|
市場調査レポート
商品コード
1076708
人工知能を活用した創薬(2022年):Frost RadarFrost Radar: Artificial Intelligence-enabled Drug Discovery, 2022 |
||||||
人工知能を活用した創薬(2022年):Frost Radar |
出版日: 2022年04月25日
発行: Frost & Sullivan
ページ情報: 英文 48 Pages
納期: 即日から翌営業日
|
人工知能(AI)ベースの製品およびソリューションは、製薬企業が創薬のタイムラインを短縮し、プロセスの俊敏性を高め、有効性と安全性に関する予測精度を高め、費用対効果の高いモデルを使用して医薬品パイプラインを多様化する機会を改善することで、創薬および開発のダイナミクスを変革しています。
当レポートでは、人工知能を活用した創薬市場について調査分析し、戦略的必須要件および成長環境、Frost Radar(TM)、活躍する企業、戦略的洞察等に関する情報を提供しています。
A Benchmarking System to Spark Companies to Action - Innovation that Fuels New Deal Flow and Growth Pipelines
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.