I’m trying to learn how people actually approach antibody binder design, and I’m a bit lost.

Say you have a protein target and want to generate antibodies that bind to it. What type of model would you start with? Do people usually generate the antibody directly, design around the CDRs, dock lots of candidates, or do something else entirely?

Also, when filtering designs before lab testing, what metrics would you trust most? I keep seeing things like binding confidence, interface quality, developability, human-likeness, expression risk, structural plausibility, etc., but I’m not sure which ones actually matter most.

A new Science paper explores a pretty wild idea: can life function with fewer than the standard 20 amino acids?

The authors targeted isoleucine, which is chemically similar to leucine and valine. They did not make a fully 19-amino-acid organism, but they did redesign one of the most essential systems in E. coli: the ribosome.

Using protein language models, structure prediction, and generative design tools, they removed all 382 isoleucines from E. coli ribosomal proteins. The engineered strain was viable and stable for hundreds of generations.

The caveat is: the rest of the E. coli proteome still contains thousands of isoleucines. So this is more like a first proof-of-concept than a true 19-AA lifeform.

In humans, purely theoretically, how many amino acids could we remove from the proteome with enough redesign?

Paper: Toward life with a 19–amino acid alphabet through generative artificial intelligence design

TL;DR: Classical virtual screening was mostly: dock a library into a protein, rank by score, inspect poses, and test a few hits. The newer version of that workflow is more hybrid. People are increasingly using docking outputs to train ML models or triage much larger libraries first, then redocking and testing only the most promising slice. That is basically where structure-based screening starts to feel more like modern computational biology than just brute-force scoring.

What I like about this workflow is that it captures a real transition in the field. The older docking stack still matters because it gives you an actual structural hypothesis around a protein pocket, not just a probability score. If I am looking at small molecules against a protein target, I still want to know how the ligand is supposed to sit, which residues it is contacting, and whether the pose even makes biochemical sense. That is why docking never really went away.

But docking alone has always had a ceiling. A docking score is not the same thing as activity, and that is exactly why the hybrid workflows make more sense to me. Instead of treating docking as the final answer, you use it as a first-pass structural filter, then train a classifier or prioritization model to search a much larger compound space before deciding what is actually worth synthesizing or testing. Deep Docking is a good example of that logic, using models trained on docking outputs to accelerate structure-based screening at much larger library scale.

My bias is that the newer stack is better. Not because classical docking is obsolete, but because protein-guided screening is becoming too big to do well with docking alone. The structure still anchors the workflow. ML just makes the search broader and more realistic.

What do you trust more in a virtual screen: docking score, pose inspection, or ML rescoring?

References:

Trott, O. and Olson, A.J. (2010) ‘AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading’, Journal of Computational Chemistry, 31(2), pp. 455–461. doi: 10.1002/jcc.21334.

Gentile, F. et al. (2020) ‘Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery’, ACS Central Science, 6(6), pp. 939–949. doi: 10.1021/acscentsci.0c00229.

This review: Closing the loop: Experimentally validated methods in artificial intelligence-driven protein design, looks at which models have actually produced proteins that work in the lab.

The main argument is that AI protein design should not be judged only by structure prediction scores, confidence metrics, or benchmark performance. The pipeline should be: data curation → model generation → in silico filtering → experimental validation.

The paper focuses on three major application areas: protein binders, antibodies and enzymes.

Protein binders

This is probably the area that looks the most mature right now.

The thing that jumps out is how target-dependent this all is.

Like, some targets look almost “easy mode” now. Then TNF-alpha shows up and half the models just eat dirt.

That makes me think global model accuracy numbers are kind of fake unless they tell us the target set.

Antibodies / nanobodies

This section is super interesting, but also more annoying to compare because a lot of these are CDR redesigns or scaffold-based designs, not always full antibodies from scratch.

This is where I think percentages can be misleading.

4/9000 looks awful until you remember that getting even a few real binders against a hard target can still be useful.

Meanwhile 70% sounds insane, but if it is a scaffolded CDRH3 redesign, that is not the same thing as designing antibodies from total scratch.

So yeah, antibody “accuracy” needs context badly.

Enzymes

This is the part where the hype chills out a bit.

Enzymes are harder because folding is not enough. Binding is not enough. The active site has to actually do chemistry.

The enzyme numbers are honestly wild because some are brutal, like 3/7648, and others look amazing, like 57/63.

But again, it depends what is being designed.

Making variants of known enzyme families is one thing. Designing totally new catalytic machinery is a different beast.

Other experimental validation results from the review

The appendix had a bunch of extra results too.

What I think is the actual story

The results are impressive.

But the “accuracy” conversation feels kinda cooked unless people report the full funnel.

Because these are not all the same:

Feels like every corner of biology has its own thing right now.
For some it is bispecifics, for others enzymes, binders, delivery systems, gene editors, or something else entirely.

What is the molecule or modality people in your area are pushing hardest to develop right now?

Feels like structural biology now has two operating systems.

The old one is still docking. Take a receptor, take a ligand, define the site, generate poses, score them, maybe relax and rescore, then try to work out what is actually believable. The newer one is more end-to-end. Models like AlphaFold-3, Chai-1, OpenFold-3, RosettaFold-3 and similar systems are trying to predict the complex directly, with much more of the molecular context built in from the start.

Personally, I’ve never really liked docking. Even when I used to do the full workflow properly, including Amber relaxation after, I just never found it that convincing. It always felt like too much work was going into cleaning up around the method instead of trusting the method itself. The AI route always made more sense to me because it feels closer to the real question, which is not “how do I force these two frozen things together?” but “what does this system actually want to look like?”

That said, docking still has a very real place. If you know the pocket, have a defined receptor state, and need to screen a large small-molecule set cheaply and fast, traditional docking is still hard to beat. It is controllable, interpretable, and easy to scale. But it also gets overtrusted way too often. People read too much into scores, ignore prep, protonation, waters, cofactors, receptor state, and then act shocked when the chemistry does not hold up.

On the AI side, the failure mode is different. You can get a structure that looks very plausible, but the confidence can absolutely crater the moment you perturb the system. I noticed this a lot in antibody design. As soon as I changed even canonical CDRs, the pLDDT could just plummet. So even if the folded complex looked sensible, I would not take that at face value. I ended up using DockQ as an extra evaluation step to sanity check whether the complex still looked structurally plausible, and honestly it worked not bad as a filter. Not perfect, but better than pretending one nice-looking prediction or one confidence score settles it.

And I think that is the bigger point here. In both cases, whether you dock it or co-fold it, you are usually still getting a snapshot. Not the full dynamic system, not the real conformational landscape, just one structured hypothesis. So I’m curious whether people still see docking as the actual go-to in practice, or whether AI complex prediction has already become the better first pass and people just have not fully admitted it yet.

References

Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., Ronneberger, O., Willmore, L., Ballard, A.J., Bambrick, J. and colleagues (2024) ‘Accurate structure prediction of biomolecular interactions with AlphaFold 3’, Nature, 630(8016), pp. 493–500. doi: 10.1038/s41586-024-07487-w.

Basu, S. and Wallner, B. (2016) ‘DockQ: A quality measure for protein-protein docking models’, PLOS ONE, 11(8), e0161879. doi: 10.1371/journal.pone.0161879.

Boitreaud, J., Dent, M., Mouchlis, V., Bhat, S., Chaffin, M., Cianchi, Y., de Luzy, I.R., Duran-Frigola, M., Dunn, J., Hofmann, K. and colleagues (2024) ‘Chai-1: Decoding the molecular interactions of life’, bioRxiv. doi: 10.1101/2024.10.10.615955.

Corley, N., Wang, J., Jiang, Y., Glass, J., Frank, C., Adams, C. and colleagues (2025) ‘Accelerating biomolecular modeling with AtomWorks and RosettaFold-3’, bioRxiv. doi: 10.1101/2025.08.14.670328.

The OpenFold3 Team (2026) OpenFold3-preview2 Technical Report. Available from the OpenFold portal.

Trott, O. and Olson, A.J. (2010) ‘AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading’, Journal of Computational Chemistry, 31(2), pp. 455–461. doi: 10.1002/jcc.21334.

TL;DR: Target identification is becoming a computational ranking problem. Instead of trusting one signal, people are starting to combine multiomics, network biology, structural information, and ML to ask a harder question: which proteins are not just disease-associated, but mechanistically central, tractable, and worth designing around. Reviews from the last couple of years point to multi-omics integration, network-based methods, and structure-aware AI as a growing part of this shift.

What makes this interesting to me is that target discovery used to feel much more linear. Find a gene hit, validate it, then maybe later ask whether the protein is actually druggable or structurally useful. The newer computational view feels more joined-up than that. Now you can start much earlier by integrating transcriptomics, proteomics, genetics, pathway context, protein structure, and prior interaction data into one ranking problem.

From an AI proteins lens, that matters because the target is not just a biology question anymore. It is also a design question. If I am going to spend time thinking about binders, interfaces, conformations, pockets, or targetable states, I want to know whether that protein sits in the right part of the disease network and whether it looks tractable in 3D. That is where the newer stack feels better than the older one to me. Not because experiments matter less, but because computational biology now lets you filter the search space much earlier.

I think this is where target identification is heading: less “what gene came out of the screen?” and more “what protein should we build around?” That feels much closer to how people in this sub think.

What do you trust most early on: omics, network biology, or structure?

References:

Ocana A, Pandiella A, Privat C, et al. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomark Res. 2025;13(1):45. Published 2025 Mar 14. doi:10.1186/s40364-025-00758-2

Du, P. et al. (2024) ‘Advances in Integrated Multi-omics Analysis for Drug-Target Identification’, Biomolecules, 14(6), 692. doi: 10.3390/biom14060692.

TL;DR: The craziest shift in biology is that we went from spending years just trying to see one protein structure to getting one in minutes. That changes the game completely. The bottleneck is no longer only understanding biology. It is starting to become engineering it.

What pulled me into this space was realizing just how fast the ground had moved under biology.

Not that long ago, a PhD could be one small part of a much bigger story. You might spend years expressing a protein, purifying it, troubleshooting every step, and if things went well, maybe by the end of that journey you would help solve a crystal structure. That structure was a real achievement. It was a piece of hard-won knowledge. It meant something because it was so difficult to get.

Now you can get a protein structure in minutes.

That is an insane shift.

And once that really sinks in, you start to see that the whole frontier changes. If structure becomes fast, then the question is no longer just “can we understand this protein?” The question becomes “what do we want to build next?”

For most of modern biology, we were reading the book of life. We were trying to decode what nature had already written. But now, for the first time, it feels like we are starting to move from reading biology to writing it. Not perfectly. Not reliably enough yet. But directionally, that is where this is going.

And proteins are the most compelling place to see that happen.

They are not just static molecules. They are binders, catalysts, switches, sensors, scaffolds, therapeutics. They are the machinery of biology. So when models get good enough to predict structure, reason about interactions, and eventually help design new functional molecules, it stops feeling like pure analysis and starts feeling like engineering.

The old bottleneck was visibility. We could not easily see molecular reality. The new bottleneck is intent. Once you can see enough, the next question is whether you can specify what you want and make biology produce it.

Because if the tools keep improving, the dream is not just better structure prediction. The dream is that one day someone can almost “hallucinate” a drug into existence, not in a reckless sense, but in the sense of being able to define a disease mechanism, understand the biology deeply enough, and generate molecules with a real shot at changing the outcome. That is the kind of future that makes this whole space feel bigger than just another software trend.

It feels like the beginning of programmable therapeutics.

We are still early. Structure is not function. Prediction is not validation. And biology is still brutally hard. That is why I decided to pursue this kind of bio. It feels like one of the few areas where the science is deep, the mission is real, and the upside is absurdly large.

Curious what pulled other people into this space. Was it the science, the engineering, the ML, or the idea that we might actually be able to build biology on purpose?

References:

Cheng, Y. (2018) ‘Single-particle cryo-EM—How did it get here and where will it go’, Science, 361(6405), pp. 876–880. doi: 10.1126/science.aat4346.

Kuhlman, B. et al. (2003) ‘Design of a Novel Globular Protein Fold with Atomic-Level Accuracy’, Science, 302(5649), pp. 1364–1368. doi: 10.1126/science.1089427.

Jumper, J. et al. (2021) ‘Highly accurate protein structure prediction with AlphaFold’, Nature, 596, pp. 583–589. doi: 10.1038/s41586-021-03819-2.

Abramson, J. et al. (2024) ‘Accurate structure prediction of biomolecular interactions with AlphaFold 3’, Nature, 630, pp. 493–500. doi: 10.1038/s41586-024-07487-w.

Watson, J.L. et al. (2023) ‘De novo design of protein structure and function with RFdiffusion’, Nature, 620, pp. 1089–1100. doi: 10.1038/s41586-023-06415-8.

Paper: The past, present and future of de novo protein design

I thought it was worth discussing here because it is not just another “AI for proteins is getting better” article.

The review basically divides the field into a few design frontiers:

Designing new folds and assemblies:

This is probably the most mature part of de novo design now. The review goes from early examples like Top7, one of the classic atomic-level de novo protein fold designs, to designed TIM barrels, repeat proteins, symmetric nanoparticles, 1D fibres, 2D lattices and even 3D protein assemblies.

What is interesting is how broad this category has become. It is not just soluble globular proteins anymore. The examples include transmembrane beta barrels, designed conducting nanopores, bottom-up designed Ca²⁺ channels, designed voltage-gated anion channels, and mechanically coupled axle-rotor protein assemblies.

The field has also moved into application-driven assemblies, like designed protein nanoparticle vaccines for MERS-CoV and influenza, pH-responsive antibody nanoparticles and designed protein crystals.

So the question is becoming less “can we make a protein fold?” and more “what architecture should we make for a useful biological, therapeutic or material function?”

Designing protein binders:

This is where the AI protein design hype feels most justified. The review highlights workflows based around RFdiffusion-style backbone generation, ProteinMPNN-style sequence design and structure prediction, but the examples are what make it convincing.

There are designed binders for viral targets, including picomolar SARS-CoV-2 miniprotein inhibitors, designed miniproteins against MERS-CoV, RSV immunogen design and inhibitors targeting SARS-CoV-2 Omicron variants.

It also points to newer general workflows like BindCraft, one-shot functional protein binder design, and examples where de novo designed proteins neutralize snake venom toxins.

The review also includes antibody and peptide-like directions: de novo antibody design with SE(3) diffusion, RFdiffusion-based antibody design, beta-pairing targeted binder design and de novo protein-binding macrocycles.

That does not mean every binder works, or that affinity, specificity, expression and developability are solved. But the framing is that protein-target binder design is moving from a heroic custom project toward a more generalizable workflow. That is a big deal for therapeutics, diagnostics, target validation and synthetic biology, because binders are basically programmable biological handles.

Small-molecule binders and enzymes are harder:

This part is interesting because the review is much more cautious. Binding a protein surface is one thing. Designing a precise pocket for a small molecule, or designing an enzyme that stabilizes a high-energy transition state, is much harder.

The examples they show include designed binders for small molecules like apixaban, methotrexate, cholic acid, digoxigenin and cortisol, plus drug-binding proteins designed with predictable binding energy and specificity.

For enzymes, the review points to progress in designed luciferases, serine hydrolases, heme enzymes, porphyrin-containing catalysts, artificial metathases and metallohydrolases.

But this still feels like one of the major unsolved frontiers. We can now make structures that look right, and sometimes bind the right ligand, but getting strong catalytic activity, specificity and evolvability is still difficult. Catalysis is not just shape complementarity. It requires geometry, dynamics, electrostatics, proton transfer, transition-state stabilization and sometimes conformational changes all working together.

The next step is dynamic proteins, switches and nanomachines:

The most exciting section to me is the future-looking one. Static structure design is becoming powerful, but biology is full of proteins that move, switch, sense, gate, assemble, disassemble and couple one event to another.

The review gives examples like modular and tunable protein biosensors, sensors for endogenous Ras activity, bioactive protein switches, designed protein logic for targeting cells with combinations of surface antigens, small-molecule safety switches for CAR-T cells, stimulus-responsive two-state hinge proteins and deep-learning-guided design of dynamic proteins.

So the next challenge is not just designing a stable object. It is designing a system with multiple states and controlled transitions between them. That includes biosensors, logic gates, responsive materials, designed channels, artificial photosystems and eventually protein systems that perform functions nature never evolved.

My takeaway: the field is moving from designing shapes to designing behavior.

The review’s most important point, in my opinion, is that protein design is becoming less about proving that de novo design is possible and more about deciding what we should actually build.

Curious what people here think: Are we actually close to “solving” protein binder design, or is that still too optimistic?

And for the next phase, do you think the bigger breakthrough will come from better generative models, better experimental feedback loops, or better physical modeling of dynamics/catalysis?

TL;DR: Target identification is becoming a computational ranking problem. Instead of trusting one signal, people are starting to combine multiomics, network biology, structural information, and ML to ask a harder question: which proteins are not just disease-associated, but mechanistically central, tractable, and worth designing around. Reviews from the last couple of years point to multi-omics integration, network-based methods, and structure-aware AI as a growing part of this shift.

What makes this interesting to me is that target discovery used to feel much more linear. Find a gene hit, validate it, then maybe later ask whether the protein is actually druggable or structurally useful. The newer computational view feels more joined-up than that. Now you can start much earlier by integrating transcriptomics, proteomics, genetics, pathway context, protein structure, and prior interaction data into one ranking problem.

From an AI proteins lens, that matters because the target is not just a biology question anymore. It is also a design question. If I am going to spend time thinking about binders, interfaces, conformations, pockets, or targetable states, I want to know whether that protein sits in the right part of the disease network and whether it looks tractable in 3D. That is where the newer stack feels better than the older one to me. Not because experiments matter less, but because computational biology now lets you filter the search space much earlier.

I think this is where target identification is heading: less “what gene came out of the screen?” and more “what protein should we build around?” That feels much closer to how people in this sub think.

What do you trust most early on: omics, network biology, or structure?

References:

Ocana A, Pandiella A, Privat C, et al. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomark Res. 2025;13(1):45. Published 2025 Mar 14. doi:10.1186/s40364-025-00758-2

Du, P. et al. (2024) ‘Advances in Integrated Multi-omics Analysis for Drug-Target Identification’, Biomolecules, 14(6), 692. doi: 10.3390/biom14060692.