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

Artificial Intelligence


[Interview] A New Way To Work With Data In Life Sciences

   by Andrii Buvailo    205
[Interview] A New Way To Work With Data In Life Sciences

Founded by renowned database researcher, Turing Award laureate MIT Professor Michael Stonebraker, Paradigm4 is not just any data analytics company in the Life Sciences. The organization is built on the decades of pioneering research in database design and possesses unique technological know-how in scientific data management and scalable computation. 

The firm has recently launched its REVEAL™: Single Cell app to offer biopharmaceutical developers the ability to break through the data wrangling and programming challenges associated with the analysis of large-scale, single-cell datasets. 

[Interview] Expediting Drug Discovery Through Advanced Machine Learning

   by Andrii Buvailo    244
[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.

Clinical Research, Artificial Intelligence, and COVID-19

   by Raj Indupuri    369
Clinical Research, Artificial Intelligence, and COVID-19

How life sciences companies are reimagining trials during the biggest health crisis in a century

Whatever the world was like on March 15, it’s not like that now — and it probably won’t be for months or years. Everything from buying groceries to renewing a driver’s license is completely different from the way we did things just a few months ago. Clinical trials, like most medical activities, have been significantly affected as governments and pharmaceutical companies have pivoted to a single focus: combatting the novel coronavirus. At the same time, the rush to develop cures and vaccines for COVID-19 is condensing the review and approval process from years to months. This is where artificial intelligence (AI) can play a vital role in changing how clinical trials are conducted and how therapies are evaluated and tested.

Aigenpulse launches data analysis suite to automate flow cytometry

   by Aigenpulse    476
Aigenpulse launches data analysis suite to automate flow cytometry

11th September 2020: Life science and data technology innovator, Aigenpulse, is launching its CytoML Experiment Suite – an automated, end-to-end, machine learning solution specifically aimed at streamlining and automating cytometry analysis at scale and replacing manual gating processes. With it, users will benefit from a single point-of-truth about all cytometry data across any organisation.

7 Notable AI Companies in Clinical Research to Watch in 2020

   by Irina Bilous    1312
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.