Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute

Kieran Didi; Zuobai Zhang; Guoqing Zhou; Danny Reidenbach; Zhonglin Cao; Sooyoung Cha; Tomas Geffner; Christian Dallago

cs.LGMar 30, 2026

Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute

Authors: Kieran Didi, Zuobai Zhang, Guoqing Zhou, Danny Reidenbach, Zhonglin Cao, Sooyoung Cha, Tomas Geffner, Christian Dallago, +6 more

Organizations: NVIDIA · University of Oxford · Mila - Québec AI Institute · Université de Montréal · School of Biological Sciences, Seoul National University · Interdisciplinary Program in Bioinformatics, Seoul National University · HEC Montréal · CIFAR AI Chair · AITHYRA · Institute of Molecular Biology and Genetics, Seoul National University · Artificial Intelligence Institute, Seoul National University

Abstract

Protein interaction modeling is central to protein design, which has been transformed by machine learning with applications in drug discovery and beyond. In this landscape, structure-based de novo binder design is cast as either conditional generative modeling or sequence optimization via structure predictors ("hallucination"). We argue that this is a false dichotomy and propose Proteina-Complexa, a novel fully atomistic binder generation method unifying both paradigms. We extend recent flow-based latent protein generation architectures and leverage the domain-domain interactions of monomeric computationally predicted protein structures to construct Teddymer, a new large-scale dataset of synthetic binder-target pairs for pretraining. Combined with high-quality experimental multimers, this enables training a strong base model. We then perform inference-time optimization with this generative prior, unifying the strengths of previously distinct generative and hallucination methods. Proteina-Complexa sets a new state of the art in computational binder design benchmarks: it delivers markedly higher in-silico success rates than existing generative approaches, and our novel test-time optimization strategies greatly outperform previous hallucination methods under normalized compute budgets. We also demonstrate interface hydrogen bond optimization, fold class-guided binder generation, and extensions to small molecule targets and enzyme design tasks, again surpassing prior methods. Code, models and new data will be publicly released.

Figures & tables

Figure 1: (Top) Proteína-Complexa’s target-conditioned generation process. (Bottom) Scaling test-time compute, we use Complexa’s generative prior for more efficient optimization than previous hallucination methods ( Sec. 3.4 ). We depict beam search, which steers stochastic generation toward high-quality binders, guided by structure prediction models’ interface scores or hydrogen bond energies. Intermediate candidate states (blue) are scored via rollouts (blue, dotted), promising candidates are kept, and new trajectories are launched (orange).

Figure 2: Binders generated by Complexa, passing in-silico success criteria (more visualizations in Appendix K ). (a) TNF- $\alpha$ three-chain target. (b) Claudin-1 target, red interface hydrogen bonds. (c) OQO small molecule target.

Figure 3: Teddymer dimers resemble realistic binder-target structures, including interface hydrogen bonding (zoom-in). Also see Appendix C .

Figure 4: Filtered training datasets used by Complexa.

Figure 5: Complexa’s latent target conditioning . When training the conditional denoiser, the encoder and decoder are frozen.

Figure 6: Fold class-conditional binder generation. Samples pass success criteria. IFNAR2 target.

Model	# Unique Successes $\,\uparrow$				Times [s] $\downarrow$	Novelty $\downarrow$
	SAM	OQO	FAD	IAI
RFDiffusion-AllAtom	2	3	5	8	87.4	0.72
Complexa (ours)	10	6	17	19	13.5	0.71

Table 1: Complexa’s generative performance for small molecule targets without optimization. RFDiffusion-AllAtom uses LigandMPNN, we evaluate sequences produced by Complexa.

Model	# Unique Successes $\,\uparrow$			# Times Best Method $\,\uparrow$			Time [s] $\,\downarrow$	Novelty $\,\downarrow$
	Self	MPNN-FI	MPNN	Self	MPNN-FI	MPNN
RFDiffusion ( Watson et al., 2023 )	–	–	4.68	–	–	3	70.8	0.87
Protpardelle-1c ( Lu et al., 2025 )	–	–	0.73	–	–	0	8.13	0.77
APM ( Chen et al., 2025 )	0.31	1.52	3.15	1	0	1	73.1	0.86
Complexa (ours)	9.10	13.6	14.4	14	14	14	15.6	0.80

Table 2: Complexa’s generative performance for protein targets without optimization vs. baselines. Self denotes model sequence evaluation, MPNN full backbone-based re-design, and MPNN-FI the same with fixed interface amino acids. Note that RFDiffusion and Protpardelle only generate backbones and not their own sequences. Complete results in Appendix I . If methods tie on unique and absolute successes, we do not count this, see Appendix F .

Figure 7: Scaling analysis of inference-time optimization across different target difficulties. Average unique success rate on 12 easy and 7 hard targets against test-time GPU hours. Five inference-time optimization algorithms from Sec. 3.4 are applied to Complexa and compared with hallucination baselines BoltzDesign and BindCraft.

Figure 8: Inference-time scaling for VEGFA multi-chain target (hard).

Figure 9: Inference-time optimization for small molecule targets.

Model	# Unique	# H-Bonds
	Successes $\,\uparrow$	(avg.) $\,\uparrow$
Complexa (no reward)	77.00	5.271
Complexa w/ $f_{\textrm{ipAE}}$	83.36	5.524
Complexa w/ $f_{\textrm{H-Bond}}$	82.36	7.154
Complexa w/ $f_{\textrm{ipAE}}$ + $f_{\textrm{H-Bond}}$	86.26	6.518

Table 3: Inference-time optimization using beam search with different combinations of folding and hydrogen bond rewards. Details in Sec. I.6 .

Figure 10: Enzyme Design Benchmark. Complexa significantly outperforms RFDiffusion2 in 38/41 AME benchmark tasks, both with re-designed and self-generated sequences. Extended results in Sec. I.9 .

Figure 11: (a) Interface hydrogen bond optimization can create binders with extended interacting regions, forming strong hydrogen bonding (red, zoom-in). (b) Regular binders, even though passing success criteria, can be smaller with less interactions. Visualization shows successful binders for TrkA target.

Appendix figures & tables33 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 12: Experimental vs. synthetic interfaces. Comparison between Teddymer and PDB multimer on various protein-protein interface metrics.

Target Name	PDB ID	Target Chain & Residues	Hotspot Residues	Binder Length	Source
BBF-14	9HAG	A1-112	–	70-250	BindCraft
Betv1	AF2 prediction	A1-159	A24	70-185	BindCraft
CbAgo (PAZ)	AF2 prediction	A180-276	A268	70-160	BindCraft
CD45	AF2 prediction	d1 domain	A1	80-200	BindCraft
Claudin1	AF2 prediction	A1-188	A31, A46, A55, A152	80-175	BindCraft
CrSAS6	AF2 prediction	A1-145	–	90-200	BindCraft

Appendix

Table 4: Selected protein targets with their structural information, binding specifications, and data source.

Target Name	PDB ID	Target Chain	Binder Length
SAM	A	A	100
FAD	B	A	100
IAI	C	A	100
OQO	D	A	100

Appendix

Table 5: Selected small molecule targets with their structural information, binding specifications, and data source.

p(c)=\frac{\exp\{\beta\,R(c)\}}{\sum_{c^{\prime}\in\mathcal{C}}\exp\{\beta\,R(c^{\prime})\}}

Appendix

Algorithm 3 Protein Binder Feynman–Kac Steering with Reward Approximation

Model	# Unique Successes $\,\uparrow$			# Times Best Method $\,\uparrow$
	Self	MPNN-FI	MPNN	Self	MPNN-FI	MPNN
Complexa	9.10	13.5	14.4	19	13	14
Complexa w/o Teddymer	0.15	1.68	3.84	0	0	0
Complexa w/o Translation Noise	1.47	3.89	3.73	0	6	5

Appendix

Table 6: Translation noise and Teddymer ablation studies. average unique successes and the number of times each method ranks best across 19 targets. Results are shown for Complexa, and variants without Teddymer data and without translation noise.

Target	Complexa			Complexa w/o Teddymer			Complexa w/o Translation Noise
Target	MPNN	MPNN-FI	Self	MPNN	MPNN-FI	Self	MPNN	MPNN-FI	Self
PD-L1	16	14	9	8	4	0	1	4	1
BHRF1	26	29	21	18	7	2	28	28	14
IFNAR2	51	52	39	5	2	0	4	2	0
BBF14	24	25	20	12	10	0	4	4	0
DerF7	24	16	10	7	0	0	1	2	2

Appendix

Table 7: Translation noise and Teddymer ablation studies (detailed results). Unique successes across 19 targets for Complexa and its variants without Teddymer data or translation noise, evaluated with different sequence redesign methods. Results are visualized in Fig. 13 .

Figure 13: Translation noise and Teddymer ablation studies. Unique successes of different Complexa variants (base, without Teddymer data, and without translation noise) on easy targets (left of the dashed line) and hard targets (right of the dashed line).

Figure 14: Teddymer ablation study with Boltz-2. Boltz-2-based evaluation of the unique successes of different Complexa variants (trained with vs. without Teddymer data) on easy targets (left of the dashed line) and hard targets (right of the dashed line).

Model	# Unique Successes $\,\uparrow$
	MPNN	MPNN-FI	Self
Complexa	34.4	31.4	14.8
Complexa w/o Teddymer	16.2	13.1	2.2

Appendix

Table 8: Teddymer ablation study with Boltz-2. Boltz-2-based evaluation of the average unique successes across 19 targets. Results are shown for Complexa and the variant trained without Teddymer data.

Figure 15: Teddymer ablation study with RF3. RF3-based evaluation of the unique successes of different Complexa variants (trained with vs. without Teddymer data) on easy targets (left of the dashed line) and hard targets (right of the dashed line).

Model	# Unique Successes $\,\uparrow$
	MPNN	MPNN-FI	Self
Complexa	4.9	4.3	2.4
Complexa w/o Teddymer	1.2	0.5	0.1

Appendix

Table 9: Teddymer ablation study with RF3. RF3-based evaluation of the average unique successes across 19 targets. Results are shown for Complexa and the variant trained without Teddymer data.

Figure 16: Generate & Hallucinate ablation study. Numbers of unique successes against inference-time GPU hours, for Generate & Hallucinate, its variant with logit optimization, and BindCraft, averaged over 12 easy and 7 hard targets.

Figure 17: Generate & Hallucinate ablation study (easy targets). Number of unique successes over 12 easy targets against inference-time GPU hours, for Generate & Hallucinate, its variant with logit optimization, and BindCraft. Successes are measured with self-generated sequences and inference-time compute includes both generation and evaluation time.

Figure 18: Generate & Hallucinate ablation study (hard targets). Number of unique successes over 7 hard targets against inference-time GPU hours, for Generate & Hallucinate, its variant with logit optimization, and BindCraft. Successes are measured with self-generated sequences and inference-time compute includes both generation and evaluation time.

Figure 19: H1, TNF-

\alpha

, and IL17A targets. Inference-time optimization compute scaling plots for three very hard targets. See Sec. I.4 for details.

Target	RFDiffusion	Protpardelle-1c	APM			Complexa (Ours)
Target	MPNN	MPNN	MPNN	MPNN-FI	Self	MPNN	MPNN-FI	Self
PD-L1	8	2	1	0	0	16	14	9
BHRF1	5	4	20	10	5	26	29	21
IFNAR2	11	2	3	3	1	51	52	39
BBF14	2	0	0	0	0	24	25	20
DerF7	5	0	0	0	0	24	16	10

Appendix

Table 10: De novo binder design performance for all protein targets . Unique successes for different models and three sequence redesign methods per target.

Target	Model	# Unique Successes $\,\uparrow$
		Self	MPNN-FI	MPNN
SAM	RFdiffusionAA	-	-	2
SAM	Complexa	10	8	15
OQO	RFdiffusionAA	-	-	3
OQO	Complexa	6	12	9
FAD	RFdiffusionAA	-	-	5

Appendix

Table 11: De novo binder design performance of models for all small molecule targets . RFdiffusionAA only produces backbones and as a result can only be evaluated under full LigandMPNN re-design. We report the number of FoldSeek clusters out of the successful subset of 200 samples of length 100.

Figure 20: Unique successes for different models with three sequence redesign methods on easy targets (left of the dashed line) and hard targets (right of the dashed line). Detailed results are in Tab. 10

Figure 21: Unique successes against inference-time GPU hours on 12 easy targets. Successes are measured with self-generated sequences and inference-time compute includes both generation and evaluation time.

Figure 22: Unique successes against inference-time GPU hours on 7 hard targets. Successes are measured with self-generated sequences and inference-time compute includes both generation and evaluation time.

Figure 23: Unique successes against inference-time GPU hours on 4 small molecule targets. Successes are measured with self-generated sequences and inference-time compute includes both generation and evaluation time.

Figure 24: Unique successes and number of interface hydrogen bonds on 19 targets using Beam Search with different combinations of folding and hydrogen bond rewards. Successes are measured with self-generated sequences and hydrogen bond counts are computed on refolded structures over successful designs.

AME Task	# residue islands	RFDiffusion2 LigandMPNN(8)		RFDiffusion2 LigandMPNN(1)		Complexa (ours) LigandMPNN(8)		Complexa (ours) LigandMPNN(1)		Complexa (ours) Self-Generated
		All	Unique	All	Unique	All	Unique	All	Unique	All	Unique
M0630	1	78	37	55	22	88	38	76	32	70	27
M0663	1	1	1	0	0	62	38	36	26	31	24
M0710	2	72	42	39	22	91	40	67	34	64	31
M0717	2	55	28	27	12	65	26	39	16	28	11
M0664	2	72	38	36	19	88	49	70	47	60	42

Appendix

Table 12: Enzyme Design Benchmark—Detailed Quantitative Results. “All” indicates total number of successes produced by the model (we produce 100 samples per task), while “Unique” indicates number of unique successes, obtained by clustering all successes. The method with the most unique successes for 8 sequence re-designs (LigandMPNN(8)) is highlighted in blue and for single sequence methods is highlighted in red.

Figure 25: Enzyme Design Benchmark—Best-of-8 Re-designs. Complexa significantly outperforms RFDiffusion2 in 38/41 AME enzyme design benchmark tasks. Both methods use best-of-8 re-designed sequences.

Figure 26: Enzyme Design Benchmark—Aggregated Results. Complexa significantly outperforms RFDiffusion2 across all sequence modes.

Figure 27: Extensive Search Case Study. Number of unique successes on the target SpCas9 against inference-time GPU hours with different TM-score clustering thresholds used in the unique success calculation (green:

0.4

, red:

0.45

and blue:

0.5

). Successes are measured with self-generated sequences and inference-time compute includes both generation and evaluation time.

Figure 28: Interface hydrogen bonding and interface predicted aligned error correlations. The number of interface hydrogen bonds increases with decreasing ipAE values of ColabDesign during inference-time search with negative ipAE as a reward ( Sec. I.11 ).

Target	Model	pK d $\,\uparrow$		$\#$ of Samples
		Mean $\pm$ Std	Best
SAM	RFDiffusionAA $\ddagger$ $\dagger$	5.65 $\pm$ 0.15	5.76	2
	Complexa (ours)	4.81 $\pm$ 0.49	5.49	10
	Complexa (ours) $\dagger$	4.94 $\pm$ 0.54	5.74	10
OQO	RFDiffusionAA $\ddagger$ $\dagger$	7.01 $\pm$ 0.31	7.35	3
	Complexa (ours)	7.19 $\pm$ 0.14	7.37	6

Appendix

Table 13: Binding affinity comparison of unique small molecule binders. We report the average and best pK d in kcal/mol predicted by FLOWR-ROOT.

\ddagger

denotes redesign of the binder sequence using LigandMPNN.

\dagger

denotes co-folding with RosettaFold3, otherwise the model-generated structure is used. RFDiffusionAA requires both LigandMPNN redesign and co-folding as it generates a backbone without sequence.

Figure 29: Generated binder interfaces vs. PDB multimer inferfaces. Comparison of bioinformatic metrics between the PDB Multimer reference distribution and successful binders generated by Complexa.

Figure 30: Binders generated by Complexa for the challenging large multi-chain targets TNF- $\alpha$ (three-chain target), IL17A (two-chain target), and H1 (two-chain target; two different binders visualized). All shown binders meet the in-silico success criteria. Binders are visualized in cartoon representation, including side chains, multi-chain targets in surface representation. See Sec. I.4 .

Figure 31: Binders generated by Complexa for small molecule targets SAM, IAI, FAD and OQO (see Appendix E ). All shown binders meet the in-silico success criteria.

Figure 32: Fold class-conditioned binders generated by Complexa for five different targets. We control the secondary structure content through conditioning on C-level CAT labels “Mainly Alpha”, “Mainly Beta” or “Mixed Alpha Beta” ( Dawson et al., 2016 ; Geffner et al., 2025 ) . We can observe that the generated binders follow the conditioning, exhibiting primarily alpha helices, beta sheets, or both. All shown binders meet the in-silico success criteria. Binders are visualized in cartoon representation, including side chains, targets in surface representation.

Hyperparameters	Complexa
	Protein-Protein	Protein-Protein CAT	Ligand-Protein
Model Architecture
Partially Latent Flow Matching
sequence repr dim	768	768	768
sequence cond dim	256	256	256
$t$ sinusoidal enc dim	256	256	256

Appendix

Table 14: Hyperparameters for Complexa model training. We denote two versions of Complexa that specialized in generating binders for protein targets and small molecule ligand target as Protein-Protein and Ligand-Protein , respectively. Protein-Protein CAT refers to the model that can generate binders to protein target conditioned on CAT labels.

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Designing Protein Structures from Natural Language

Joseph Lee, Yanjing Li, Bo Qiang, Gregory Lau+6

University of Washington · National University of Singapore · Allen Institute for AI (Ai2)+1

Protein Design Generative Modeling Protein Binder Design

Open paper AskPDF arXiv

A central challenge in de novo protein design is generating proteins with desired structural and functional properties. Existing design models typically require a dedicated input representation for each property, limiting the design objectives they support. We develop Prompteina, the first all-atom structure generation model that supports new and broader design objectives specified in natural language, analogous to text-to-image generation. To train Prompteina, we construct Prompteina-Data, a paired text--protein structure dataset of 1.8M structures and 50M+ captions describing global and residue-specific structural and functional properties. We adapt Proteina-Complexa, a pretrained structure generation model, through text-conditioned pretraining and finetuning that aligns residues with their corresponding text. Prompteina outperforms baselines on 15 of 18 structural properties and generates proteins that more closely match descriptions of their intended functions. In binder design, it leads baselines on at least 13 of 20 targets and improves interface confidence by 14% over the strongest baseline. With iterative in silico feedback, Prompteina's gain over non-iterative generation is 57% larger than the next-best baseline on the hardest targets. Finally, we experimentally validate Prompteina designs against GM2A, a target with no reported de novo binders. We identify nine binders, with the strongest at an apparent

K_{\mathrm{D}}

of 107 nM. These results establish natural language as an effective and powerful conditioning interface for protein structure generation.

Abstract

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