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Deep Knowledge Analytics is pleased to announce the publication of its most advanced industry analytical report to date: AI for Drug Discovery, Biomarker Development and Advanced R&D 2018 Q2.

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The large majority of the report, including Section I: AI for Drug Discovery Landscape Overview (Industry Developments Q2 2018), Section II: Novel Technologies & Trends (Industry Forecast 2019-2020), and Appendices (which feature profiles of 100 Companies, 220 Investors, 15 Biopharma, 15 IT/Tech Corporations, and 20 leading R&D Centres active in the AI for Drug Discovery space) totalling 400 pages in length, has been made available free of charge. Sections I delivers a summary of the most important recent industry events, including coverage of the most important BioPharma and IT/Tech corporations active in the space, government initiatives aiming to accelerate the dynamic of industry progress in the USA, UK, EU and Asia-Pacific region, industry-specific conferences and journalists, and a regional comparison of industry activity that summarizes which countries are currently at the forefront of activity, which ones are lagging behind, and which ones are showing evidence of rapidly increasing activity in the years to come. Section II provides in-depth coverage of the science and technology behind the industry, analysis of emerging subsectors and new application within the industry (including the convergence of AI with blockchain, digital medicine and Longevity technologies), and finally, a near-future forecast of industry growth and diversification from 2019 to 2020.

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Meanwhile, Section III: Comparative Industry Analysis & Classification Framework (Investor and M&A Guide), 70 pages in length, a section specifically tuned to industry professionals such as investors, analysts, BioPharma/Tech executives and decision-makers, is available for purchase here. This section is devoted to an in-depth comparative and quantitative analysis of the entire AI for Drug Discovery landscape, utilizing advanced infographics and tangible parameters both for (1) ranking various AI for Drug Discovery companies according to their levels of scientific validation, clinical development, R&D and industry-application diversification, and overall prospects for future growth and for (2) classifying AI for Drug Discovery companies according to their type and number of distinct industry applications, proportion of AI specialists, number of patents and publications, use of next-generation AI technologies (e.g. GANs vs ML), and whether they utilize AI as a core component of their R&D or as a complementary element to enhance their primary, non-AI focus and business model.