活用了人工智慧的藥物研發(2022年):Frost Radar

Frost Radar: Artificial Intelligence-enabled Drug Discovery, 2022

出版日期: 2022年04月25日 | 出版商:

Frost & Sullivan | 英文 48 Pages | 商品交期: 最快1-2個工作天內

價格

簡介目錄

人工智慧為基礎的產品及解決方案，由於縮短製藥企業藥物研發的時間軸，提高流程的聰敏性，提高有效性和安全性相關預測準確度，使用成本效益高的模式改善醫藥品開發平台多樣化的機會，變革藥物研發及開發動態。

本報告提供活用人工智慧的藥物研發市場相關調查分析，策略性必要條件及成長環境，Frost Radar(TM)，活躍的企業，策略性洞察能力等相關資訊。

Pharmaceutical drug discovery and development has been suffering from declining success rates with new molecules primarily because of poor external validity of preclinical models and lack of efficacy of the molecule in terms of the intended disease indication. Drug success rates continue to be in the range of only 1 in 10 that enters clinical phases pushing through to FDA approval. Frost & Sullivan finds that traditional solutions focused primarily on data from limited sources and rule-based computational techniques used to address the understanding of targets and leads are inefficient.

Artificial intelligence (AI) is set to transform the drug discovery landscape. AI-based products and solutions are transforming drug discovery and development dynamics by enabling pharmaceutical players to shorten discovery timelines, enhance process agility, increase prediction accuracy on efficacy and safety, and improve the opportunity to diversify drug pipelines using a cost-effective model.

Most pharmaceutical vendors are focused on collecting, creating, and augmenting data from across laboratories, clinical trials, real-world evidence, biobanks, and repositories. The increasing volume and veracity of clinical and research data is compelling traditional providers to leverage enabling tools and technologies such as cloud computing, AI and machine learning, natural language processing, and advanced analytics to make a shift to a relatively fast, rational data-driven drug discovery and development approach.

To remain competitive, companies must strike the right balance of data, AI, and computational capability and match it with the wet lab capability. There remains inadequate understanding of the biological networks and drug-target interactions. Enter AI, which has been able to support the identification and prioritization of disease-specific therapeutic targets based on gene-disease associations. Such results must be replicated and validated through in vitro experiments and in vivo models.

Frost & Sullivan finds that the impact of AI on the entire pharma value chain can more than double what is achievable using traditional analytics and capture between 2% and 3% of industry revenue, amounting to more than $50 billion in potential annual impact.

The Frost Radar™ reveals the market positioning of companies in an industry using their Growth and Innovation scores as highlighted in the Frost Radar™ methodology. The document presents competitive profiles on each of the companies in the Frost Radar™ based on their strengths, opportunities, and a small discussion on their positioning. Frost & Sullivan analyzes hundreds of companies in an industry and benchmarks them across 10 criteria on the Frost Radar™, where the leading companies in the industry are then positioned.