Could q-CAR Redefine Drug Discovery?

q-CAR drug discovery could represent the next step in the evolution from empirical medicine to precision molecular design.

Understanding how proteins move, not simply how they look, could give researchers a more accurate way to design drugs around disease-relevant protein states. 

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From Trial And Error To Structure-Based Discovery

The history of drug discovery has moved from observation towards increasingly precise biological and computational methods.

Early medicines emerged through trial and error. Plants, minerals, and animal-derived materials were used based on observed effects.

Later, researchers isolated active compounds, developed vaccines, and moved towards systematic pharmacology.

Penicillin’s development and large-scale production in the 1940s marked another major step towards modern pharmaceutical development. 

The next major shift came with high-throughput screening (HTS). Automated systems allowed researchers to test large compound libraries against cells and biological targets.

Disease-oriented models later incorporated iPSCs, organoids, and 3D systems to reproduce aspects of human biology more closely.

Structure-based drug discovery then transformed drug discovery again. Instead of simply asking whether a compound produced an effect, researchers could examine how molecules interact with a three-dimensional protein structure.

The development of X-ray crystallography, cryo-EM, molecular modelling, and computational screening expanded this approach considerably.

More recently, AI has accelerated analysis and virtual screening, although the quality and consistency of experimental data remain important limitations.

Pharmatica’s analysis of the DMTA cycle explores how these technologies increasingly connect design, making, testing, and analysis in the drug discovery process.

Read Reimagine AI-driven Drug Discovery with Pharma Superintelligence

Reimagine AI-driven Drug Discovery with Pharma Superintelligence

Explore how AI-driven drug discovery is evolving through Pharmaceutical Superintelligence and what your organisation can do to keep up.

Why Static Protein Structures Are Not Enough for Drug Discovery

Quantitative conformation-activity relationship (q-CAR) positions proteins as hugely dynamic systems.

A conventional structure-based approach can provide an extremely detailed molecular snapshot. But a protein does not remain frozen in that configuration inside a living system.

Proteins shift between multiple conformations, with those states influencing ligand binding, signalling, and biological activity.

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This creates a blind spot for conventional QSAR and structure-based screening. A single structure may fail to represent less populated states, transient binding pockets, or conformations that become important when a ligand or signalling partner interacts with the protein. 

Recent research supports the importance of this dynamic view. A 2026 review in Trends in Pharmacological Sciences describes proteins as conformational ensembles and highlights transient pockets and alternative states as opportunities for allosteric drug discovery. 

How q-CAR Changes the Drug Discovery Question

Quantitative Conformation-Activity Relationship, or q-CAR, aims to measure the relationship between protein conformational states and biological activity.

The proposed approach goes beyond identifying whether a molecule binds. Researchers would quantify different protein states, determine how ligands shift the population of those states, and connect those changes with signalling outcomes.

One particular promising drug discovery tool is MilliporeSigma’s 19F NMR. It can profile multiple conformational states and monitor transitions during ligand interactions. Cryo-EM, single-molecule FRET, DEER, and time-resolved X-ray methods are all complementary approaches.

This could give discovery teams a different design objective:

• Identify the protein state associated with a desired biological response.
• Find compounds that preferentially stabilise or shift that state.
• Link conformational changes directly to signalling or functional output.
• Use experimentally generated datasets to improve computational and AI models.

The concept could be as useful for G protein-coupled receptors (GPCRs) as it is for other multi-state proteins, enzymes, and transporters. q-CAR could even identify compounds that hold drug-efflux transporters in partially open states, potentially helping address multidrug resistance. 

The Future Drug Discovery Loop Needs Protein Motion

q-CAR remains a proposed framework rather than an established replacement for current discovery platforms.

Significant technical barriers remain, including NMR sensitivity, protein production, fluorine probes, physiological relevance, standardisation, and the scalability of NMR-based screening. 

Ultimately, the value of q-CAR will depend on whether dynamic biological measurements can become sufficiently reproducible and scalable for real discovery programmes.

AI could become an important bridge. Combining experimentally measured conformational landscapes with AI models, allowing relatively small experimental datasets to inform larger-scale computational discovery.

This fits a broader shift in drug discovery towards richer biological models and more integrated computational workflows to address current drug discovery challenges.

Pharmatica analyses how these approaches are changing the Discovery Loop. We focus on the technologies and scientific strategies reshaping pharmaceutical R&D, separating genuine advances from promising concepts that still need validation.

Pharmatica: Insight. Connection. Impact.

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