PXDesign de novo binder design
PXDesign is the de novo binder design pipeline Ranomics runs on its own wet-lab campaigns. RFdiffusion-style backbone generation, ProteinMPNN sequence design, and JAX AF2 Initial Guess scoring. The same algorithm our team uses when a client pays us to deliver binders.
Run it self-serve, then hand the candidates to our team for wet-lab validation when you find designs worth testing.
Based on Bennett et al., Nature Communications 2023, the AF2 Initial Guess methodology.
Target structure to ranked binder candidates
Define hotspots
Upload a target PDB or AlphaFold model. Specify hotspot residues that the designed binder must contact, or pull them from Epitope Scout output.
Generate backbones
RFdiffusion-style diffusion produces hundreds to thousands of novel scaffolds geometrically complementary to the hotspot patch.
Design sequences
ProteinMPNN assigns sequences to each backbone, with fixed positions at predicted contact residues to preserve binding geometry.
Score with Initial Guess
JAX AF2 Initial Guess refolds each binder-target complex and filters on interface pLDDT, PAE, and ipTM. Ranked CSV plus PDBs delivered.
Four stages, one integrated run
PXDesign chains the four canonical stages of modern de novo binder design into one integrated run. Each stage passes its outputs directly to the next, with no manual handoff between tools.
Backbone generation
RFdiffusion-style hotspot-conditioned diffusion. Reverses a noise process to produce backbone coordinates that are geometrically complementary to the specified epitope. Scaffold length range and topology constraints are tunable per run.
ProteinMPNN sequences
Inverse-folding sequence design. Multiple sequences sampled per backbone at controllable temperature, with hotspot contact positions fixed to preserve the diffusion-defined binding geometry.
JAX AF2 Initial Guess
AlphaFold2 multimer refolds each binder-target complex using the designed structure as the initial coordinate guess. This is faster and more discriminative than de novo AF2 multimer prediction. The differentiation step from vanilla pipelines.
Interface filtering
Candidates are ranked on interface pLDDT, PAE at the predicted interface, and ipTM. Designs whose Initial Guess refold matches the original geometry rise to the top; designs that drift are discarded.
Ranked candidate set
CSV of scored designs plus per-candidate PDB structures. Top candidates are pre-formatted for direct handoff into a Ranomics wet-lab Binder Pilot or AI Binder Sprint.
Why PXDesign is not a hosted academic tool
Initial Guess vs. vanilla AF2
Default AF2 multimer predicts complexes from scratch using MSAs and templates, which is slow and noisy when the binder is a never-seen-before designed sequence. Initial Guess seeds AF2 with the designed structure and asks: does the model agree this complex is real? The result is faster scoring and a success metric that correlates with wet-lab binding much more tightly than default multimer scores.
Wet-lab feedback loop
Every campaign Ranomics runs sends real binding data back into how we tune PXDesign, covering filter thresholds, scaffold lengths, and hotspot weighting. The version you launch on tools.ranomics.com is the version we calibrated against actual yeast display screens, not the public release weights frozen at paper publication.
Scales to full campaigns
Self-serve PXDesign runs on the same infrastructure we use internally. Hundreds of candidates per run. Need 10,000 to 50,000 designs across a full campaign? The same pipeline scales up inside an AI Binder Sprint with no rewrite and no re-validation.
The algorithm we trust on paid campaigns
PXDesign exists because we needed a binder design pipeline we could defend at a wet-lab handoff, not a stitched-together set of GitHub repos with different conda environments and inconsistent scoring conventions. The Initial Guess scoring step is the load-bearing piece. It is what makes us willing to put a 100% binder guarantee on the AI Binder Sprint.
Run it self-serve when you want the same algorithm Ranomics uses internally, with results in a format that drops straight into a wet-lab campaign if the candidates are worth testing.
You want the same de novo binder algorithm Ranomics runs on its own paid wet-lab campaigns
Comparing AF2 Initial Guess scoring against your existing RFdiffusion plus default AF2 multimer pipeline
Need ranked binder candidates against a target you already have a PDB or AlphaFold model for
Planning a Binder Pilot or AI Binder Sprint and want to scope the design space before paying for wet-lab validation
Running an internal feasibility study before committing budget to a multi-week campaign
Tuning hotspot residue selection from Epitope Scout output and want to see which patches generate scoreable designs
From ranked candidates to validated binders
PXDesign gives you scored, ranked binder candidates. The next step is putting the top hits on cells and seeing which ones actually bind. Two entry points depending on scope, both running the same PXDesign pipeline on the wet-lab side as you just ran self-serve.
Validate your PXDesign candidates in cells
The Binder Pilot takes your top PXDesign hits, expresses them on yeast display, and sorts for target binding. Single-round, ranked hits, technical report. Scoped for academic labs, seed biotech, industrial SMBs, and student research groups.
See the Binder Pilot → Flagship programRun PXDesign at full campaign scale
The AI Binder Sprint runs PXDesign alongside RFdiffusion, BindCraft, and BoltzGen at 10,000 to 50,000 designs over 6-8 weeks, with milestone check-ins and a 100% binder guarantee. For teams building a binder pipeline on a hard deadline.
See the AI Binder Sprint →Run the algorithm we run on our own campaigns
Sign in, upload a target structure, define hotspots. Get ranked binder candidates scored with JAX AF2 Initial Guess.
Algorithm based on Bennett, J. et al. Improving de novo protein binder design with deep learning. Nature Communications 14, 2625 (2023). See all Ranomics technology.