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Deep Knowledge Ventures is an investment fund focused on AI and DeepTech, which values knowledge above profit - a fact that is embedded in its very name and brand. Hence, the fund considers its analytical subsidiaries among its most precious assets, driving the fund toward its long-term strategic vision: Deep Knowledge Analytics, focusing on advanced DeepTech and on the application of AI to Healthcare and Drug Discovery in particular, and Aging Analytics Agency, focusing on the science, business and economics of Longevity.


These two subsidiaries are the analytical engines driving and structuring the investment target identification and due diligence activities of Deep Knowledge Ventures, using sophisticated multi-dimensional, data-driven analytical frameworks and algorithmic methods that combine hundreds of specially-designed and specifically-weighted metrics and parameters to deliver sophisticated industry analysis, pragmatic forecasting and tangible industry benchmarking, with a focus on analyzing frontier technologies and the convergence of deep science and technological megatrends.


The specific focus of these two agencies on the convergence of advanced science and technology-driven industries and domains was a deliberate choice. The complexity of such sectors as AI in Healthcare and Longevity, and their pace of change and innovation, are increasing specifically due to the intersection of multiple scientific and technological domains. Whereas five years ago it was possible for industry innovations to be driven by advancements in a single domain or sector, today it is simply impossible, with the vast majority of such changes occurring at the intersection of two, three or more scientific and technological domains. And this is just the beginning of a rapidly expanding trend, because the degree of complexity behind these fields, and the number of points at which they are converging and intersecting, are increasing at an exponential pace.


Currently, these convergent paradigm shifts are occurring in industries that are already dominated by deep science and emerging technologies. But within the next 3-5 years, due to the pressures imposed by ongoing innovations and the arrival of advanced technologies, especially in the fields of AI and Advanced Biomedicine, we will begin to see other, much more conservative and slow-moving industries be shocked as well and transformed by innovations at the intersection of advanced technologies, literally forcing them to evolve in the face of significant technological change, despite their complete natural conservatism. Consider, for example, the management of entire countries, or the governance of such entities as pension funds. These are domains that are not technocratically driven, or marked by a rapid pace of innovation, and which typically make their strategic decisions reactively rather than proactively, making short-term decisions too late under the front of long-term strategy. But, under the pressures of emerging technologies and AI in particular, it is inevitable that at least some entities in ultraconservative domains and sectors as these will also soon come to be driven by similar paces of disruption and innovation absorption, marshalling towards new frontiers of science and technology.


Deep Knowledge Analytics’ 3-D Analytical Framework for AI in  Drug Discovery sector, whose production was necessitated by the complexities of the sector, and required in order to obtain a tangible and pragmatic understanding of the industry in order to structure investment strategy in a relevant way.


Thus, the focus of Deep Knowledge Analytics and Aging Analytics Agency on the convergence of technological megatrends, deep science and advanced technologies, and the development of multidimensional analytical frameworks that possess a level of complexity proportional to the industries and domains they are analysing, was a necessary requirement in order to conduct effective industry analysis, forecasting and benchmarking of DeepTech, deep science and frontier-technology industries and sectors at the minimum level of relevance and pragmatism.


Deep Knowledge Ventures invested in a number of companies applying AI for Drug Discovery and Biomarker Development. In 2018 it aggregated its industry intelligence under umbrella of the Pharma Division of Deep Knowledge Analytics, which has become the dominating analytics entity in this field, publishing multiple open-source reports with the aim of accelerating the development of the sector and improve the implementation of AI in Pharma in particular, as well as a number of specialized proprietary analytical reports for its corporate clients and strategic partners.


The goal of applying AI for Drug Discovery is finding more efficient and targeted ways to discover new drugs. The AI for Drug Discovery sector is currently focused on designing new blockbuster drugs. Blockbuster drugs typically are considered as the drugs that can generate at least $1 billion annually for one given Pharma corporation. Companies in the AI for Drug Discovery sector are focused on the needs of pharma corporations, which are trying to develop the next blockbuster drug aimed at a single disease and optimized for effectiveness in all patients equally. The Pharma business model which was established over 30-40 years ago, has not evolved significantly since that time, and has some very obvious apparent limitations.

As AI for Drug Discovery becomes more sophisticated, it will focus on Personalized Precision Medicine. When we reach this milestone in the evolution of drug discovery, drug development companies will switch from “blockbuster drugs” towards Personalised Precision Medicine, where drugs will be designed and applied using precise, individually-tailored methods of dosing, drug cocktail composition, and accurate efficient delivery. Medicine will be optimized for each individual person and varied according to their genetics, current state of health, age, and many other parameters. The next major innovation in drug discovery will be moving away from single drugs optimized for entire patient populations, and towards increased personalization.


Deep Knowledge Analytics and Aging Analytics Agency have become recognized among industry executives and experts for the quality, rigour and periodicity of their industry benchmarking, employing quantitative analytical frameworks routinely in order to rank industry entities, influencers, experts, technologies and practical applications.

In the most advanced stage, AI for Drug Discovery will focus on Precision Health and Healthy Longevity rather than Precision Medicine. The Longevity sector is the most complex area of AI for Drug Discovery because Longevity focuses on prevention rather than treatment. Longevity requires complete optimization of health at the deepest level targeting biological systems that control disease. This stage will mark the shift from precision medicine towards precision health, developing drugs not for just single diseases, but entire states of patient health. The Precision Health stage is the most complex and will require the most advanced next-generation technologies.


Aging Analytics Agency’s Proprietary Longevity Industry Classification and Benchmarking Framework uses dozens of specially-designed and specifically weighted quantitative metrics and submetrics to identify the specific scientific, technological, executive management and business development strengths, weaknesses and prospects of Longevity Industry companies.

The most sophisticated tools will be required to assess companies working in the field of Precision Health and Longevity. Compared to the current business models and technical approaches to the discovery, design and delivery of drugs, the shift towards Personalized Precision Medicine will be 10x more complex and multidimensional, and correspondingly the further shift from precision medicine to precision health will be 100x more multidimensional and complex.


As a result, the methods and approaches for industry analysis, investment target identification, and due diligence applied to companies during the occurrence of these two oncoming paradigm shifts will be proportionately more advanced, multidimensional and sophisticated than they are today. However those methods in common use today are even below the bar of the current complexity of AI for Drug Discovery landscape, and therefore they will prove increasingly ineffective tools for strategic decision making as the level of complexity in the biopharma increases more and more during the shift from blockbuster drugs to Personalized Precision Medicine and finally towards Precision Health.


The assumption that these coming shifts will not occur until far future is incorrect, and our current methods of industry analysis and tangible, quantifiable forecasting have informed us that it is quite reasonable to expect the approaches and business models underlying Precision Health will be the norm in developed countries by the year 2027-2030, and that in some countries and regions, significant elements behind this approach may be in place as early as 2024-2025.


The proprietary analytical frameworks of Deep Knowledge Analytics and Aging Analytics Agency not only utilize dozens to hundreds of parameters to quantify, benchmark and forecast developments in frontier-technology driven industries, but also structure those metrics and their importance factors in highly organized and multi-dimensional ways in order to efficiently manage the volume of data involved in their comparative analyses.


Furthermore, we can expect to see intermediate progress towards the full realization of this shift, like the widespread use of AI for blockbuster drug discovery, and even the shift from single-drug-fits-all business model towards Personalised Precision Medicine as early as 2022-2023. During the time major shifts will occur in healthcare. Healthcare decisions will be transferred from doctors to IT-systems. Progressive healthcare clinics will be managed by technical engineers and IT-company executives. The doctor’s role will shift to being hands-on patient centric.


While many analysts consider these events to be decades away, our forecasting and industry analysis show that this will be standard by 2030. In many countries, significant elements will be achieved much sooner and become reality by 2027. Intermediate steps like personalised advanced biomedicine capable of delivering some elements of precision health will be achievable by 2024. The importance of AI and transformation of approach from treatment towards prevention and maintenance of patient health will be transferred from doctors to IT-systems, and clinics will be engineered and managed by people with methods of thinking as now have aerospace engineers and managers of IT-corporations, whereas doctors will be those people who are now like mechanical engineers in aerospace -- the practitioners, but not the designers and strategic decision makers. In many cases patients, being empowered by sophisticated Data and AI systems, will be capable to become CEO’s of their own health.


The healthcare industry is currently dominated by biopharma corporations. In 7-10 years it will be dominated by healthcare corporations, who will take the form of either IT-giants, or tech corporations who have merged with or acquired the most progressive pharma corporations, and will eventually sell to their clients subscriptions to Precision Health, in the same way that today they are selling subscriptions to Netflix and Amazon Prime.


Deep Knowledge Analytics and Aging Analytics Agency have been working over the course of the past five years on designing and validating increasingly sophisticated and multidimensional approaches to industry analytics, to serve as the leading tools and solutions for strategic decision making, with the aim of developing such frameworks to the levels necessitated by the rapidly complexifying nature of the global healthcare system.


The analytical methodologies of Deep Knowledge Analytics and Aging Analytics Agency have evolved substantially since they began to be developed in 2013, and now incorporate 3-D frameworks where metrics and submetrics can be visualized simultaneously, as well as the development of “timeline machines” that al