SDG 3. GOOD HEALTH & WELL-BEING DRUG DESIGN Valence Discovery

Valence Discovery: transforming AI-enabled drug design with ‘few-shot learning’

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Artificial intelligence (AI) has become an increasingly popular tool for drug companies discovering and designing new therapies. According to analysis by Deloitte, the AI market in drug discovery is expected to grow from $159.8m in 2018 to $2.9bn by 2025.

Of the almost 180 start-ups involved in AI-assisted drug discovery in 2019, 40% were working on repurposing existing drugs or generating novel drug candidates using AI, machine learning, and automation.

AI-enabled drug design company Valence Discovery, formerly InVivo AI, was founded in 2018. Since its rebrand last month, the company has announced a series of impressive drug discovery and design partnerships, with the aim of making advanced technology accessible to R&D organisations of all sizes.

“The overarching mission of Valence is really to empower drug discovery scientists with the latest advances in AI-enabled design,” says CEO Daniel Cohen. “And that’s not just faster, cheaper drug discovery, but it’s also about unlocking novel therapeutics space so that we can now address what were previously intractable problems using these AI methods.”

Valence’s academic origins

Valence has its origins at Canadian AI research institute Mila, where the company’s founding team focused on developing deep learning tools for drug discovery and design during their PhD studies.

“What we’re trying to accomplish is the very rapid and cost-effective design of high-quality drug candidates that are optimised for a broad range of potency, selectivity, safety, DMPK [drug metabolism and pharmacokinetics] parameters that are relevant to whatever particular drug discovery programme we’re working on,” Cohen explains.

Pharma and biotech companies have been using AI tools to make sense of big data for some time, but what makes Valence’s technology unique is that it’s centred around ‘few-shot learning’ – that is, developing a learning model using very little training data – and finding value in small, noisy datasets.

For targets and indications that have already been extensively researched, there will be large amounts of pre-existing data to use. Companies looking to develop and design novel therapeutic approaches, however, will be working with very limited information. While small data holds the potential for novel therapies in areas of high unmet need, drug discovery teams must be able to effectively work with sparse datasets – and this is precisely what Valence’s technology aims to achieve.

“If we want to move into novel target spaces, or novel chemical areas where we have inherently little pre-existing data, we need entirely new classes of deep learning methods built specifically for low-data environments,” Cohen says. “And that’s what few-shot learning allows us to do for our partners.”

Addressing the limitations of AI-driven drug design

As well as small data’s inherent trickiness, synthetic accessibility – how easily chemical compounds can be synthesised – is another challenge involved in incorporating AI into novel drug discovery and design. Many of today’s AI systems generally yield low-quality molecules; they are highly reactive, unstable, synthetically infeasible, and therefore difficult to translate into effective treatments.

What sets Valence’s platform apart, Cohen says, is that it addresses the core limitation of existing AI technologies around synthetic accessibility. “The limiting step in these AI-oriented design, make, test cycles is always the make and the test,” he adds. “If you can’t readily synthesise these AI-generated molecules, the value-added AI in a typical discovery programme is going to be limited.”

To circumvent this obstacle, Valence has developed new classes of design technologies that Cohen says enables teams to enforce a high degree of synthetic accessibility and medicinal chemistry quality into AI-generated molecules.

Despite most major biopharma companies now employing AI-driven solutions for drug discovery, effectively integrating this technology into the process remains another major challenge.

“When you look at biopharma today, only a tiny fraction of the space is AI-enabled,” Cohen says. “Building high-quality AI capabilities internally is just not a core competency for a lot of discovery-oriented organisations – the space is evolving really quickly; it’s very challenging to stay on top of the latest methods.

“The field really needs to move to a point where you have plug-and-play infrastructure that’s been built specifically for drug design, that makes these tools more accessible to drug discovery scientists and to R&D organisations of all sizes, not just the largest pharmas.

“Really what we’re trying to do at Valence is democratise access to deep learning and drug design.”

Valence launches with a raft of new partners

As Cohen highlights, one strategy for overcoming the biopharma-AI integration struggle is collaboration with AI tech start-ups. In the weeks immediately following Valence Discovery’s unveiling, the company announced several partnerships with major pharmaceutical companies and research institutes. Despite being made so early in Valence’s journey, the collaborations were exciting, rather than daunting, for the company.

“We had many years of peer-reviewed science demonstrating the value of these technologies, we’re headquartered at the largest deep learning research institute in the world, we have some of the world’s leading deep learning scientists, like Professor Yoshua Bengio as close scientific advisors,” Cohen explains. “And we built up this really interdisciplinary team that’s bilingual in computation and also in the life sciences.”

Cohen emphasises that all of Valence’s deals are structured around the needs of the partner, and that the company is an active collaborator that seeks to “share in the successes of any AI-derived molecules”.

The company’s first announced collaboration, with pan-Canadian drug discovery and research commercialisation centre IRICoR, Université de Montréal, and the Institute for Research in Immunology and Cancer of the Université de Montréal, seeks to discover novel drug candidates for the treatment of levodopa-induced dyskinesia in Parkinson’s disease.

The target in Valence’s collaboration with Repare Therapeutics is similarly specific: precision oncology medicines. Cohen says AI is a natural partner for companies looking to optimise personalised treatments of this kind, allowing them to move through the discovery process as quickly and cost-effectively as possible, and explore chemical spaces they ordinarily wouldn’t have access to.

“In Repare’s case, it’s a really, really nice collaboration because we’re combining the best of both worlds,” he says. “They have this really powerful platform on the biology side for target identification, and we’re combining that with our platform for generative chemistry, really allowing their team to focus on what they do best, which is innovating on the biology.”

Valence’s most recent partnership, a drug discovery deal with French pharma giant Servier, is far broader. Under the agreement, Servier will leverage Valence’s technological expertise to generate novel drug candidates for multiple targets, while Valence is set to receive an upfront payment and success-based milestones on any drugs derived from the partnership. While Cohen can’t go into the specifics of the Servier deal, he says the collaboration involves moving into new chemical spaces to unlock difficult-to-treat targets.

At this relatively early stage of AI’s development as a drug discovery and design tool, technologies like Valence’s, while immensely promising, are circling around the margins of mainstream drug development – as Cohen acknowledges, AI currently supports only a small proportion of the pharma sector’s clinical programmes. But the potential for machine learning to find clinically-relevant links that human minds have missed is clear, and Valence is betting that these technologies will drive a major sea-change in drug development over the next decade.

“We believe quite strongly that by 2030, the majority of drug candidates entering the clinic will have been designed with meaningful input from AI systems and advancements,” Cohen says. “We’re very excited to be playing a role in empowering the shift towards AI-enabled drug design across the entire industry.”

The post Valence Discovery: transforming AI-enabled drug design with ‘few-shot learning’ appeared first on Pharmaceutical Technology.






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