活用了人工智慧的药物研发(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个工作天内

价格

简介目录

人工智慧为基础的产品及解决方案，由于缩短製药企业药物研发的时间轴，提高流程的聪敏性，提高有效性和安全性相关预测准确度，使用成本效益高的模式改善医药品开发平台多样化的机会，变革药物研发及开发动态。

本报告提供活用人工智慧的药物研发市场相关调查分析，策略性必要条件及成长环境，Frost Radar(TM)，活跃的企业，策略性洞察能力等相关资讯。

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.