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Yes—an algorithm can design a protein from scratch to bind a small molecule that no natural protein has evolved to recognize. A physics-based workflow has already produced de novo binders for the anticoagulant apixaban and for the cancer-drug compound rucaparib. In the apixaban study, two of six designed sequences bound the drug, and X-ray crystallography showed that one complex closely matched its computational model. A 2024 extension produced two rucaparib-binding proteins with nanomolar-range dissociation constants.
Those results are proof that the design strategy can work, not evidence of an approved antidote or medicine. The proteins still need testing for selectivity, stability, safety, pharmacokinetics, manufacturing and activity in animals or people.
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What the protein-design algorithm is trying to do
The workflow starts with a chosen free small molecule rather than with a naturally occurring binding protein. Its goal is to build a compact protein whose amino-acid side chains form a chemically compatible pocket around the molecule. The search has to satisfy several constraints at once: the pocket must have the right three-dimensional shape, the protein must fold, the ligand must make favorable contacts, and the assembled structure must be energetically stable in water.
This is called de novo design because the sequence is not copied from a known drug receptor and then improved through a long directed-evolution campaign. The method instead searches computationally for new backbones and side-chain arrangements. The 2020 apixaban work showed that six designed sequences could yield two working binders; the 2024 report extended the approach to poly(ADP-ribose) polymerase-1 inhibitors (PARPi), including rucaparib.
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The central idea is to reason about molecular forces explicitly. A ligand group that normally hydrogen-bonds to water cannot simply be buried in a dry protein pocket without paying a desolvation cost. The design must replace the lost water interactions with hydrogen bonds or other favorable contacts to the protein, while also packing the surrounding atoms without clashes.
How the physics-based workflow works
1. Represent useful contacts as van der Mers
The foundational 2020 study introduced a structural contact unit called a van der Mer, or vdM. A vdM records how a residue backbone is positioned relative to a particular functional group on a nearby ligand. Instead of treating an entire protein pocket as an indivisible pattern, the algorithm can use these smaller, chemically meaningful contact motifs.
2. Mine experimentally solved structures
Researchers search Protein Data Bank structures for recurring contacts between amino-acid residues and ligand functional groups. Contacts that appear more often than expected provide statistical evidence that a particular geometry is favorable. Those preferences become building blocks for the computational search.
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3. Assemble a pocket around the target molecule
The program places vdM contacts around the selected drug or small molecule and looks for protein backbones that can support them simultaneously. Side chains are positioned to satisfy hydrogen-bonding, electrostatic and shape requirements. A successful arrangement must leave enough room for the ligand while preventing steric collisions.
4. Score water replacement, packing and stability
Physical energy terms rank the proposed designs. Polar atoms that lose interactions with water are penalized unless the protein supplies compensating hydrogen bonds. Packing quality, side-chain geometry and the stability of the unbound protein also matter. This prevents the algorithm from favoring a pocket that looks complementary on paper but would be unstable or poorly folded in solution.
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5. Select sequences for laboratory testing
Once promising backbones and side-chain arrangements are found, the computational design is converted into amino-acid sequences. Only a subset can be synthesized and tested, so the ranking stage is crucial. The experiments then determine whether the proteins express, fold and bind the intended molecule.
6. Compare the measured structure with the model
Binding assays provide an affinity measurement, commonly expressed as a dissociation constant (Kd). A lower Kd indicates tighter binding, but an affinity number alone does not prove that the protein adopts the intended architecture. Structural methods such as X-ray crystallography can test whether the ligand sits in the predicted pocket.
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| Study | Small molecule or target | Design and test result | Structural evidence |
|---|---|---|---|
| Polizzi and DeGrado, 2020 | Apixaban, an anticoagulant | Six sequences were constructed; two bound apixaban | An X-ray structure closely matched the computational protein–drug model |
| Lei Lu and colleagues, 2024 | PARP-1 inhibitors, including rucaparib | Two designed proteins bound rucaparib with nanomolar-range dissociation constants | The report establishes binding results; an equivalent apixaban-style X-ray confirmation is not reported in the supplied account |
| Baker-lab work summarized in 2022 | Protein targets in a related de novo binder demonstration | Twelve protein targets were used; large computational screens were followed by bacterial expression tests | The work illustrates a broader protein-binder design workflow rather than the free-small-molecule examples above |
The apixaban result is significant because two of six sequences worked without a prolonged evolution campaign. The success rate is not a promise that future drugs will have the same hit rate: each ligand presents a different geometry, chemistry and solubility problem.
The rucaparib result matters for a second reason. It shows that the method was not limited to one anticoagulant scaffold. PARP inhibitors are chemically distinct cancer-drug molecules, so the experiment tests whether the workflow can be adapted to another class of small-molecule target.
“It is now possible to design proteins from scratch that bind complex small molecules.” — William F. DeGrado, quoted by Chemistry World in 2020.
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Why this approach can help with unfamiliar drugs
Many machine-learning systems learn statistical regularities from structures represented in their training data. That is powerful when a new design resembles known examples, but a drug with an unusual scaffold or functional-group arrangement may be far outside that distribution. A physics-based search can extrapolate from general interaction rules—hydrogen bonding, desolvation, geometry and packing—even when no close natural binder is available.
This does not make physics-based design universally superior. The method still relies on accurate structural assumptions, suitable contact statistics and an energy model that ranks real proteins above attractive-looking failures. A ligand may be flexible, poorly soluble or chemically difficult to express around. The practical choice depends on the molecule, the target geometry, the available structural data and how many laboratory candidates can be tested.
“Deep learning could be learning physical principles,” Nicholas Polizzi told Chemistry World in 2024, “and the way to test that is to design things with it that are totally outside the distribution of the training data.”
How this differs from AlphaFold, RoseTTAFold All-Atom and RFdiffusion
These names are often grouped together because all use computation to reason about protein structures, but they address different design questions.
AlphaFold
AlphaFold is principally a protein-structure prediction system: given a sequence, it predicts a likely three-dimensional fold. That capability can help researchers inspect or prioritize proteins, but predicting a fold is not the same as searching for a new sequence whose pocket binds a specified free drug. The physics-based workflow described here starts with the ligand and performs that inverse design search.
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RoseTTAFold All-Atom
RoseTTAFold All-Atom extends learned structural modeling to all-atom interactions and complexes. In the related design literature, RoseTTAFold-family methods are associated with designing or evaluating binders against molecular surfaces, especially protein targets. A free small molecule such as apixaban or rucaparib presents a different problem: there is no extended protein surface to guide the design, so the pocket must be built around the ligand’s functional groups.
RFdiffusion
RFdiffusion is a generative approach used to create protein backbones and assemblies, including experimentally characterized binders for protein targets. Its strength is generating structural frameworks that satisfy a specified shape or interface. The vdM workflow instead uses experimentally observed residue–ligand contact geometries and explicit energetic reasoning to place a small molecule in a pocket.
| Design question | Physics-based vdM workflow | Related learned binder systems |
|---|---|---|
| Primary target | Free small molecules such as apixaban and rucaparib | Often protein surfaces, interfaces or assemblies |
| Main computational signal | vdM contact statistics, hydrogen-bond replacement, packing and physical energy | Statistical patterns learned by deep neural networks and generative structure models |
| Evidence highlighted here | Two of six apixaban designs bound; two rucaparib binders reached nanomolar-range affinity | Related work produced experimentally characterized protein binders and assemblies; one summarized demonstration tested twelve protein targets |
| Key extrapolation challenge | Finding a pocket for a molecule with no natural receptor | Generating or recognizing compatible protein structures and interfaces |
The approaches can also be combined in a research pipeline. A learned model might propose or evaluate a backbone, while contact statistics and physical scoring check whether a ligand pocket is chemically credible. None of these systems removes the need for synthesis and binding experiments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Could a designed protein become an overdose antidote?
In principle, yes. A high-affinity protein could be administered into the bloodstream, capture a drug and reduce the amount available to its physiological targets. The same concept could be considered for a severe reaction caused by a drug: removing the offending molecule from circulation could complement emergency treatment. These are proposed applications, not approved antidotes.
A practical antidote would need more than nanomolar binding in a purified assay. Researchers would have to establish:
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- Stoichiometry: enough protein must be delivered to capture a clinically relevant quantity of drug.
- Speed and distribution: binding must occur before the drug causes irreversible injury, and the protein must reach the relevant bloodstream compartment.
- Selectivity: the protein should capture the intended drug without binding essential medicines or endogenous molecules.
- Stability: it must remain folded in serum, resist degradation and avoid aggregating.
- Pharmacokinetics: researchers need to know how long the protein persists, where it goes and how the drug–protein complex is cleared.
- Immunological safety: a novel sequence could trigger antibodies, inflammation or other immune reactions.
- Manufacturing: production must be reproducible at a quality suitable for emergency use.
The protein might also serve as a delivery vehicle. In that design, the pocket would protect a small molecule during circulation and release it after the complex reaches a chosen site. Controlled release, tissue distribution and dose control are substantial engineering problems; the binding demonstrations alone do not solve them.
What still has to be tested
The published examples establish proof-of-concept binding, not clinical performance. A candidate can fail at several stages even after a favorable initial assay:
- It may express poorly in bacteria or mammalian cells.
- It may fold into multiple conformations or lose affinity outside the test buffer.
- It may bind the target but also capture chemically related compounds.
- Its pocket may be correct while the rest of the protein is unstable in serum.
- Its affinity may be insufficient at the concentrations required for therapy.
- Animal exposure, toxicity or immune responses may prevent further development.
The available reports do not provide a clinical trial, an approved therapeutic, a manufacturing process, a pharmacokinetic profile or a human-safety result for these designed drug-binding proteins. They also do not show that every arbitrary drug can be handled equally well. Affinity, selectivity, folding, expression, stability and off-target binding remain experimental questions for each new design.
Why the candidate count matters
Computational design is valuable partly because it can narrow an enormous sequence space before synthesis. The apixaban study built six sequences and obtained two binders, an unusually direct demonstration that a small experimental set can yield a hit. In other de novo binder projects, large computational screens were followed by bacterial expression tests across twelve protein targets. The number of tested candidates, however, must always be reported alongside the success count: a single successful protein does not reveal how many designs failed before it.
For readers comparing methods, five measures are especially informative: whether the target is a free molecule or a protein surface; whether scoring is physics-based or learned; how many candidates were synthesized per hit; how closely measured structures agree with models; and whether the work has progressed to cells, animals, pharmacokinetics, manufacturing and safety studies.
The bottom line for drug-binding protein design
Physics-based de novo design has crossed an important threshold: researchers have made new proteins that bind complex drugs, including apixaban and rucaparib, without starting from a natural receptor. vdM contact mining gives the algorithm reusable geometric knowledge, while energetic scoring forces the pocket to account for water, hydrogen bonds and packing. The results justify further work on molecular capture and delivery, but they are early-stage demonstrations. A designed protein becomes an antidote or delivery medicine only after rigorous selectivity, stability, pharmacology, safety and clinical testing.
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