u/XpertAI

Paper: PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors

This matters because virtual screening often depends heavily on which protein structure is used. A ligand-bound holo structure, an apo structure, a homology model, or an AlphaFold-predicted model can all give different screening results.

PL-PatchSurfer3 tackles this by comparing local surface patches between the ligand and receptor pocket using 3D Zernike descriptors. The new version adds improved hydrogen-bond complementarity and a visibility feature that captures local curvature. The authors report that this improves performance while keeping the method robust across holo, apo, modeled, and AlphaFold-predicted receptor structures.

For AI protein workflows, the key point is practical: predicted structures are increasingly used in drug discovery, but they are not always in the right binding conformation. Methods like PL-PatchSurfer3 may help make virtual screening more reliable when starting from imperfect or AI-predicted protein models.

Paper: RareFold: Structure prediction and design of proteins with noncanonical amino acids

Repo: RareFold

Most protein design models are still built around the 20 natural amino acids. RareFold pushes beyond that limit by supporting 49 amino acid types in total, including 29 rare/noncanonical residues. The key finding is that these expanded chemical building blocks can be handled directly by the model, opening the door to protein and peptide designs with more chemical diversity.

The authors also introduce EvoBindRare, a binder design framework that can generate both linear and cyclic peptide binders from a target protein sequence, without needing a predefined binding site. According to the project page, the designs were experimentally validated for both linear and cyclic binders.

This could be important for AI protein design because noncanonical amino acids can add properties that natural residues often lack, including improved stability, altered binding chemistry, and new therapeutic possibilities.

Credit: u/Acceptable-Cover706

Paper: ODesign: A World Model for Biomolecular Interaction Design
Repo: ODesign

Wanted to discuss ODesign, especially in the context of models like BoltzGen, and RFdiffusion3.

The key distinction is that ODesign is closer to an “all-to-all” biomolecular design model, while BoltzGen is more like a universal protein/peptide binder design model. ODesign tries to design across multiple molecular modalities:

• proteins
• peptides
• DNA/RNA
• small molecules
• multimolecular complexes

BoltzGen, by contrast, mainly designs protein-like binders: miniproteins, peptides, cyclic peptides, nanobodies, and antibody-like binders, against many target types.

So the difference is roughly:

BoltzGen:
“Given a biomolecular target, design a protein/peptide binder.”

ODesign:
“Given a biomolecular target/interface, design the appropriate molecular partner, potentially protein, nucleic acid, or ligand.”

That makes ODesign broader in ambition, but BoltzGen currently looks stronger on experimental validation. BoltzGen reports validation across nanobodies, miniproteins, peptides, cyclic peptides, and challenging target classes, while ODesign’s wet-lab validation appears mainly focused on protein minibinders so far, with other modality validation still pending.

Technically, ODesign is interesting because it builds on an AlphaFold3-like structure-prediction backbone. It uses unified generative tokens for different chemical modalities, then performs conditional all-atom diffusion to generate coordinates. After that, an inverse-folding/type-design module assigns amino acids, nucleotides, or ligand atom types depending on the modality. The clever part is the masking system. ODesign can mask at different levels:

• whole molecule/entity level
• residue/token level
• atom/motif level

That lets it handle tasks like binder design, motif scaffolding, ligand-binding protein design, aptamer-like design, and ligand generation in one framework.

Compared with other models:

RFdiffusion3 is probably the closest “serious” competitor from the protein-design side. It is all-atom and can design proteins in the context of ligands, DNA/RNA, and other molecules, but it is still mostly about generating proteins, not freely switching between protein, nucleic acid, and ligand outputs.

I think, BoltzGen feels closer to a practical wet-lab binder design tool today.
ODesign feels like the broader future direction: a unified model for programmable molecular interaction design across modalities.

The big question is whether ODesign’s cross-modality promise will translate experimentally beyond protein minibinders. If it can actually produce validated RNA/DNA binders, ligand designs, and non-protein interaction partners, that would be a major step beyond current protein-centric design workflows.

Curious what people think: are these “world models” actually becoming useful design engines, or are we still mostly benchmarking pretty structures until the wet-lab hit rates catch up?

Paper: PXDesign: Fast, Modular, and Accurate De Novo Design of Protein Binders
Repo: PXDesign

I know some people may be skeptical because PXDesign comes from ByteDance Seed, but for general de novo protein binder design, this is honestly one of the more impressive and practical papers I’ve seen, while researching de novo design.

To be clear, I’m not talking about antibody-specific design or niche antibody engineering tasks. I mean general protein binder generation against protein targets like the red protein in the image above..

The main claim is strong: PXDesign reports 20–73% nanomolar binder hit rates across five of six tested targets, with wet-lab validation on IL-7RA, SARS-CoV-2 RBD, PD-L1, TrkA, VEGF-A, and TNF-α. It combines two parts:

1. PXDesign-d, a diffusion-based generator for making candidate binders.
2. PXDesign-h, a hallucination/optimization-based approach using Protenix-style structure prediction.

The diffusion model seems to be the real workhorse. It is fast, generates structurally diverse binders, and appears better suited for large-scale exploratory campaigns than slower hallucination methods. They also put a lot of effort into filtering and ranking, comparing AF2-style filters with Protenix-based filters, and showing that Protenix often improves enrichment and ranking.

What I like most is that this is not just another “we generated nice-looking structures” paper. They actually test designs experimentally, report hit rates, compare against methods like AlphaProteo, RFDiffusion, Chai, and Latent-X, and release a benchmarking framework.

The important caveat is that this is not peer-reviewed. Also, TNF-α failed, and the authors are pretty open about limitations in filtering thresholds, dataset sparsity, and experimental throughput.

But overall, for de novo protein binder design, PXDesign looks strong. I would not treat it as a universal solution, and I would not use it as an antibody design tool, but for general binder generation it seems very reliable and worth paying attention to.

Repo: AFSample2
Paper: Improving AlphaFold2 Performance in Virtual Screens Targeting GPCRs by Enhancing Binding-Site Conformational Sampling

The AFSample2T paper is finally out, and it is a cool example of where AI protein modelling is heading.

Vanilla AlphaFold2 is great at predicting a likely protein structure, but it usually collapses toward one dominant conformation. For GPCR drug discovery, that can be limiting because the binding pocket is flexible, and small local changes can strongly affect docking and virtual screening results.

AFSample2T tackles this by using targeted MSA masking around the binding site. Instead of perturbing the whole protein, it selectively weakens the evolutionary signal near the pocket, pushing AF2 to sample more ligand-compatible GPCR conformations.

So the difference is basically:

Vanilla AF2: predicts one likely structure.
AFSample2T: samples alternative binding-site conformations for better virtual screening.

That matters because drug discovery needs more than static structures. It needs useful receptor states.

Blog: Fully open source reasoning model (8b and 70b) trained using GRPO towards a physics based reward function for protein stability.

Repo: Pro1

I feel like it is pretty underrated, especially because it takes a different approach from a lot of protein design models.

Most tools in this space are either sequence-only, structure-prediction-first, or diffusion-based. Pro-1 is interesting because it uses an LLM-style reasoning loop with a physics-based reward signal, mainly around Rosetta stability scoring.

We have been testing it for antibody design, and the useful part is that it can reason over the sequence, structural context, prior mutations, and design goals before suggesting changes. That makes it feel less like a black-box generator and more like a guided protein engineering assistant.

The downside is that it is heavy computationally, especially if you want to run the full loop properly with structure prediction/scoring. It is not a lightweight “generate 100 sequences instantly” kind of model.

Still, I think the direction is underrated: using language models to reason through protein engineering decisions, then grounding them with physics-based scoring.

Curious if anyone else has tested it, especially for antibodies or enzyme stability.