Category Archives: Drug Discovery

How reliable are affinity datasets in practice?

The Data Bottleneck in AI-Powered Drug Discovery

The pharmaceutical industry is undergoing a profound transformation, driven by the promise of Artificial Intelligence (AI) and Machine Learning (ML). These technologies offer the potential to escape the industry’s persistent challenges of high costs, protracted development timelines, and staggering failure rates. From accelerating the identification of novel biological targets to optimizing the properties of lead compounds, AI is poised to enhance the precision and efficiency of drug discovery at nearly every stage

Yet, this revolutionary potential is constrained by a fundamental dependency. The power of modern AI, particularly the deep learning (DL) models that excel at complex pattern recognition, is directly proportional to the volume, diversity, and quality of the data they are trained on. This creates a critical bottleneck: the high-quality experimental data required to train these models—specifically, the protein-ligand binding affinity values that quantify the strength of an interaction—are notoriously scarce, expensive to generate, and often of inconsistent quality or locked within proprietary databases.

Continue reading →

Can AI help us design better viruses?

Viruses are the most abundant biological entity on the planet. They infect virtually every kind of life form including (sort of) other viruses. Viruses are intensely efficient – some viruses contain as few as 4 genes. Their strategy is typically simple: infect a cell, use its machinery to produce more viruses, and spread to other cells.

Pathogenic human viruses are terrible, but there are many other viruses which are useful for humans. For instance, many modern vaccines use viral vectors to produce antigens of other pathogenic entities. There is also growing interest in using viruses to fight off bacterial infections.

Continue reading →

Open Source Pharma: From Idealism to Pragmatic Solutions

In an industry dominated by patents, proprietary data, and the race to get a first-in-class drug, the concept of open source drug development once seemed like an impossible dream. Yet as traditional pharma continues to leave many global health needs unaddressed—particularly for diseases affecting low and middle income countries1,2—the open source model has evolved from idealistic theory to pragmatic reality. In this post, I’ll lead us through how open source drug development has overcome key obstacles of funding and intellectual property (IP) management to deliver real-world solutions.

Continue reading →

The business of health: research and funding from academia to big pharma

In a world in which the probability of clinical success is just 10%-20% for new medicines, pharmaceutical multinationals increasingly turn to academia and biotech as a source of “de-risked” technology for their pipelines. This exchange of ideas, equity and capital depends on firm relationships between entities having apparently divergent interests: from not-for-profit research to international commerce.

As a former pharma contract negotiator, I spent much of my past life attempting to find common ground with university researchers and biotech leadership teams. In 2021, I had the privilege of returning to academia in the UK after a prolonged hiatus, and – more recently – of working with start-ups. In this blog, I will comment on some of the surprising trends I have observed in how pharma, biotech and academics balance the conduct of meaningful research with the requirements of their respective sectors. The views herein are entirely my own.

Continue reading →