{"id":44611,"date":"2022-06-02T15:01:14","date_gmt":"2022-06-02T13:01:14","guid":{"rendered":"https:\/\/pharma-trend.com\/en\/artificial-intelligence-enabled-drug-discovery-competitive-analysis-report-2022-a-benchmarking-system-to-spark-companies-to-action-innovation-that-fuels-new-deal-flow-and-growth-pipelines-researc\/"},"modified":"2022-06-02T15:01:14","modified_gmt":"2022-06-02T13:01:14","slug":"artificial-intelligence-enabled-drug-discovery-competitive-analysis-report-2022-a-benchmarking-system-to-spark-companies-to-action-innovation-that-fuels-new-deal-flow-and-growth-pipelines-researc","status":"publish","type":"post","link":"https:\/\/pharma-trend.com\/en\/artificial-intelligence-enabled-drug-discovery-competitive-analysis-report-2022-a-benchmarking-system-to-spark-companies-to-action-innovation-that-fuels-new-deal-flow-and-growth-pipelines-researc\/","title":{"rendered":"Artificial Intelligence-enabled Drug Discovery Competitive Analysis Report 2022: A Benchmarking System to Spark Companies to Action – Innovation that Fuels New Deal Flow and Growth Pipelines – ResearchAndMarkets.com"},"content":{"rendered":"
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DUBLIN–(BUSINESS WIRE)–The “Artificial Intelligence-enabled Drug Discovery, 2022: Frost Radar Report”<\/a> report has been added to ResearchAndMarkets.com’s<\/strong> offering.\n<\/p>\n

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\nThis report presents competitive profiles on each of the companies based on their strengths, opportunities, and a small discussion on their positioning.\n<\/p>\n

\nThe report 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.\n<\/p>\n

\nPharmaceutical 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.\n<\/p>\n

\nDrug 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.\n<\/p>\n

\nArtificial 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.\n<\/p>\n

\nMost 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.\n<\/p>\n

\nTo 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.\n<\/p>\n

\nKey Topics Covered:<\/strong>\n<\/p>\n

\n1. Strategic Imperative and Growth Environment<\/strong>\n<\/p>\n