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

New Tools, Products And Technologies

Reviews, news and use cases

18 Startups Using Quantum Theory To Accelerate Drug Discovery

   by Andrii Buvailo    10207
18 Startups Using Quantum Theory To Accelerate Drug Discovery

Molecular mechanics (MM) is a traditional computational approach when it comes to modeling in synthetic organic chemistry, medicinal chemistry and versatile aspects of drug design. However, MM methods have significant limitations, for example, when used to study electron-based properties within the drug-receptor microenvironment. Quantum mechanical (QM) methods allow to substantially increase the accuracy of predictions and provide much more relevant models of chemical and biological objects and their interactions, but QM methods are extremely (often prohibitively) computationally costly.

However, a series of advancements over recent years allowed to expand horizons in this direction, for example, the emergence of density functional theory (DFT), the overall increase in the computation power and the emergence of distributed cloud-based computational infrastructures.

Virtual Immunostaining for Digital Pathology

   by Victor Dillard    799
Virtual Immunostaining for Digital Pathology

Owkin is a French-American startup, backed by Google Ventures among others, that deploys Artificial Intelligence (AI) and Federated Learning for medical research. The company was co-founded in 2016 by Thomas Clozel, a hematologist oncologist and researcher, and Gilles Wainrib, a computer science teacher-researcher at the École Normale Supérieure, and a Stanford University PostDoc.

Over the years, the team at Owkin has developed AI systems to analyze and interpret multimodal medical data, including pathology images, radiology images, genetic data, lab analysis, and clinical outcomes. The company recently announced the launch of a new product -- Virtual Staining, integrated as a feature in Owkin Studio platform.

19 Marketplaces for the Life Sciences Industry

   by Andrii Buvailo    26556
19 Marketplaces for the Life Sciences Industry

(Last updated: 12.05.2020)

Online marketplaces are websites with a “many-to-many” business logic. They can host multiple suppliers trading with multiple buyers via different e-commerce tools available as a part of a website functionality.

Why are online marketplaces great?

Online marketplaces can provide a substantial added value to its users. For example, buyers can quickly compare and select better offerings without the need to research multiple websites and surf online for price comparisons or product specifications. Additionally, marketplaces bring more transparency, trust, and standardization to the whole process of sourcing.

Tzager - A Smart AI Agent For Biomedical Research

   by Nikos Tzagarakis    1182
Tzager - A Smart AI Agent For Biomedical Research

Tzager is an A.I. agent built for biomedicine research, drug discovery and personalized medicine, with the main features being Biochemical Analysis, Predictor Research/Models and Literature Review/Management. The difference with Tzager is that it is not just another deep learning algorithm trained to solve very specific problems, but the intelligence system with its own framework based on Causal Equations and Bayesian Networks.

ConstruQt – The Beginnings of the Chemical Data Revolution

   by Peter Jarowski    580
ConstruQt – The Beginnings of the Chemical Data Revolution

Chemical Data Has Problems

The state of data access, quality and dissemination in Chemistry is extremely poor - so poor that it is blocking advances in machine learning (ML) and artificial intelligence (AI), and also impeding research and development in traditional methods. The recent surge in AI skepticism is a direct consequence of years of over-hype and promises based on precarious data. Over-the-top expectation were offered without enough consideration for the data quality and volume required to train fancy algorithms. The old adage “^&$% in, ^&$% out” holds true (we can say ‘crap’ right?). This opinion is in line with recent statements by the CEO of Novartis, for example, who runs the second largest pharmaceutical company in the world, lamenting the difficulty in accessing quality datasets to make AI effective.