A Landscape of Artificial Intelligence (AI) In Pharmaceutical R&D

Where you find opportunities to grow

* Can't find your brand, product, service? Join BPT Marketplace

Startups

Section: Biopharma Insights     View all sections


[Interview] Expediting Drug Discovery Through Advanced Machine Learning

   by Andrii Buvailo    438
[Interview] Expediting Drug Discovery Through Advanced Machine Learning

The application of next-generation data analytics tools, powered by machine learning and artificial intelligence (AI) components, has become a long-term strategic priority for most companies in the pharmaceutical and biotech industries. However, such systems have to make sure the organisational data is findable, accessible, interoperable, and reusable across different sub-systems, applications, departments, teams, and even companies. 

Aigenpulse, a technology company at the forefront of data management and analytics in the Life Science industry, has built a portfolio of tools for working with organisational research data at scale and accelerating the discovery and development of better targets and candidates using advanced machine learning technologies.

7 Notable AI Companies in Clinical Research to Watch in 2020

   by Irina Bilous    1537
7 Notable AI Companies in Clinical Research to Watch in 2020

The clinical trial is a critical stage of drug development workflow, with an estimated average success rate of about 11% for drug candidates moving from Phase 1 towards approval. Even if the drug candidate is safe and efficacious, clinical trials might fail due to the lack of financing, insufficient enrollment or poor study design. [Fogel DB. 2018].  

Artificial Intelligence (AI) is increasingly perceived as a source of opportunities to improve operational efficiency of clinical trials, and minimize clinical development costs.  Typically AI vendors offer their services and expertises in the three main areas. AI start-ups in the first area help to unlock information from disparate data sources, such as scientific papers, medical records, disease registries, and even medical claims by applying Natural Language Processing (NLP). This can support patient recruitment and stratification, site selection, improve clinical study design and understanding of diseases mechanisms. As an example, about 18 % of clinical studies fail due to insufficient recruitment, as 2015 study reported.

Major VC Rounds For AI-driven Biotechs in 2020

   by Andrii Buvailo    1329
Major VC Rounds For AI-driven Biotechs in 2020

The ongoing COVID19 crisis forced biotech-oriented venture capitalists (VC) to somewhat slow down the pace of their usual deal-flow -- according to data by PitchBook biopharma venture deals in 2020 are down roughly 16% compared to last year. 

However, there is still a lot going on in drug discovery and healthcare, and here I would like to specifically focus on artificial intelligence (AI)-driven biotech and health tech startups who managed to raise notable rounds in 2020, so far (based on our report “A Landscape of Artificial Intelligence (AI) In Drug Discovery and Development”). The deals are ordered by the amount of money raised 

[Interview] How COVID-19 Catalyzed AI-assisted Open Science Drug Discovery

   by Andrii Buvailo    1407
[Interview] How COVID-19 Catalyzed AI-assisted Open Science Drug Discovery

Biopharma companies are now racing to find much-needed cures against SARS-CoV-2, a virus that caused the largest global pandemic of our time. One notable effort is the COVID Moonshot project, organized by an international consortium of scientists from academia, biotechs, contract research organizations (CROs), and pharma -- all working pro bono or via crowdfunding, philanthropy, and grants. 

The aim of the project is to rapidly develop easily manufacturable antiviral drugs that can inhibit the SARS-CoV-2 main protease, which is believed to be an Achilles heel of the coronavirus. The project is managed by PostEra, a startup company that uses artificial intelligence (AI) algorithms to map routes for chemical synthesis to speed the drug-discovery process.