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Artificial intelligence in drug development: present status and future prospects.
Literature Information
| DOI | 10.1016/j.drudis.2018.11.014 |
|---|---|
| PMID | 30472429 |
| Journal | Drug discovery today |
| Impact Factor | 7.5 |
| JCR Quartile | Q1 |
| Publication Year | 2019 |
| Times Cited | 188 |
| Keywords | Artificial Intelligence, Drug Development, Efficiency Improvement, Pharmaceutical Industry, R&D Costs |
| Literature Type | Journal Article, Review |
| ISSN | 1359-6446 |
| Pages | 773-780 |
| Issue | 24(3) |
| Authors | Kit-Kay Mak, Mallikarjuna Rao Pichika |
TL;DR
This review highlights the potential of artificial intelligence (AI) to transform drug development amidst rising R&D costs and inefficiencies faced by the pharmaceutical industry. It discusses the causes of high attrition rates in drug approvals and explores how AI can enhance development efficiency through collaborations with AI-driven discovery firms.
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Artificial Intelligence · Drug Development · Efficiency Improvement · Pharmaceutical Industry · R&D Costs
Abstract
Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.
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Primary Questions Addressed
- How can AI specifically address the challenges of high attrition rates in drug development?
- What are some successful case studies of AI implementation in the pharmaceutical industry?
- In what ways could AI technology evolve to further enhance drug discovery processes in the future?
- What are the potential ethical implications of using AI in drug development and approval processes?
- How do collaborations between pharmaceutical companies and AI firms impact the overall efficiency of drug development?
Key Findings
Key Insights
1. Research Background and Purpose
The research paper explores the integration of artificial intelligence (AI) into the drug development process, addressing the pressing challenges currently faced by the pharmaceutical industry. Drug development is an intricate and costly endeavor, marked by high attrition rates and escalating R&D expenses. The purpose of the review is to assess the current state of AI in this field, examine the underlying causes of inefficiencies in drug development, and discuss potential future applications of AI that could enhance the process.
2. Main Methods and Findings
The authors systematically analyze the challenges that hinder drug development, such as lengthy timelines, high costs, and the high rate of failure in drug approvals. They highlight AI's potential to streamline various phases of drug development, from initial discovery through clinical trials. The review suggests that AI can enhance data analysis, predict drug interactions, and optimize clinical trial designs. Furthermore, it emphasizes the trend of collaboration between traditional pharmaceutical companies and AI-driven drug discovery firms, which is increasingly seen as a strategic partnership to leverage advanced computational techniques and machine learning algorithms.
3. Core Conclusions
The review concludes that AI has the potential to significantly transform the drug development landscape by improving efficiency and reducing costs. By harnessing AI technologies, the pharmaceutical industry can address the high attrition rates in drug approvals and overall inefficiencies in the R&D process. The collaboration between established pharmaceutical companies and innovative AI firms is deemed crucial for overcoming current challenges and accelerating drug discovery.
4. Research Significance and Impact
This research underscores the transformative role of AI in drug development, positioning it as a pivotal tool for the future of pharmaceuticals. The insights provided highlight not only the potential for AI to resolve existing inefficiencies but also the need for a paradigm shift in how drug discovery is approached. The implications of this study are far-reaching, as successful integration of AI could lead to faster, cheaper, and more effective drug development, ultimately benefiting public health and leading to more innovative therapeutic options. As the pharmaceutical industry continues to evolve, embracing AI will be essential for sustaining competitive advantages and meeting the growing demand for new medications.
Literatures Citing This Work
- A Structure-Based Drug Discovery Paradigm. - Maria Batool;Bilal Ahmad;Sangdun Choi - International journal of molecular sciences (2019)
- Neuronal Circuit-Based Computer Modeling as a Phenotypic Strategy for CNS R&D. - Hugo Geerts;James E Barrett - Frontiers in neuroscience (2019)
- Pharmacy Informatics: Where Medication Use and Technology Meet. - Daniel Cortes;Jodie Leung;Andrea Ryl;Jenny Lieu - The Canadian journal of hospital pharmacy (2019)
- DeepScreening: a deep learning-based screening web server for accelerating drug discovery. - Zhihong Liu;Jiewen Du;Jiansong Fang;Yulong Yin;Guohuan Xu;Liwei Xie - Database : the journal of biological databases and curation (2019)
- Artificial intelligence in drug development: clinical pharmacologist perspective. - In-Jin Jang - Translational and clinical pharmacology (2019)
- In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery. - Lauro Ribeiro de Souza Neto;José Teófilo Moreira-Filho;Bruno Junior Neves;Rocío Lucía Beatriz Riveros Maidana;Ana Carolina Ramos Guimarães;Nicholas Furnham;Carolina Horta Andrade;Floriano Paes Silva - Frontiers in chemistry (2020)
- Paper-Based Electrochemical Devices for the Pharmaceutical Field: State of the Art and Perspectives. - Amina Antonacci;Viviana Scognamiglio;Vincenzo Mazzaracchio;Veronica Caratelli;Luca Fiore;Danila Moscone;Fabiana Arduini - Frontiers in bioengineering and biotechnology (2020)
- Isolation and Characterization of a New Endophytic Actinobacterium Streptomyces californicus Strain ADR1 as a Promising Source of Anti-Bacterial, Anti-Biofilm and Antioxidant Metabolites. - Radha Singh;Ashok K Dubey - Microorganisms (2020)
- A Practical Perspective on the Roles of Solution NMR Spectroscopy in Drug Discovery. - Qingxin Li;CongBao Kang - Molecules (Basel, Switzerland) (2020)
- Application of Artificial Intelligence in COVID-19 drug repurposing. - Sweta Mohanty;Md Harun Ai Rashid;Mayank Mridul;Chandana Mohanty;Swati Swayamsiddha - Diabetes & metabolic syndrome (2020)
... (178 more literatures)
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