Informatics
ARTIFICIAL
INTELLIGENCE and biopharma R&D IT
As noted in the Ernst & Young report Beyond borders Biotechnology report 20171, “…R&D productivity remains an ongoing concern. Artificial Intelligence and the accompanying analytics are now so advanced that these tools promise to improve the traditional drug target selection and R&D process.”
By The PRISME Forum
T
he PRISME Forum is the biopharmaceuti- cal industry R&D IT leadership group that meets twice a year. It addresses common
industry challenges, shares use cases and catalyses more rapid creation, adoption and application of solutions to increase the efficiency and effective- ness of biopharmaceutical R&D. As such, there is a contribution that the PRISME
Forum should make to the development and imple- mentation of AI to reduce the time and cost of bringing new medicines to market to treat unmet patient needs. With that in mind, the PRISME Forum focused its Fall 2017 Technical Meeting on the potential of Artificial Intelligence (AI) to improve biopharma R&D and healthcare.
Definition What is AI? A google search reveals a practical def- inition, ie “the theory and development of comput- er systems able to perform tasks that normally require human intelligence, such as visual percep- tion, speech recognition, decision-making and translation between languages”2. However, IBM Research provides a more pragmatic definition of AI: “By AI we mean anything that makes machines act more intelligently”3 and this is the definition that will be adopted in this article. There are many technical definitions and tax-
8
onomies that can be used to stratify the various computer science tools that live under the umbrella term AI. Indeed, AI experts can get excited about the nuances between terms such as machine intelli- gence and human intelligence or deep learning methods and pattern matching. For further background, Stanford University’s
initiative on the One Hundred Year Study on Artificial Intelligence or AI1004 provides a per- spective on the history and future of AI. This arti- cle identifies the start of the ‘AI100 timeline’ with Alan Turing’s 1950 paper on Computing Machinery and Intelligence5. Technologists working in Life Science R&D
should be interested in the practical applications within Biopharma R&D. IBM’s pragmatic definition of AI is helpful and helps identify the opportunities in automation that are not necessarily in alignment with precise, computer-science definitions of AI.
AI at a tipping point AI and Machine Learning have reached a tipping point. While many of the AI-based, computer sci- ence techniques have been available for some time (eg neural networks) the increase in data availabil- ity, vastly more powerful and easily accessed com- puter power (eg GPUs in the cloud), increased interest in data science and analytics, and
Drug Discovery World Spring 2018
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72
