London Futurists
Anticipating and managing exponential impact - hosts David Wood and Calum Chace
Calum Chace is a sought-after keynote speaker and best-selling writer on artificial intelligence. He focuses on the medium- and long-term impact of AI on all of us, our societies and our economies. He advises companies and governments on AI policy.
His non-fiction books on AI are Surviving AI, about superintelligence, and The Economic Singularity, about the future of jobs. Both are now in their third editions.
He also wrote Pandora's Brain and Pandora’s Oracle, a pair of techno-thrillers about the first superintelligence. He is a regular contributor to magazines, newspapers, and radio.
In the last decade, Calum has given over 150 talks in 20 countries on six continents. Videos of his talks, and lots of other materials are available at https://calumchace.com/.
He is co-founder of a think tank focused on the future of jobs, called the Economic Singularity Foundation. The Foundation has published Stories from 2045, a collection of short stories written by its members.
Before becoming a full-time writer and speaker, Calum had a 30-year career in journalism and in business, as a marketer, a strategy consultant and a CEO. He studied philosophy, politics, and economics at Oxford University, which confirmed his suspicion that science fiction is actually philosophy in fancy dress.
David Wood is Chair of London Futurists, and is the author or lead editor of twelve books about the future, including The Singularity Principles, Vital Foresight, The Abolition of Aging, Smartphones and Beyond, and Sustainable Superabundance.
He is also principal of the independent futurist consultancy and publisher Delta Wisdom, executive director of the Longevity Escape Velocity (LEV) Foundation, Foresight Advisor at SingularityNET, and a board director at the IEET (Institute for Ethics and Emerging Technologies). He regularly gives keynote talks around the world on how to prepare for radical disruption. See https://deltawisdom.com/.
As a pioneer of the mobile computing and smartphone industry, he co-founded Symbian in 1998. By 2012, software written by his teams had been included as the operating system on 500 million smartphones.
From 2010 to 2013, he was Technology Planning Lead (CTO) of Accenture Mobility, where he also co-led Accenture’s Mobility Health business initiative.
Has an MA in Mathematics from Cambridge, where he also undertook doctoral research in the Philosophy of Science, and a DSc from the University of Westminster.
London Futurists
Pioneering AI drug development, with Alex Zhavoronkov
This episode discusses progress at Insilico Medicine, the AI drug development company founded by our guest, longevity pioneer Alex Zhavoronkov.
1.20 In Feb 2022, Insilico got an IPF drug into phase 1 clinical trials: a first for a wholly AI-developed drug
1.50 Insilico is now well-funded; its software is widely used in the pharma industry
2.30 How drug development works. First you create a hypothesis about what causes a disease
4.00 Pandaomics is Insilico’s software to generate hypotheses. It combines 20+ AI models, and huge public data repositories
6.00 This first phase is usually done in academia. It usually costs $ billions to develop a hypothesis. 95% of them fail
6.50 The second phase is developing a molecule which might treat the disease
7.15 This is the job of Insilico’s Chemistry 42 platform
7.30 The classical approach is to test thousands of molecules to see if they bind to the target protein
7.50 AI, by contrast, is able to "imagine" a novel molecule which might bind to it
8.00 You then test 10-15 molecules which have the desired characteristics
8.20 This is done with a variety of genetic algorithms, Generative Adversarial Networks (GANs), and some Transformer networks
8.35 Insilico has a “zoo” of 40 validated models
10.40 Given the ten-fold improvement, why hasn’t the whole drug industry adopted this process?
10.50 They do all have AI groups and they are trying to change, but they are huge companies, and it takes time
11.50 Is it better to invent new molecules, or re-purpose old drugs, which are already known to be safe in humans?
13.00 You can’t gain IP with re-purposed drugs: either somebody else “owns” them, or they are already generic
15.00 The IPF drug was identified during aging research, using aging clocks, and a deep neural net trained on longitudinal data
17.10 The third phase is where Insilico’s other platform, InClinico, comes into play
17.35 InClinico predicts the results of phase 2 (clinical efficacy) trials
18.15 InClinico is trained on massive data sets about previous trials
19.40 InClinico is actually Insilico’s oldest system. Its value has only been ascertained now that some drugs have made it all the way through the pipeline
22.05 A major pharma company asked Insilico to predict the outcome of ten of its trials
22.30 Nine of these ten trials were predicted correctly
23.00 But the company decided that adopting this methodology would be too much of an upheaval; it was unwilling to rely on outsiders so heavily
24.15 Hedge funds and banks have no such qualms
24.25 Insilico is doing pilots for their investments in biotech startups
26.30 Alex is from Latvia originally, studied in Canada, started his career in the US, but Insilico was established in Hong Kong. Why?
27.00 Chinese CROs, Contract Research Organisations, enable you to do research without having your own wetlab
28.00 Like Apple, Insilico designs in the US and does operations in China. You can also do clinical studies there
28.45 They needed their own people inside those CROs, so had to be co-located
29.10 Hong Kong still has great IP protection, financial expertise, scientific resources, and is a beautiful place to live
29.40 Post-Covid, Insilico also had to set up a site in Shanghai
30.35 It is very frustrating how much opposition has built up against international co-operation
32.00 Anti-globalisation ideas and attitudes are bad for longevity research, and all of biotech
33.20 Insilico has all the data it needs. Its bottleneck is talent
35.00 Another requirement is co-operation from governments and regulators, who often struggle to sort the chaff from the wheat in self-proclaimed AI companies
37.00 Longevity research is the most philanthropic activity in the world
37.30 Longevity Medicine Course is available to get clinical practitioners up to speed with the sector
