In medicinal chemistry, it’s often the little things, like the subtle changes in a molecule’s structure, that make the difference. The number of aromatic rings, a lone heteroatom, a hidden amide, the magic methyl… all can tilt the balance between a promising lead and a project dead end.
In StarDrop 8, we’ve introduced a comprehensive set of structural descriptors designed to make these subtleties visible, measurable, and actionable. These new descriptors turn chemical intuition into quantifiable data, giving chemists another intuitive way to understand their data to make smarter design decisions.
Our new structural descriptors instantly tell you the numbers of:
All of the descriptors are available in StarDrop, ready to visualise, analyse, and integrate into SAR exploration, multiparameter optimisation (MPO) and predictive modelling workflows. That means no scripting and no moving data between multiple applications.
StarDrop users have told us that whilst advanced property models and machine learning tools are powerful, sometimes, what they need most is a simple, transparent metric to explain what they see in their data.
Structural descriptors give chemists that immediate insight to:
In short, these descriptors make structure-activity relationships even more accessible.
Structural descriptors are supported by Glowing Molecule visualisation, which highlights the regions of your compounds which have the greatest influence on predicted properties. By highlighting specific functional groups, rings, or atoms across your data set, you can explore how these features correlate with potency, ADMET properties, or other key parameters.
This makes it easy to:
Medicinal chemists are natural pattern recognisers. They can look at molecules and instinctively feel what’s likely to work. Structural descriptors, in combination with Glowing Molecule visualisation, evidence that intuition with numbers and instant visual feedback.
Want to understand why a series suddenly loses permeability? Check the hydrogen bond donor count or polarity balance.
Need to simplify a scaffold? Monitor aromatic ring count and heavy atom number.
Trying to optimise metabolic stability? Watch sulphur- and nitrogen-containing functional groups that drive clearance.
Every count and highlight becomes a usable data point that helps refine your design strategy with evidence-based decision-making.
In addition to enhancing analysis, structural descriptors strengthen StarDrop’s predictive power. When included in QSAR or machine learning models, they provide interpretable features that chemists innately understand.
And because they integrate seamlessly with StarDrop’s multi-parameter optimisation (MPO) scoring, you can use them as tunable parameters to balance molecular complexity, polarity, and synthetic accessibility right alongside potency and ADMET properties.
To demonstrate how our new descriptors can help identify structural features that drive potency, let’s look at a set of inhibitors against a well-studied target: Epidermal Growth Factor Receptor (EGFR) kinase.
The EGFR kinase is a validated oncology target with numerous inhibitors developed over the past two decades, from early anilinoquinazolines (e.g., gefitinib, erlotinib) to more advanced scaffolds addressing resistance mutations.
Across multiple published EGFR inhibitor series, medicinal chemistry studies1-3 have demonstrated that potency increases as aromatic ring count grows from mono- to tri-aryl systems, up to a point. Beyond this, additional aromatic rings tend to offer diminishing returns in potency whilst negatively impacting solubility and permeability.
Similar trends are observed in halogen substitution, where one or two strategic halogens can enhance potency through improved binding interactions/lipophilicity, but excessive halogenation can reduce overall drug-likeness.
These trends are well established and are easily observed using structural descriptors in StarDrop. In our data set4, we focus on aromatic ring and halogen counts and explore their relationship with potency.
In the Card View® layout below, the most potent compounds are in the top row. The Glowing Molecule highlights the 6-membered aromatic rings in red, whilst the card border is coloured according to the number of halogen substituents.
Scanning across the most potent compounds, we can quickly see that the common structural feature is an aromatic 6-6 fused ring, plus an additional phenyl. This is consistent with reported SAR studies. Further, the card border colouring illuminates the relationship between potency and the presence of two halogens (Chlorine and Fluorine), again consistent with published studies.
Simply complete the form now for a free personalised demo and see how StarDrop fits your unique research needs.
If you’d like to find out more about the StarDrop evaluation process read our step-by-step guide.
Product Manager
Rae holds a PhD in Theoretical Chemistry from the University of Missouri-Columbia.
After 2 years at the helm of Optibrium’s super-star software development team, she made a lateral move within the organisation to focus more on planning and scoping the products and technologies of the future.
As Product Manager, she leverages over two decades of experience in software development, applications science, and business development—including valuable time spent as a customer. Rae serves as the crucial “universal translator,” adeptly converting customer requirements into technical specifications that enable our developers to deliver effective solutions to complex problems
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