{"id":5743,"date":"2021-04-01T12:13:04","date_gmt":"2021-04-01T12:13:04","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2021\/04\/01\/valence-discovery-transforming-ai-enabled-drug-design-with-few-shot-learning\/"},"modified":"2021-04-01T12:13:04","modified_gmt":"2021-04-01T12:13:04","slug":"valence-discovery-transforming-ai-enabled-drug-design-with-few-shot-learning","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2021\/04\/01\/valence-discovery-transforming-ai-enabled-drug-design-with-few-shot-learning\/","title":{"rendered":"Valence Discovery: transforming AI-enabled drug design with ‘few-shot learning’"},"content":{"rendered":"

Valence Discovery: transforming AI-enabled drug design with ‘few-shot learning’<\/a><\/p>\n

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

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.\n<\/p>\n

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.\n\t\t\t\t\t<\/p>\n

\u201cThe overarching mission of Valence is really to empower drug discovery scientists with the latest advances in AI-enabled design,\u201d says CEO Daniel Cohen. \u201cAnd that\u2019s not just faster, cheaper drug discovery, but it\u2019s also about unlocking a novel therapeutics base so that we can now address what were previously intractable problems using these AI methods.\u201d<\/p>\n

Valence\u2019s academic origins<\/h2>\n

Valence has its origins at Canadian AI research institute Mila, where the company\u2019s founding team focused on developing deep learning tools for drug discovery and design during their PhD studies.<\/p>\n

\u201cWhat we\u2019re 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\u2019re working on,\u201d Cohen explains.\n<\/p>\n

Pharma and biotech companies have been using AI tools to make sense of big data for some time, but what makes Valence\u2019s technology unique is that it\u2019s centred around \u2018few-shot learning\u2019 \u2013 that is, developing a learning model using very little training data \u2013 and finding value in small, noisy datasets.\n<\/p>\n

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 \u2013 and this is precisely what Valence\u2019s technology aims to achieve.\n<\/p>\n

\u201cIf 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,\u201d Cohen says. \u201cAnd that\u2019s what few-shot learning allows us to do for our partners.\u201d\t\t\t\t\t<\/p>\n

\n