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Could AI create a brave new world of pharma R&D?

Barely a week goes by without one big pharma or biotech announcing that it is using AI to develop new medicines. Richard Staines looks at the progress already made thanks to this potentially revolutionary technology, and how it could change the fortunes of a beleaguered pharma industry.

Artificial Intelligence (AI) has become the latest buzz word in drug R&D, and many of the

world’s biggest pharma companies claim to be using it to drive forward the process of scientific discovery.

The concept is that using AI’s capabilities, the previously unpredictable process of drug development can become much more predictable, and as a result less expensive.

While it is still early days, AI in its many guises can be used at many points during drug research process.

While it is still early days, AI in its many guises can be used at many points during the drug research process.

It’s far too costly to develop drugs, which are becoming more and more difficult to find as scientists attempt to tackle diseases that are poorly understood.

It is also tough to find drugs that are significant improvements over existing medicines in more common diseases, and those which already have well-established treatments.

Companies are turning to AI from the very beginning of the R&D process, when they are looking for that needle in a haystack that is the ideal drug candidate for a particular disease.

Already this year there have been two major developments in early drug R&D. In September, Hong Kong’s Insilico Medicine found a way to use AI and deep learning techniques to design, synthesise and validate a novel drug in 46 days – 15 times faster than the best pharma companies.

And later that month, AI therapeutics firm Deep Genomics claimed a world first after using artificial intelligence to identify a therapeutic drug candidate.

These developments were hailed as pivotal moments for AI in drug R&D and suggest that there is much more to come as computers become more powerful, and the way that AI is used becomes more nuanced.

Fooling the computer

Insilico pioneered the use of cutting edge techniques such as Generative Adversarial Networks (GANs) and Reinforcement Learning for drug discovery and biomarker development.

GANs are based around the concept of two neural networks arguing with each other to create an ever-more accurate depiction of reality.

A GAN based system has already created artwork that is eerily similar to that drawn by humans. Last year Christie’s sold a fictional portrait generated by a GAN-based AI algorithm “artist” called min/G max/D E_x [log⁡(Dx) ) E_z [log⁡(1-D(G(z)))] for $432,500.

The portrait, called Edmond de Belamy, from La Famille de Belamy, is incredibly realistic, with only the bizarre artist’s signature and the slightly off-centre position of the “painting” providing the clue about its real author.

A GAN-based development model with this degree of realism could lead to new medicines that the pharma industry needs to create the next generation of drugs.

GANs work using two networks – Generator and a Discriminator – where the former generates data and the latter evaluates the data for authenticity.

The Discriminator is first fed real-world and fake data, then the Generator attempts to fool the Discriminator network into thinking that its images are real.

After many thousands of attempts, and the Discriminator is fooled, the image is created – and it’s hoped that GAN technology such as that created by Insilico could create an ideal drug instead of a fake painting.

And while the sum raised by the Sotheby’s sale was impressive, and nearly 45 times more than expected, a drug to treat an incurable condition such as Alzheimer’s is worth billions to the pharmaceutical industry.

Written by: Richard Staines (Senior Reporter at pharmaphorum)

Published on: November 29, 2019

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