In what I have to admit is now becoming an annual tradition ([2023] [2019]), I’d like to highlight the 2024 edition of the fragment-to-lead success stories, published in J. Med. Chem. at the end of 2025 [Paper].
Continue readingBack in 2021, I highlighted the annual fragment-to-lead (F2L) success stories from 2019 [Blog post] [Paper]. This is one of my favourite annual publications, and I’m delighted to see that it’s still going strong. In this post, I’ll discuss the 2023 edition that was published in at the start of 2025 [Paper].
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 readingSince OPIG began in 2003, 53 students* have managed to escape. But where are these glorious people now? I decided to find out, using my best detective skills (aka LinkedIn, Google and Twitter).
* I’m only including full members who have left the group, as per the former members list on the OPIG website
Firstly, the countries. OPIGlets are mostly still residing in the UK, primarily in the ‘golden triangle’ of London, Oxford and Cambridge. The US comes in second, followed closely by Germany (Note: one former OPIGlet is in Malta, which is too small to be recognised in Geopandas so just imagine it is shown on the world map below)
In this blogpost, I want to highlight the excellent work by Jahnke and collaborators. For the past 5 years, they have published an annual perspective covering fragment-to-lead success stories from the previous year. Very helpfully, their work includes a table detailing the hit fragment(s) and lead molecule, together with key experimental results and parameters.
Continue readingIn drug discovery, compound promiscuity and selectivity refers to the ability of drug compounds to bind to several different- (promiscuous) or only one main target (selective). An important distinction here is that promiscuity is defined as specific interactions with multiple biological targets (polypharmacology) rather than a number of non-specific targets. At first glance, you might expect drugs to be designed to be as selective as possible, only hitting one biological target necessary to treat the disease and therefore reduce the chance of any side effects. This paradigm of single-target specificity has been challenged over the past two decades. Even between scientists in the drug discovery field, compound promiscuity is still a controversial topic. The field has increasingly paid attention to the topic of polypharmacology and studies have shown many pharmaceutically relevant compounds, including approved drugs to derive their biological activity from polypharmacology [1-3].
Continue readingProf. Charlotte Deane, the new Deputy Executive Chair of the EPSRC, Deputy Head of Division of MPLS, and Head of the Oxford Protein Informatics Group, was interviewed by BBC World Service’s programme “Tech Tent”, about the role of AI in drug discovery; jump to about 13:30 to hear Charlotte, and the segment on AI in healthcare starts at 9:45:
The dominant paradigm in drug discovery has been one of finding small molecules (or more recently, biologics) that bind selectively to one target of therapeutic interest. This reductionist approach conveniently ignores the fact that many drugs do, in fact, bind to multiple targets. Indeed, systems biology is uncovering an unsettling picture for comfortable reductionists: the so-called ‘magic bullet’ of Paul Ehrlich, a single compound that binds to a single target, may be less effective than a compound with multiple targets. This new approach—network pharmacology—offers new ways to improve drug efficacy, to rescue orphan drugs, re-purpose existing drugs, predict targets, and predict side-effects.
Building on work Stuart Armstrong and I did at InhibOx, a spinout from the University of Oxford’s Chemistry Department, and inspired by the work of Shoichet et al. (2007), Álvaro Cortes-Cabrera and I took our ElectroShape method, designed for ultra-fast ligand-based virtual screening (Armstrong et al., 2010 & 2011), and built a new way of exploring the relationships between drug targets (Cortes-Cabrera et al., 2013). Ligand-based virtual screening is predicated on the molecular similarity principle: similar chemical compounds have similar properties (see, e.g., Johnson & Maggiora, 1990). ElectroShape built on the earlier pioneering USR (Ultra-fast Shape Recognition) work of Pedro Ballester and Prof. W. Graham Richards at Oxford (Ballester & Richards, 2007).
Our new approach addressed two Inherent limitations of the network pharmacology approaches available at the time:
Our method addressed these issues by taking into account 3D information from both the ligand and the target.
The approach involved comparing the similarity of each set ligands known to bind to a protein, to the equivalent sets of ligands of all other known drug targets in DrugBank, DrugBank is a tremendous “bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.” This analysis generated a network of related proteins, connected by the similarity of the sets of ligands known to bind to them.
We looked at two different kinds of ligand similarity metrics, the inverse Manhattan distance of our ElectroShape descriptor, and compared them to 2D Morgan fingerprints, calculated using the wonderful open source cheminformatics toolkit, RDKit from Greg Landrum. Morgan fingerprints use connectivity information similar to that used for the well known ECFP family of fingerprints, which had been used in the SEA method of Keiser et al. We also looked at the problem from the receptor side, comparing the active sites of the proteins. These complementary approaches produced networks that shared a minimal fraction (0.36% to 6.80%) of nodes: while the direct comparison of target ligand-binding sites could give valuable information in order to achieve some kind of target specificity, ligand-based networks may contribute information about unexpected interactions for side-effect prediction and polypharmacological profile optimization.
Our new target-fishing approach was able to predict drug adverse effects, build polypharmacology profiles, and relate targets from two complementary viewpoints:
ligand-based, and target-based networks. We used the DUD and WOMBAT benchmark sets for on-target validation, and the results were directly comparable to those obtained using other state-of-the-art target-fishing approaches. Off-target validation was performed using a limited set of non-annotated secondary targets for already known drugs. Comparison of the predicted adverse effects with data contained in the SIDER 2 database showed good specificity and reasonable selectivity. All of these features were implemented in a user-friendly web interface that: (i) can be queried for both polypharmacology profiles and adverse effects, (ii) links to related targets in ChEMBLdb in the three networks (2D, 4D ligand and 3D receptor), and (iii) displays the 2D structure of already annotated drugs.
References
Johnson, A. M., & G. M. Maggiora (1990). “Concepts and Applications of Molecular Similarity.” New York: John Willey & Sons.
Landrum, G. (2011). “RDKit: Open-source cheminformatics.” from http://www.rdkit.org.