, it’s been hard to measure adoption of artificial intelligence in drug discovery. Pharma and biotech companies tend to not publicly disclose competitive technology use. best efforts
So we went to the source. In partnership with
—a global expert network to advance science and medical technology—we surveyed 330 scientists in December 2017 who work in drug discovery. The Science Advisory Board
The key takeaway: while organizations are adopting the tech, there’s
significant untapped potential for those willing to be more aggressive. But the industry—and society—will only realize the potential with education and relevant success stories.
Read on to learn why.
Many Scientists Are Still Unfamiliar With AI in Drug Discovery
Given breakthroughs and extensive media coverage for AI in 2017—for example,
AlphaGo’s victories—it’s perhaps unsurprising that 59% of scientists are familiar with it. But about 4 in 10 scientists working in drug discovery are not.
Most Use of AI in Drug Discovery Is Low Hanging Fruit
There are many
AI drug discovery startups offering solutions for every phase of the process, from research to publishing. Yet among scientists whose organizations use AI, the focus is heavily on target identification and validation, safety tests, compound discovery, and lead optimization. There is untapped potential to explore other uses, from reagent selection to clinical trial optimization.
Speed Is the Biggest Perceived Benefit, but Gaps Remain Between Perception and Reality
What drives interest in AI for drug discovery? Far and away, it’s increased speed of discovery, which 61% of respondents perceive as a benefit. This is followed by increased comprehensiveness of research (46%), increased opportunities for existing compounds (45%), and increased novelty of targets and compounds (45%). But gaps remain between perception and reality. On average, realized benefits are 14% below perceived, with the biggest gaps between increased opportunities for existing compounds (-20%) and decreased cost of discovery (-19%).
Few Organizations Have a Sense of Urgency
AI is progressing rapidly. The
(PDF) notes that published papers increased 9x per year since 1996, course enrollment increased up to 11x since 1996, startups increased 14x since 2000, and performance has exceeded or will soon meet human performance for object detection, speech recognition, and question answering. Yet while 59% of surveyed scientists anticipate their organization will increase use of AI for drug discovery in 2018, just 15% say it will be substantial. And 39% of respondents say that their organization’s use of AI will remain about the same as in 2017. 2017 Artificial Intelligence Index
Lack of Knowledge Inhibits Use
Why might so few organizations be planning aggressive application? My first assumption, having long worked at the intersection of technology and healthcare, was a culture of conservatism, or concerns about data security and privacy. But relatively few respondents (18% and 16% respectively) reported these. The biggest barrier? Education. This includes lack of knowledge and expertise about the technology (62%) and lack of knowledge and expertise about available companies and tools (42%). Many respondents (42%) also report concerns about cost, but given that the cost can be low and usage-based, my hunch is that this reflects a lack of education about pricing.
Education and Relevant Case Studies Are Key to Adoption
Given that lack of knowledge hinders adoption, AI startups take note: you must invest in education. Fully 71% of respondents say that education about the technology would help overcome barriers to adoption. But pharma and biotech companies have a role, too. They must more openly disclose and discuss successful (and unsuccessful) use, as 50% of respondents are looking for relevant case studies.
What Do You Think?
If you’re a scientist, an executive at a pharma or biotech company, or a leader at a relevant startup, I hope these results provide insight into AI drug discovery attitudes and adoption. Do they align with your experience? Please let us know in the comments, or feel free to reach out directly to Simon Smith at BenchSci on
, LinkedIn , or by Twitter . email
Also, if you’re a scientist and want to participate in future surveys, please
. You can also follow us on join The Science Advisory board here Twitter.
This article was written by Simon Smith. As Chief Growth Officer at BenchSci, Simon works to ensure more scientists know about and benefit from our technology. He's passionate about (and a bit obsessed with) scaling innovative solutions to meaningful problems. Outside work, Simon devours books and articles on light topics such as business strategy, personal effectiveness, and technological innovation, and hacks content, code and his biology.
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Established in 1997, The Science Advisory Board is an international community of science and medical experts. It connects a global network of research, development, and manufacturing professionals to collaborate on shaping the future of scientific technology. Members share their knowledge and experience with the community, advise and consult leading life science companies, and earn rewards for their engagement. We welcome member-generated content on our blog. If you have an opinion article that you would like to see published, please contact us here.
BenchSci is a start up that has leveraged machine learning to develop a reagent intelligence platform that transforms published antibody usage data into experiment-specific recommendations to reduce time, money and uncertainty in planning materials and methods. Unlike PubMed, Google Scholar, reagent directories and vendors, BenchSci uses machine learning to decode open- and closed-access data and present published figures with actionable insights.
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