estimated value (2021) of global AI healthcare market

projected value of global AI healthcare market by 2030² 

estimated # of companies working in AI-driven drug discovery worldwide³

We’ve heard plenty about the promise — but has the time come for algorithm-designed drugs to show some profit? Drugmakers could certainly use an accelerant: despite billions of pounds of investment, few new treatments for major diseases, from cancer to dementia, have made it to market in recent years. Big Pharma wants to shrink the costs of drug development, which currently takes 12 years on average — often far longer — and costs over $1bn. And then, nine out of ten drugs that actually are developed fail to win regulatory approval and many drug candidates never move from “bench to bedside”.

Two distinct business models involving modelling and simulation are emerging: the first category includes companies that provide AI to Big Pharma as a service, while in the second startups run their own AI-enabled drug development pipeline, making them both providers and users of AI tech. 

Since our [2021 briefing](https://sifted.eu/pro/briefings/ai-driven-drug-discovery) on the topic, investment has been given a shot in the arm. Medicines designed by AI for conditions including lymphoma, inflammatory diseases and motor neurone disease are finally reaching human trials. According to one analysis, the work to date could potentially lead to the discovery of 50 new therapies within the next decade, an opportunity value more than $50bn¹. Delivery has fallen short in the past — but those investments may be about to pay off.


AI-driven drug discovery 2.0

Built Atlas, an advanced drug design platform to discover drug candidates at scale with quantum accuracy. Its proprietary supercomputer was developed in collaboration with American chipmaker Nvidia

Qubit Pharmaceuticals

Backed by the likes of Hoxton Ventures and Ada Ventures, it’s focused on fighting metabolic syndrome – a cluster of closely related diseases including diabetes.

Multiomic Health

Backed by senior members of Tiger Global and several angels, it develops algorithms for drug discovery, design and molecular structure prediction. The leadership team includes CEO Sabrina Maniscalco, Dawn Capital’s Haakon Overli and Nokia’s former CEO Jussi Westergren. 

Algorithmiq 

An Entrepreneur First alum company that uses deep learning to discover alternative immunotherapies for cancer treatments. 

Exogene

Dealroom

Sifted 

Have AI drug discovery startups delivered on their promise 10 years on?

Why AI could speed drug discovery 

Why clinical AI is the future of healthcare

Unlocking the potential of data and AI-driven drug discovery & development 

Artificial intelligence in European medicines regulation 

3 AI trends in drug discovery that stood out in 2022

MEPs want more safeguards in the European health data space 

Eray Kumdereli 

Seize the digital momentum: Measuring the return from pharmaceutical innovation 2022

AI in biopharma research: A time to focus and scale 

AlphaFold reveals the structure of the protein universe

How AI insights could add value across the pharmaceutical value chain

Oxford-based Exscientia’s $510m IPO two years ago was one of the biggest ever in biotech.
Boasts the first drug discovered with AI to enter clinical trials through a collaboration with Japanese pharma partner Sumitomo Dainippon, reducing the development time to less than a year, compared to the traditional four-year timeline to treat OCD.

Exscientia 

German vaccine maker BioNTech acquired London-based InstaDeep for $682m in January 2023 — one of the first big acquisitions in the space.
The two companies have been working together for years and together developed an early warning system to predict future variants of the Sars CoV-2 virus.

InstaDeep

Went public via SPAC in November 2021 on the Dutch exchange Euronext, alongside €435m of investment from Temasek, AstraZeneca, Ally Bridge, Invus and several other institutional investors.
Despite a recent setback in a mid-stage clinical trial for an AI-enabled eczema drug, UK-based biotech is also involved in research related to personalised medicine, genomics and digital therapeutics.

BenevolentAI

Celeris Therapeutics

Cradle

BioCorteX

CHARM Therapeutics

PICTURA BIO LTD

Indentification and classification of target cells

CardiaTec Biosciences

Novai

Qubit Pharmaceuticals SAS

Algorithmiq

Ancora.ai AG

Deepflare

AQEMIA

Ochre Bio

ILoF - Intelligent Lab on Fiber

Biomatter 

PreComb

Peptone

MAbSilico

Antiverse

Scailyte

Arctoris

Kantify

Pharmacelera

Iris.ai

Turbine

LabGenius

The pharma industry has been slow to welcome digitalisation but is in command of giant cheque books, so more bankrolling of agile early-stage startups  may help turbo-charge more efficient drug development.
Despite the long approval cycles and significant regulatory hurdles, many of the early-stage companies highlighted in our previous briefing are already showing traction. Parisian rising star Aqemia raised a €30m Series A in October 2022 to develop new AI that can massively increase the speed at which promising drug candidates are found using quantum physics, and has already partnered with incumbents Sanofi, Servier and Janssen.

While its success at discovering new drugs has been limited, AI has proven highly effective in analysing the secondary use of approved drugs to repurpose them for conditions they weren’t originally designed for. For example, Clemastine, an antihistamine, has been shown to reduce repetitive behaviours, common in those suffering from autism.Without the need to start from scratch, the drug development process can move much faster, leading to more effective treatments for a wider range of illnesses while saving on R&D costs.

Machine and deep learning are increasingly being deployed to predict the structure of unknown proteins, determined by the sequence of 20 amino acids assembled by DNA.
DeepMind (acquired by Google in 2014 for £400m) expanded the number of protein structures in its open-source database from 350k to 200m+ in July 2022. Widening access to a growing number of proteins will help accelerate others’ progress in accurately determining protein structures from just their amino acid sequencing — without the need for tedious and costly lab analysis.

The more data AI has to work with, the better the insights. That means massive amounts are needed  to train AI models in protein design, with precision medicine being the ultimate goal.
But  Europe is suffering compared to the US: it’s just not easy for scientists on the continent to access that scale of data. At present it necessitates extensive collaborations between labs to process massive datasets, which includes health data from diverse sources such as clinical research, electronic medical records and smart wearable devices. Companies that possess advanced data generation capabilities will have the upper hand.