DKA logo transparent.png

DeepTech & GeoEconomic Big Data Analytics Dashboards

Deep Pharma Intelligence
Big Data

Analytics Dashboard

General Information

Cooperation Between Different AI-Related Pharma Fields

Till the second half of 2020, number of international AI companies has increased to 240 worldwide. The same rapid growth trend can be observed among influencers, investors, R&D centers, outsourcing groups and private researchers who work on AI application in pharmaceutics. The most popular directions investigated by AI drug discovery startups are first screening, leading molecule identification, preclinical testing, drug candidate selection, target identification, target validation. Biopharma-related companies constantly try to apply developed AI algorithms to multiple drug research and discovery areas at once. Main focus of AI research for today is concentrated on small molecules libraries.

Application of AI for Advanced R&D

Generate Novel Drug Candidates

 

  • Analyze data sets, form hypotheses and generate novel insights

  • Identify novel drug candidates

  • Analyze data from patient samples in both healthy and diseased states to generate novel biomarkers and therapeutic targets

  • Predict binding affinity and other pharmacological properties of molecules

  • Allow filtering for drug-like properties of molecules

  • Reduce complexity in protein design

Design and Processing of Preclinical Experiments

 

  • Reduce time, money, and uncertainty in planning experiments

  • Decode open- and closed-access data on reagents and get actionable insights

  • Automate selection, manipulation, and analysis of cells

  • Expedite development of cell lines and automate manufacturing of cellular therapeutics

  • Automate sample analysis with a robotic cloud laboratory

Clinical Trials

 

  • Optimize clinical trial study design

  • Transform diverse streams of biomedical and healthcare data into computer models representative of individual patients

  • Deliver personalized medicine at scale by revealing optimal health interventions for individual patients

  • Analyze medical records to find patients for clinical trials

  • Automate matching cancer patients to clinical trials through personal medical history and genetic analysis

  • Improve pathology analysis

  • Identify patients that would benefit from novel therapies

Repurposing of Existing Drugs

 

  • Rapidly identify new indications for many known drugs

  • Match existing drugs with rare diseases

  • Conduct experimental biology at scale by testing 1000+ of compounds on 100+ of cellular disease models in parallel

  • Generate novel biomarkers and therapeutic targets

Aggregation and Synthesis of Information

 

  • Extract knowledge from literature

  • Generate insights from thousands  of unrelated data sources

  • Improve decision-making 

  • Eliminate blind spots in research

  • Identify competitive whitespace

Key Trends

Expanding upon the key observations in our previous reports with new knowledge and analytics of Q2 2019, we can now better distinguish main trends within the industry in Q3 that will be shaping the market of AI in Drug Discovery in 2020 and beyond. 

 

Following a long lasting period of skepsis in 2018 and previous years the industry continues the “heating up” trend observed in Q2 also in Q3. This trend mainly consists of substantial increase in the volume of investments (notably, venture rounds become larger), financial support and the number of collaborations in Q3 of 2020. The industry’s growth dynamics is mainly influenced by the more active participation of largest pharmaceutical corporations in the AI-related investment and research collaborations. The number of research collaborations between pharma companies and AI-expertise vendors continues to increase. 

 

Despite the fact that IT and Tech corporations are more advanced in the AI tech in comparison to Pharma Corporations, they are increasingly open to co-operations and other forms of partnership with conventional drug makers -- in order to leverage their computational infrastructures and high-tech opportunities with enormous experience of pharma/biotech companies, their gigantic collections of datasets and novel approaches. AI here helps to discover new drugs, molecules, repurpose already existing drugs, find new targets that influence the illness progression etc. In this case, AI-Companies entering healthcare space are seeking strategic benefits, such as the ability to co-own important scientific discoveries and intellectual property obtained from the partnership and ability to continue developing new algorithms that will help these companies to leave their mark on medical history.

 

As 2020 marks a challenge in the ability to innovate, transform and adopt AI at scale faster, the so-called “Big Gap” continues narrowing due to rapidly increasing attention and activity of pharmaceutical companies with regards to AI prospects. Even tech giants like Google and Tencent are willing to expand their super-platforms to the area of pharmaceutical research. Having much bigger expertise of building and integrating super-platforms, currently they are conducting significant M&As and gaining some expertise in the area of the drug discovery, which would enable in the nearest future their expansion in this area. At the same time, the number of the deals between BioPharma corporations and AI companies aiming at the application of AI in drug discovery increased comparing with the same period in 2019. 

Global shortage of AI talent continues to be a serious  challenge for Biopharma industry, repeating the trend from our previous reports. However it should be noted that there is an increase in number of training courses and overall representing of AI-related directions in education programmes worldwide. Big pharmaceutical companies invest huge amounts of money in preparing of such specialists. But still the majority of talented AI professionals have been acquired by traditional IT-corporations and have been applied for purposes other than AI in healthcare. Therefore a lack of experienced specialists to support the activities of AI for Drug Discovery companies in particular is still a matter of today’s reality. Consequently large pharmaceutical companies continuously increase competing for the talented AI specialists as a valuable resource. Even specialized AI-driven drug discovery companies cannot fulfill gaps of AI talents as only 15.6% of their stuff being AI-experts.

 

Technologies, based on Deep learning (DL) algorithms will hold their leading position in the pharmaceutical AI race. Generative Adversarial Networks (GANs) and their variants are being increasingly regarded as a “golden standard” of innovation in the pharmaceutical AI space. 

 

Lack of available quality data is still a challenge for Investigations in AI and cooperating between AI and non-AI companies. The significant bottleneck in the AI applications for drug discovery purposes is the need to have correctly prepared, systematized and properly linked data that is ready to processing or is at least easy to manipulate with. Such types of data are quite scarce for the life sciences industry. A lot of research data in drug discovery is poorly validated and provided under a strict code of secrecy due to the high level of competition between drug makers. This is an issue unless there exists a well trained AI that is able to operate with unsorted collected information. THis means that as AI technologies evolve the weight of problems with unsorted information will decrease. 

 

There is a growing “AI democratization” trend, making machine learning and other advanced data analytics and modelling technologies increasingly commoditized, and available for use by non-AI experts. Examples include cloud-based “drag-and-drop” model builders, AI-focused frameworks, specialized out-of-the box software packages, and pretrained ML models. 

 

COVID19 accelerated progress in the pharmaceutical AI space, mainly due to the urgent need of running drug repurposing programs to come up with quick solutions to the pressing need, and the need to analyze large amounts of dynamically changing healthcare information (e.g. In the area of epidemiology, diagnostics and so on). The urgency of the situation stimulated accelerated research in the AI space and increased investments into such programs and projects.

 

Valuation of the industry is thought to show substantial growth, that, however, can be delayed in time. This appears to be a result of the general growth in the number of active business players, rather than an increase in the new products’ value.. No AI-derived drug has been approved by the FDA or validated in clinical trials so far. Despite this fact first milestones are expected to be reached by the end of 2020. On the other hand, the anticipated global financial crisis may hinder the industry’s growth dynamics, delay the AI adoption at scale, as well as the emergence of the first AI-derived blockbuster drugs.  

 

AI-friendly decision makers in pharmaceutical companies will become a great advantage in the competition for faster and cheaper development of new drugs. According to a recently conducted research, US, Japan and Germany are the countries with the biggest concentration of AI-friendly decision makers. About 46% of them work in pharmaceutical industry, while  only 3% are dealing with both AI and Pharma. In such more efficient application of AI technologies are expected to be observed and it will bring faster and more noticeable results.

Geography

US is a main player in AI industry. In the beginning of AI implementation, US was a pioneer and then the main player with the greatest number of companies using AI to force R&D, research centres and institutes, and investments. However, we observe the increased level of the UK and EU activity through big corporations that use AI to reorganise drug discovery and in launching government initiatives. It is also important to note a great increase in activity from the Asia-Pacific region generally, and particular from China — AI superpower. 

 

China engages in extensive investment activity. In particular, it has promised to invest $5 billion in AI. Tianjin, one of the biggest municipalities, is going to invest $16 billion in its local AI industry, and the Beijing authorities will build $2.12 billion AI development project. China also has at least ten privately owned AI startups valued at more than US$1 billion. Moreover, China has been heavily investing in biotech R&D, although lately a serious decrease in Chinese investment in US biotech startups has been observed which can be explained by the trade conflicts between the US and China.

 

China plans to become the world AI leader by 2030, according to the AI Strategic Plan released in July 2017. The analysis of the the Asia-Pacific region has shown that the main forcers of AI implementation include Saama Technologies, Inc., a leading clinical data analytics company. It has announced a collaboration with researchers at the Tufts Center for the Study of Drug Development to ascertain how biopharmaceutical companies optimize automation and information technologies, including machine learning and neural networks, to support the research and development of new therapeutics. Moreover, XtalPi provides a huge number of talent to work with machine learning, create drug discovery and development applications that predict the properties of small molecules. Another innovators of Asian AI industry are Cytlimic and Fujitsu that offer software for predicting how well compounds will bind with each other and proteins. 


Europe has traditionally been a strong breeding ground for biopharma activity, with some recent large valuations and mega deals. The UK and EU activity in the pharmaceutical AI race is mainly boosted by Novartis that announced an important step in reimagining medicine by founding the Novartis AI innovation lab and by selecting Microsoft Corp. as its strategic AI and data-science partner for this effort. Furthermore, GlaxoSmithKline has announced a few deals with companies such as Exscientia, Insilico Medicine, Insilico Biotechnology to use new computer modelling systems. BenevolentAI, a global leader in the application of AI for scientific innovation, also has several high-profile research collaborations, including AstraZeneca, and licensed in a group of drugs to develop from Janssen in 2016. This all demonstrate that Pharma is increasingly turning to AI to transform the drug discovery process.

Business Activity

The business activity has been increasing in the pharmaceutical AI space over Q3 2020, judging by an increased number of transactions and partnership announcements in this period. 

 

The most significant deals and collaborations in Q3 2020 include: 

 

  • XtalPi — SoftBank Vision Fund 2, PICC Capital, and MorningSide Venture Capital: XtalPi has raised $319M in funding round C. The funding will be used for the further development of XtalPi’s Intelligent Digital Drug Discovery and Development (ID4) platform.

 

  • AstraZeneca intends to open AI and drug research centers in China and make a $1 billion capital infusion into Chinese biotech innovations.

 

  • Exscientia — Bayer: Exscientia has entered a 3-year $266 million agreement with Bayer. The partnership will leverage AI to accelerate the discovery of small molecules candidates programs for oncology and cardiovascular diseases.

 

  • Schrödinger — Bayer: Schrödinger has entered a 5-year agreement with Bayer to work on a new virtual platform for small molecules design, which will be able to design and screen synthetically feasible compounds.

 

  • Schrödinger — Google Cloud: Schrödinger enters a 3-year collaboration with Google Cloud to leverage the supercomputer power for speeding up Schrödinger’s molecular modeling platform.

 

  • Boehringer Ingelheim — BERG: Boehringer Ingelheim partners with BERG to investigate inflammatory diseases, particularly inflammatory bowel disease and Crohn’s disease, find the causes, develop new biomarkers, targets, and new drugs.

 

  • Global Open Science project COVID Moonshot was launched by the international consortium of industrial and academic partners, including AI-driven startup PostEra.

 

  • Insitro — Andreessen Horowitz, Canada Pension Plan Investment Board: insitro, a machine learning-driven drug discovery company, has raised $143 million in Series B financing. The investment will be used to further develop the company's technology and automation.

 

Partnerships like these provide a huge effect on Pharma industry and are needed in case if a company intends to become a leader in the ongoing competition.

Major Conclusions for Q3 2020

  • The rapid development of the industry continues, following the trends mentioned in our previous reports. Now not only private investors are getting increasingly interested in AI for Drug Discovery companies, but the Biopharmaceutical companies are looking for long-term cooperation with AI startups on their own and are ready to invest in them. In Q3 2020, 400 active investors were identified. Companies, Corporations and R&D Centers demonstrated growing interest in the industry as well. 30 Pharma corporations applying AI for drug discovery and 40 Tech corporations applying advanced AI applications in healthcare are highlighted in this report which speaks to further growth of the market. Additionally, this report introduces 20 Contract Research Organizations demonstrating interest in AI for Drug Discovery worldwide.

 

  • Declining R&D efficiency of Biopharma Companies remains a major concern among all parties in the industry with a continuous decline recorded during the last 9 years. Costs of R&D per drug are growing exponentially, yet sales per asset are not showing the trend to increasing. Pharma companies need to consider new approaches in their R&D field, such as the development of AI algorithms for better data processing in order to discover new drugs, their components and improve already existing ones or implementation of biotech startups’ experience. This brings us to an issue which is of profound concern to everyone — a growing race for AI talent. 

 

  • The challenge of developing treatments to fight COVID-19 pandemic has prompted the research groups to use AI as a main tool for identifying new drugs of interest. Multiple drug candidate pipelines were developed using machine learning models. As AI demonstrates its ability to accelerate drug repurposing for COVID-19, the interest of investors in AI companies markedly increased.

 

  • Pharmaceutical and healthcare companies have developed a strong interest in applying AI in many different areas over the last several years. The demand for the ML/AI technologies, as well as for ML/AI talent, is growing in pharmaceutical and healthcare industries and driving the formation of a new interdisciplinary field — data-driven drug discovery/healthcare. The overall success of all the companies in the industry depends strongly on the presence of highly skilled interdisciplinary decision makers, able to streamline, organize and guide in this direction. For Big Pharma companies that are fighting to survive it will be extremely important to hire top AI specialists as well as prepare and support special training courses within academia field to meet this need in the nearby future. The USA and Great Britain remain home for the largest number of top experts. However, it should be noted that China has the potential to substantially alter these statistics in the coming years due to reverse migration of top AI experts from the USA. It will also be a challenge to poach AI experts from academia, where most of them work and where they are clearly comfortable.