development process?
  5. How is the role of data quality and availability affecting the effectiveness of AI applications in drug discovery?

Key Findings ​

Research Background and Purpose ​

The pharmaceutical industry has witnessed a significant transformation with the integration of Artificial Intelligence (AI) in drug discovery and development processes. This review highlights the potential applications of AI across various stages of drug development, including drug design, screening, clinical trials, and market analysis. The aim is to explore how AI can enhance efficiency, reduce costs, and improve the success rates of drug development.

Main Methods/Materials/Experimental Design ​

The review discusses several AI methodologies, focusing on machine learning (ML) and deep learning (DL) techniques. These methodologies are applied to analyze large datasets, predict drug interactions, and optimize drug designs. Key components of AI integration in drug discovery are illustrated in the following flowchart:

Mermaid diagram

Key Results and Findings ​

  1. Data Handling: AI systems can efficiently process vast amounts of pharmaceutical data, overcoming challenges related to data scale, diversity, and uncertainty.
  2. Drug Discovery Applications: AI enhances the identification of hit and lead compounds, optimizes drug structures, and accelerates the validation of drug targets.
  3. Clinical Trials: AI aids in patient selection for clinical trials, improving recruitment efficiency and reducing dropout rates.
  4. Market Positioning: AI tools help in analyzing market trends and consumer behavior, facilitating strategic product positioning.

Main Conclusions/Significance/Innovation ​

The integration of AI in the pharmaceutical sector represents a paradigm shift, promising to enhance productivity and innovation. AI can significantly shorten the drug development timeline, improve the accuracy of drug design, and reduce costs associated with failed trials. This transformative potential highlights the need for continued investment and research in AI technologies within the pharmaceutical industry.

Research Limitations and Future Directions ​

While the potential of AI is substantial, several challenges remain:

  • Data Quality: Access to high-quality, diverse datasets is crucial for effective AI training.
  • Skilled Workforce: There is a shortage of professionals skilled in both AI and pharmaceutical sciences.
  • Regulatory Concerns: The "black box" nature of AI algorithms raises questions about transparency and accountability in drug development.

Future research should focus on developing standardized protocols for AI integration, enhancing data sharing practices, and training interdisciplinary teams to leverage AI effectively in drug discovery and development.

References ​

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Literatures Citing This Work ​

  1. Challenges and advances in clinical applications of mesenchymal stromal cells. - Tian Zhou;Zenan Yuan;Jianyu Weng;Duanqing Pei;Xin Du;Chang He;Peilong Lai - Journal of hematology & oncology (2021)
  2. Presenting artificial intelligence, deep learning, and machine learning studies to clinicians and healthcare stakeholders: an introductory reference with a guideline and a Clinical AI Research (CAIR) checklist proposal. - Jakub Olczak;John Pavlopoulos;Jasper Prijs;Frank F A Ijpma;Job N Doornberg;Claes Lundström;Joel Hedlund;Max Gordon - Acta orthopaedica (2021)
  3. Enhancing Clinical Translation of Cancer Using Nanoinformatics. - Madjid Soltani;Farshad Moradi Kashkooli;Mohammad Souri;Samaneh Zare Harofte;Tina Harati;Atefeh Khadem;Mohammad Haeri Pour;Kaamran Raahemifar - Cancers (2021)
  4. Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. - Manish Kumar Tripathi;Abhigyan Nath;Tej P Singh;A S Ethayathulla;Punit Kaur - Molecular diversity (2021)
  5. Emerging Therapeutic Potential of SIRT6 Modulators. - Francesco Fiorentino;Antonello Mai;Dante Rotili - Journal of medicinal chemistry (2021)
  6. 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)
  7. Current Status of Baricitinib as a Repurposed Therapy for COVID-19. - Maha Saber-Ayad;Sarah Hammoudeh;Eman Abu-Gharbieh;Rifat Hamoudi;Hamadeh Tarazi;Taleb H Al-Tel;Qutayba Hamid - Pharmaceuticals (Basel, Switzerland) (2021)
  8. Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19. - Gunjan Arora;Jayadev Joshi;Rahul Shubhra Mandal;Nitisha Shrivastava;Richa Virmani;Tavpritesh Sethi - Pathogens (Basel, Switzerland) (2021)
  9. Blockchain and artificial intelligence technology in e-Health. - Priti Tagde;Sandeep Tagde;Tanima Bhattacharya;Pooja Tagde;Hitesh Chopra;Rokeya Akter;Deepak Kaushik;Md Habibur Rahman - Environmental science and pollution research international (2021)
  10. Computational healthcare: Present and future perspectives (Review). - Ayumu Asai;Masamitsu Konno;Masateru Taniguchi;Andrea Vecchione;Hideshi Ishii - Experimental and therapeutic medicine (2021)

... (346 more literatures)

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