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Interviews


[Interview] Shaping European Life Sciences with AI

   by Andrii Buvailo    558
[Interview] Shaping European Life Sciences with AI

There is a great deal of hype and a lot of misconceptions among life science experts as to how AI can or can’t be applied in pharmaceutical research and business. Judging by the rapidly increasing number of AI-involved deals and partnerships tapped by big pharma recently, it becomes obvious that life sciences decision-makers are eager to understand what this new and disruptive technology can bring to the table, and how it can be adopted efficiently with tangible ROI.

In order to get valuable first-hand insight and new ideas about the technology and its emerging role in the life sciences industry, I have asked several questions to Dr. Loubna Bouarfa, Founder and CEO at ​OKRA Technologies ― a leading AI company for healthcare, which builds a sophisticated AI-driven engine specialized in supporting faster and more accurate decisions for life science executives and field teams. Loubna is also a member of the European Union AI High-Level Expert Group (HLEG) and the winner of several prestigious awards, such as MIT Innovator Under 35 and Forbes Top 50 European Women in Technology. Last year, OKRA was named the Best Female-Led Startup at the StartUp Europe Awards.

[Interview] Using Generative AI to Rapidly Identify Novel Therapies for COVID-19

   by Andrii Buvailo    598
[Interview] Using Generative AI to Rapidly Identify Novel Therapies for COVID-19

Generative models have become one of the hottest areas in de-novo molecular design over just several years, basically revolutionizing our perception of what can be done with artificial intelligence in this area. One important aspect of generative models is that they can produce new quality hit molecules using combined data from various experimental and theoretical sources -- and output results rapidly. 

One notable drug discovery startup betting on deep learning and generative models for innovative drug design is Vancouver-based Variational AI.

[Interview] The Rise of Quantum Physics in Drug Discovery

   by Andrii Buvailo    3269
[Interview] The Rise of Quantum Physics in Drug Discovery

Computer-aided drug design (CADD) is a central part of so-called “rational drug design”, pioneered in the last century by companies like Vertex. Over the last decades, CADD had great influence on the way new therapeutics are discovered, however, it also showed limitations due to modest accuracy of computational tools.  

The majority of software tools used for computational chemistry and biology rely on molecular mechanics -- a simplified representation of molecules, essentially reducing them down to “balls and sticks”: atoms and bonds between them. In this way it is easier to compute, but accuracy suffers greatly.

In order to gain adequate accuracy, one has to account for the electronic behavior of atoms and molecules, i.e. consider subatomic particles -- electrons and protons. This is what quantum mechanical (QM) methods are all about -- and the theory is not new, dating back to the early decades of the 20th century.  

[Interview] Applying AI To Shape Business Strategies At European Pharma Organizations

   by Andrii Buvailo    1017
[Interview] Applying AI To Shape Business Strategies At European Pharma Organizations

The application of artificial intelligence (AI) in the pharmaceutical industry has become a long-term strategic priority for most companies. However, the efficiency of this endeavor depends greatly on the availability of large volumes of properly curated quality data, which is not always the case.

While pharma organizations generate huge volumes of data across all stages of drug discovery, development, and commercialization, not all types of data are equally useful for building efficient machine learning (ML) pipelines. For instance, it is relatively easier to apply AI-tech to consumer-related business processes, where lots of well-understood and properly labeled data is available, than it is for basic research tasks, where data is complex, often poorly labeled and extremely domain-specific.

The above situation leads to a faster pace of progress with AI application in such areas as financial analysis, consumer-behavior prediction, patient classification, marketing, and so on.

One of the important hurdles that pharma companies are trying to solve using AI tech is brand management. Indeed, understanding peculiar features of various patient categories, their purchasing behaviors, reactions to different products, revealing possible risks and side-effects for each class -- those things become essential for pharma companies to be able to develop and implement truly patient-centric brand management strategies. Luckily, this is one of the most fruitful areas for the application of machine learning (especially deep learning) models.

To get a better understanding of how it can be done, I have asked several questions to Agnieszka Wolk, Senior Director, Data Science, IQVIA, who recently presented this topic at the PMSA 2019 European Summit in Basel, Switzerland. . 

 

[Interview] Demystifying the Role of Artificial Intelligence in The Life Sciences

   by Andrii Buvailo    1096
[Interview]  Demystifying the Role of Artificial Intelligence in The Life Sciences

In this interview, Rasim Shah, Director at OKRA Technologies provided a glimpse into how the company applies state of the art machine learning technologies to solve real world challanges in the life sciences. Rasim also agreed to answer several questions about a more general context of AI in pharma, its current challanges and future perspective, as well as describe the current efforts the European Union puts into supporting the AI ecosystem in the region.

 

Rasim Shah, Director at OKRA Technologies:

OKRA Technologies is a leading European artificial intelligence (AI) company for life sciences. Our goal is to empower life science executives at their desks or whilst on the move, with explainable AI outputs. OKRA’s solutions deliver suggestions, predictions and explanations to enable life sciences executives and operational teams to drive the right drug to the right patient with humanised and understandable AI outputs. The OKRA engine learns from real-world data, structured, unstructured, clinical, commercial and scientific literature to drive the right insight to the different teams in life sciences. Our deep expertise in AI, combined with in-depth medical and product knowledge from life science leaders, has allowed us to develop and co-create products that can transform the way life sciences approach traditional industry challenges. We focus on operationalising AI in an ethical way by placing users of these systems at the centre.