AI-developed Drug Candidates in Clinical Trials

While it is probably early to say that AI adoption in the pharmaceutical industry revolutionized drug discovery altogether, several “AI-native” companies did manage to gain notable efficiency in building their therapeutic pipelines quickly. What is one common feature of such companies? Each built a specialized, highly integrated AI platform, including many models and data sources. Some platforms are also available as software-as-a-service to external R&D partners, such as Chemistry42.

One of the most vivid examples of benefiting from a “digital-first” strategy the industry has seen is Moderna Therapeutics, which not only managed to incorporate cutting-edge AI analytics in its research but digitalized and integrated every aspect of its R&D workflow, including production and distribution. When the COVID-19 pandemic struck the world at the beginning of 2020, Moderna was among the first companies to be able to come up with an efficient mRNA-based vaccine within several days (!) and bring it to the market within a year.??

A wave of therapeutics discovery successes enabled by AI demonstrates the ability of AI-native companies to come up with drug candidates faster than it typically used to take for similar programs.?

AbCellera’s monoclonal antibody LY-CoV555 was developed within three months and obtained emergency use authorization by the FDA.?

BenevolentAI’s Knowledge Graph helped the company identify Baricitinib as an efficient COVID-19 antiviral within a matter of days (now approved for use by the FDA). Another small molecule BEN-8744, a novel inhibitor to treat Ulcerative colitis and Dermatitis, was advanced to late preclinical studies within less than 24 months.?

Exscientia’s small molecule inhibitor EXS-21546 marked the first AI-designed molecule for immuno-oncology to enter human clinical trials (now in Phase I) and was discovered in just eight months. The company has several other molecules in clinical trials.?

Insilico Medicine’s small molecule inhibitor ISM001-055, to treat Idiopathic Pulmonary Fibrosis, was de novo designed and advanced into late preclinical studies within 18 months (now in Phase I).??

New York-based Schrodinger developed a small molecule SGR-1505 to treat B-cell lymphoma within ten months and is now in the process of IND application.?

Salt Lake City-based Recursion Pharmaceuticals developed a drug candidate for an unspecified rare disease within 18 months. The company has a large and diverse portfolio of preclinical and clinical drug candidates designed with the help of its digital biology platform.?

Toronto-based Deep Genomics used its AI Workbench platform to develop a novel genetic target and a corresponding oligonucleotide drug candidate DG12P1 to treat a rare inherited Wilson's disease.

To keep track of the leading AI-developed clinical drug candidates, we have created?“The Roadmap of Drug Candidates Designed by AI,”?which will be updated regularly.?

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Twenty most “productive” AI companies in the drug discovery space

Having shortlisted around 130 companies from more than 380 AI companies in the BiopharmaTrend AI Report, we have further selected 20 companies -- using a simple but robust evaluation formula taking into account clinical and preclinical pipelines of companies, the ability for target discovery, and the time in business. The 20 selected companies formed the?BPT20: Artificial Intelligence in Drug Discovery Productivity Index?-- the industry’s first point of reference to highlight companies championing the application of AI for de novo drug design, virtual screening, or drug repurposing.?

Traditionally, by the end of the year, I am summarizing key developments and trends in the AI for drug discovery space. So, here is the white paper, Artificial Intelligence in Drug Discovery and Biotech: 2022 Recap and Key Trends, which condenses this complex and dynamically evolving field into a dozen of chapters:

The advent of AI in drug discovery at a glance

What is AI, and how can it boost drug research?

AI drug discovery investment landscape, 2022

AI-enabled biology modeling and target discovery

Cracking structural biology with AI

Developing small molecules using AI

AI meets DNA-encoded libraries

AI-driven drug design beyond small molecules

The first wave of AI-developed drug candidates goes clinical

Twenty most “productive” AI companies in the drug discovery space

AI and robotized labs of the future

Navigating clinical trial bottlenecks with AI

AI in the contract research industry?

Technology giants go after drug discovery and biotech

The future of AI in drug discovery: all things "quantum"?

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Welcome to Biotech Oracle's newsletter "Where Technology Meets Biology." I am sharing noteworthy news, trends, biotech startup picks, industry analyses, and interviews with pharma KOLs. Contact [email protected] for consulting or sponsorship opportunities at www.BiopharmaTrend.com. Shop world-class chemistry for drug discovery at www.enaminestore.com.

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-- Andrii