UBS: Generative AI is no silver bullet for drug discovery

[Image courtesy of Scanrail/Adobe Stock]

Imagine a world where the process of developing life-saving drugs is as streamlined as a modern assembly line.

In such a reality, generative AI in drug discovery might churn out promising compounds with similar efficiency and precision as a factory robot assembling a car. Moreover, such technology could chip away at the steep cost and lengthy timelines typically associated with drug development, which can cost north of $2 billion and take more than a decade.

However, such a vision may be more hype than reality, according to a new UBS report. While it is true that AI is carving out a niche in life sciences, a recent Q-Series report from UBS titled “Will Generative AI deliver a generational transformation?” reaches mostly muted conclusions about the potential of generative AI in drug development. In essence, the investment bank projects that generative AI…

Read more
  • 0

A current perspective on machine learning’s role in advancing clinical trials diversity

[Image courtesy of Adobe Stock]

The year 2020 was a watershed moment for many reasons, but notably, it cast a light on the pervasive health and social inequities that have long marred the U.S. The COVID-19 pandemic hit diverse populations disproportionately hard, as Deloitte and others have noted. Additionally, the tragic deaths of George Floyd, Breonna Taylor and others provoked an uproar over systemic racism that permeates society, including healthcare. This period of societal upheaval has also underscored the necessity of novel approaches involving techniques like the use of machine learning in clinical trials, to ensure that diverse populations are represented.

Such disparities in healthcare were further highlighted when Moderna, soon to become a critical player in the vaccine race, faced a glaring revelation in late 2020. Only 24% of participants in their phase 3 study were from communities of color, despit…

Read more
  • 0

Decoding the enigma of the commander complex: Employing AlphaFold2 to illuminate biological structures

The commander complex. [New image from Institute for Molecular Bioscience The University of Queensland]

Machine learning algorithms, such as Alphabet’s neural network-based model AlphaFold2, are steadily transforming medical research, shedding light on complex biological structures. A recent case in point involves research using the technology to explore the Commander complex, a 16-protein complex crucial for cellular protein transport processes. This complex is not only vital for normal cellular function, but it’s also associated with several diseases, making it a potential target for novel therapies. Scientists at the Universities of Bristol and Queensland (Australia) and the Medical Research Council Laboratory of Molecular Biology in Cambridge, collaborated on the research.

The renowned journal Cell published the study, titled “Structure of the endosomal Commander complex linked to Ritscher-Schinzel syn…

Read more
  • 0

BenevolentAI is pioneering AI-driven drug discovery methods

“Can we treat chronic inflammation in ulcerative colitis by reversing immune cell activation in colonic mucosa?” That’s an example of a biological question that the AI-enabled drug discovery firm BenevolentAI (AMS:BAI) would ask when exploring a new drug target.

Incorporating a disease, sign, mechanism and tissue into a single question provides focal points for the company’s AI models to explore when generating target hypotheses, said Anne Phelan, chief scientific officer of Benevolent AI in a presentation at the Royal Society earlier this year.

BenevolentAI’s strategy involves a comprehensive understanding of the biological systems underlying various diseases, breaking down silos in clinical areas and tapping diverse multimodal data to discover novel therapeutic targets. For ulcerative colitis, the company’s AI models sift through vast amounts of scientific literature and data to spot promising targets and pathways that could potentially alleviate …

Read more
  • 0