Fast Proteome-Scale Protein Interaction Retrieval via Residue-Level Factorization
Authors: Jianan Zhao
Zhihao Zhan
Narendra Chaudhary
Xinyu Yuan
Zuobai Zhang
Qian Cong
Jian Zhou
Sanchit Misra
Jian Tang
Venue: ICLR 2026 Poster (2026)
Keywords: Protein–protein interaction
Kernel methods
Random Fourier features
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
TL;DR: We propose RaftPPI, which enables residue-aware PPI retrieval at proteome scale, reducing screening from GPU months to minutes while improving accuracy.

Abstract: Protein-protein interactions (PPIs) are mediated at the residue level. Most sequence-based PPI models consider residue-residue interactions across two proteins, which can yield accurate interaction scores but are too slow to scale. At proteome scale, identifying candidate PPIs requires evaluating nearly *all possible protein pairs*. For $N$ proteins of average length $L$, exhaustive all-against-all search requires $\mathcal{O}(N^2L^2)$ computation, rendering conventional approaches computationally impractical. We introduce RaftPPI, a scalable framework that approximates residue-level PPI modeling while enabling efficient large-scale retrieval. RaftPPI represents residue interactions with a Gaussian kernel, approximated efficiently via structured random Fourier features, and applies a low-rank factorized attention mechanism that admits pooling into a compact embedding per protein. Each protein is encoded once into an indexable embedding, allowing approximate nearest-neighbor search to replace exhaustive pairwise scoring, reducing proteome-wide retrieval from *months* to *minutes* on a single GPU or CPU. On the human proteome with the D-SCRIPT dataset, RaftPPI retrieves the top 20% pairs from $\sim$200M candidate pairs in 5.7 minutes on an A100 GPU, or 3.3 minutes on an Intel Xeon 6980P CPU, covering 75.1% of the true interacting pairs, compared to 4.9 GPU months for the best prior method (61.2%). Across seven benchmarks with sequence- and degree-controlled splits, RaftPPI achieves state-of-the-art PPI classification and retrieval performance, while enabling residue-aware, retrieval-friendly screening at proteome scale.

Published as a conference paper at ICLR 2026
FAST PROTEOME-SCALE PROTEIN INTERACTION RETRIEVAL VIA RESIDUE-LEVEL FACTORIZATION
Jianan Zhao Zhihao Zhan Narendra Chaudhary?, Xinyu Yuan 12 Zuobai Zhang!
Qian Cong', Jian Zhou', Sanchit Misra', Tang
I 'Mila- Québec AI Institute, Universitté de Montréal, 'Intel Corporation
UUniversity of Texas Southwestern Medical Center, 'HEC Montréal, $CIFAR AI Chair
ABSTRACT
Protein-proteins interactions (PPIs) are mediated the residue level. Most sequence- based PPI models consider residue-residue interactions across two proteins, which can yield accurate interaction scores but are too slow scale. proteome scale, identifying candidate PPIs requires evaluating nearly all possible protein pairs, For proteins of average length L, exhaustive all-against-all search requires ( computation, rendering conventional approaches computationally impractical. We introduce RaftPPI, a scalable framework that approximates residuc-level PPI mod- eling while enabling efficient large-scale retrieval. RaftPPI represents residue interactions with a Gaussian kernel, approximated efficiently via structured random Fourier features, and applies low-rank factorized attention mechanism that admits pooling into a compact embedding per protein. Each protein is encoded once into indexable embedding, allowing approximate nearest-neighbor search replace exhaustive pairwise scoring, reducing proteome-wide retrieval from months to min- utes on single GPU or CPU. On the human proteome with the D-SCRIPT dataset, RaftPI retrieves the top 20% pairs from 200M candidate pairs in 5.7 minutes on an A 1000 GPU, or 3.3 minutes on Intel Xeon 6980P CPU, covering 75. 1 % of the true interacting pairs, compared to 4.9 GPU months for the best prior method (61.2%). Across seven benchmarks with sequence- and degree-controlle splits, RaftPPI achieves state-of-the-ar PPI classification and retrieval performance, while enabling rasie, retrieval- -rrendlllllllyyyy screening at proteome scale.
INTRODUCTION
Protein Protein-protein interaction (PPI) is central to understanding cellular mechanisms and enabling applications target discovery (Loscalzo) 2023), pathway reconstruction (Ritz et al. 2016), and functional annotation (Sharan et al. 2007). In practice, many discovery tasks require proteome-scale screening, scoring protein pairs within a species to surface plausible interactors (Humphreys et al.) 2024 Zhang et al. 2024). However, proteome-scale PPI prediction remains time-conssie coneeconnuming due to the quadratic number of candidate protein pairs. One high-accuracy route is to predict multimer structures with end-to-end structure predictors (Jumper al. 2021 2024 Evans et al. 2021 While accurate, these pipelines rely on MSAs/templates and require ( complexity to update pairwise representations of proteins with length L. This daunting cost makes per-complex structure prediction difficult to amortize across large candidate sets. An alternative frames PPI as binary classification from alignment-free PLM embeddings (Sledzieski al. 2024 Ko et al. 2024, Liu al. 2024). Although per-sequence encoding is O(L)), these models must jointly encode each protein pair inference; thus an exhaustive screen over the human proteome with 20,000 proteins entails Ro 2x 10 candidate pairs, making large-scale screening computationally prohibitive. To illustrate, the state-of-the-arr PPI classification model PI.M-Interact (Liu al. 2024) requires 148.47 A100 GPU-days (s44.9 months) to screen the human proteome (see Table and 4.3
In light of these limitations, we propose Residue-interaciion Approximation with Fourier Feal FeaTures (RaftPPI), which models PPI by approximating residue-level interactions while enabling scalable protein retrieval. RaftPPI models residue- residue scores with a Gaussian kernel and aggregates them
Corrrsponning author. Code release: http://///tttttb.om///////////// P

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Published as a conference paper at ICLR 2026
to protein-level score (Fig. T). The non- con-linear kernel is efficiently approximated via random Fourier features (Rahimi and Recht 2007 Yu et al. 2016), and pooling uses a low-rank factorized attention that admits a linear-time time approximation at inference. As result, each protein is encoded once into fixed-length representation amenable to approximate nearest-neighbor search, e.g., Hierarchical navi- gable small world (HNSW; (Malkov and Yashunin 2020)), to retrieve likely interactors--rttiine retaining residue-level interactions while avoiding explicit per-pair computation. In practice, the dominant cost PLM encoding, giving overall (N L²) across proteome; on single A single Intel Xeon 6 CPU, retrieving the top 20% of human-proteome proteome pairs completes in minutes, achieving a -fld speedup over prior art.
Besides model design, we also seek to mitigate challenges from the lack of reliable negative data in PPI datasets. Experimentally confirmed non-interactions are rare, so negatives are typically constructed, and their quality varies widely (Neumann et al. [2022] Zhao et al. 2022). Random or compartment-basse pairing often produces overly easy, biased examples that lead to overly optimistic results, whereas co-localized, functionally related, or topology copology-aware aware sampling yields harder and more informative ones (Ben-Hur and Noble 2006 Park and Marcotte 2011 2012 Zhang et al.] 2018).
These observations motivate our adaptive negative weighting loss, which applies velf-aaversarial weights (Sun et al. 2019) based on model confidence so that harder negatives are assigned greater weights, mitigating biases from easy constructed pairs.
Contributions. We introduce RaftPPI, a retrieval- -rrinvdly,, residue-aware framework that com- presses each protein into an indexable embedding for efficient ANN search, reducing proteome- PPI screening from GPU months to minutes on single GPU or CPU. With an adaptive negative- weighting loss that emphasizes hard negatives and mitigates the lack of reliable negative data, RaΩPPI achieves strong classification and retrieval performance under rigorous sequence- and degree- controlled benchmarks.
2 RELATED WORK
Protein Language Models. Transformer-basee Protein Language Models (PLMs) pretrained on large sequence corpora (c.g.. ProtTrans (Elnaggar et al. 2021). ESM lb (Rives et al. 2021), and ESM 2 (Lin et al. 2023) learn residue-level embeddings that implicitly capture evolutionary and structural priors. Generative PLMs such as ProGen and ProGen2 (Madani et al. 2020; Nijkamp et al. 2022) use autoregressive modeling for controllable sequence design. Beyond sequence-only vocabularies, Foldseek (van Kempen al. 2024) introduces discrete 3D structure tokens that have been used to augment PLMs with structure-awad vocabularies (SaProt (Su et al. (023)) or to predict structure tokens directly (ISM (Ouyang Zhang al. (025)). These models provide transferable features that support many modern PPI predictors, including our method.
Protein-Protein Interaction Prediction. An ideal way to assess PPIs to predict the 3D structure of protein complexes directly using end-to-end structure predictors such AlphaFold2 (Jumper ct al. 2021), RoseTTAFold (Back et al., (02T), AlphaFold-Multing
(Evans et al. (022). and the
more recent AlphaFold3 (Jumper al. (0024). These systems achieve remarkable accuracy but computationally intensive, often rely on multiple sequence alignments (MSAs) and/or structural templates, and require explicit per-pair inference. Another line of work formulates PPI graph machine learning problem (e.g., link prediction (Nasiri et al. (021)) on residue- or protein-level graphs. Examples include diffusion-state methods (Devkota et (020)) and GNN-based approaches such as GNN-PPI (Lv et al. 2021), SGPPI (Huang et al.) 2023). PPI-GNN (Jha et al., (022), and HIGH-PPI (Gao et al.) 2023). Although graph priors can be powerful, the performance of such models depends on the availability and quality of the underlying network and can be vulnerable to degree bias and data leakage (Bernett al., (2024). These limitations prevent proteome-leva screening. In light of this, we focus on sequence-only methods, as they are alignment-free (no MSA needed) and graph-free, while maintaining good performance.
Among sequence-only methods, early work such as SPRINT (Li and Ilie 2017) computes pair-specific similarities with spaced-seed k-mer hashing. Subsequent deep learning encoders include DeepFE- PPI (Yao et al. 2019), the fully connected and LSTM models of Richoux et al. (Richoux et al. (211)). and PIPR's Siamese residual RCNN (Chen et al., 2019). Many later sequence models follow the D-SCRIPT (Sledzieski et al.
2021 paradigm, computing residue-residue interaction scores and
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Residue Contact Prediction
Pred&Agg
ProteinLM
Res-interaction Scores
PPI Score for single pair
Residue pair Attention
O O(N2 1.²)
Iterate 200M protein pairs
Res-interaction Attention
Approximates
Random Fourier Transform
Protein Query
O RaftPPI
Low-Rank Attention
Fourier Feature
ProteinLM
Protein Key
Fourier Feature
Top-K Retrieved Protein
Database
HNSW
ANN Search
DB
Iterate 20k query proteins
Protein
Protein K
Figure 1: Overview of RaftPPI. RaftPPI approximates a standard PPI pipeline Pred&Agg (above) that predicts residue-level contact scores and aggregates them using attention a final PPI score. The Pred&Agg pipeline costs ((N2L3) for proteome-win retrieval. RaftPPI uses Random Fourier Fea- tures to approximate a Gaussian kernel and leverages low-rank attention. The pipeline factorizable (below), enabling per-protein Fourier-feature embeddings and ultra-fast retrieval via approximate nearest neighbor search (c.g., HNSW) at protcome scale with ONN L2)
precomputation.
aggregating them into a protein-level score; Topsy- Turvy (Singh et al. 2022) and TT3D (leeziiskii al., 2023) further extend this approach by incorporating graph priors (Devkota al. 2020) and structural embeddings (van Kempen et al. 2024). Recently, PLM-based approaches (Sledzieski al. 2024 Ko et al., 2024| Yang et al.) 2024 Liu et al. 2024) leverage the strong performance of pretrained PLMs (Lnn et al., 2023 Elnaggar et al. (021) to model interactions residue-level resolution. These approaches are typically accurate but factorizze: they jointly encode each protein pair, which makes proteome-scal. retrieval computationally prohibitive.
Proteome-level PPT Screening. Whole-proteome screening poses computational challenge due to the quadratic number of protein pairs. Recent pipelines address this using multi-stage filters coupled with structure prediction. In RF2-style workflows (Humphreys et al., 2024 Zhang et al. 2024), GPU-accelerated coevolutionary analysis (DCA (Ekeberg al. 2013)) first prunes hundreds of millions of candidates; top pairs are then rescored with lightweight Rose TTAFold2-2aa contact predictor, and the highest-confidence subset is finally evaluated with full AlphaFold2. These efforts primarily focus accelerating structure prediction; for example, RF2-Lite (Humphreys al., 2024) streamlines refinement with a two-track network that ~ 20 faster than AlphaFold2, yet still requires explicit per-pair inference after pre-screening.
Our Position. RaftPPI is residue-awar yet retrieval-friest framework. It produces indexable protein embeddings whose inner products approximate residue-level interaction scoring, enabling ANN-based proteome screening without explicit per-pair inference. Compared with sequence-only models, RaftPPI attains stronger PPI classification while scaling to whole-proteome retrieval via precomputed embeddings. Compared with structure-prediction methods, it offers orders-of-magnituue faster screening by narrowing candidates first, after which shortlisted pairs can be rescored with accurate complex structure predictors.
METHODOLOGY
Figure summarizes the RaftPPI framework. We first revisit a standard two-step pipeline for sequence-based PPI prediction (3.1), then show how RaftPPI approximates residue-level modeling while enabling scalable protein retrieval by coupling a Gaussian interaction with low-rank (separable) attention and Structured Orthogonal Random Features (SORF) ($3.2) We optimize the
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model with adaptive negative reweighting objective ($3.4) and enable proteome proteome-scale search via vector indices over precomputed embeddings ($3.3).
Throughout, consider candidate protein pair (A, with residues indexed I.... La and I...., LB. respectively. Bold lowercase symbols (c.g.. =) denote vectors, bold upperease symbols (e.g., W) denote matrices, ((+) is the logistic sigmoid, and || the Euclidean norm.
3.1
PIPELINE FOR TWO-STEP PPI PREDICTION
Since protein interactions occur at the residue level, standard approach is two-step pipeline (Sledzieski et al., 2021 Singh et al., 2022] Sledzieski et al., (023) as illustrated in the upper panel of Figure [] Predict residue-pair contact scores, then aggregate them into protein-pair score. We refer to this two-step pipeline as Pred&Agg and define it as follows:
Residue-level contact matrix prediction. Given residue embeddings FA,6, E Rd from pretrained protein language model (PLM) (Lin al. 2023 compute contact scores
Cuj f(zAn) R,
(I)
where f(..)) typically an MLP or an inner product, producing contact matrix C E BLaxLa,
Protein-level interaction aggregation. This residue-level score matrix is pooled into scalar logit:
((A, B) g(C) TR,
where g(-) denotes pooling operation (e.g., max/conv/2D attention).
There are many instances of this Pred&Agg pipeline. For example, D-SCRIPT (Sledzieski et al. 2027) uses Bepler & Berger embeddings (Bepler and Berger 2019] with element-wise transforms to predict C, then pools to a pair score. TT3D (Sledzieski et al. (202) and Topsy-Turvy (Singh et al. 2022) incorporate graph-based supervision (Devkota al.j 2020) and 3Di structure encodings (van Kempen et al. 2024). While effective, these methods are foctoritee;::ble: they rely on dense nonlinear residue pair computations, yielding O I,2) complexity for N proteins of length L, which is prohibitive for proteome-scale screening. Next we show how RaftPPI approximates this pipeline as a product between single-protein embeddings.
3.2 KERNELIZED RESIDUE INTERACTIONS WITH LOW-RANK ATTENTION
As noted above, the nonlinearity in Pred&Agg yields strong accuracy but hinders retrieval because requires explicit pairwise inputs. Our idea is to approximate these steps with factorizable ones where protein interaction scores are computed via dot-product between protein embeddings.
Predict (kernelized residue-residue scoring). Gaussian kernel
We model residue-residue interactions with
ZA,i =BB;
As(ZA,,.ZB.) exp
20
where 6² is the kernel bandwidth that controls how quickly the kernel decays with residue-embeddiig distance. Smaller ô emphasizes very local residue matches, while larger values blend information across a wider neighborhood (see Appendix B.2 for the quantitative sweep). The kernel corresponds to an inner product in an infinite-dimensionall Reproducing kernel Hilbert space (RKHS), enabling rich nonlinear scoring.
Aggregate (attention-weighte pooling). protein-level logit via weighted sum:
We aggregate the residue-level kernel scores into
LA
La
E(A, B
ZB.3)
(4)
j=
where 82j is the attention weight, determining the set of residue pairs of interest.
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Factorizable low-rank attention. Low-rank (separable) attention is standard strategy for reducing quadratic cost (e.g.. Wang et al. [020]] Choromanski et al. (021). We approximate the residue- pair attention with a rank-r separable form. Denote the residue embedding matrix A E R x and B E IR X for each E (l....,,7) r a lightweight per-residue scorer 1i Rd
applied row- wise to produce unnormalized importances, which we normalize with softmax obtain per-chain weights:
A softmax h2 (0) (Za)),
(2) softmax (1g (ZB)). w B
(5)
Collecting columns gives Wa = [w (1) .."... with nonnegative entries and each column summing to We then set
w (r)
E R 50
LA and WB = w B ...00.. E
(1)
(r)
R
L
20
$
(8) A. B
(9)
== B-WWBB
(6)
yielding a factorizable attention surface. In practice we instantiate r=1 (so 8i, WA,.B.)), which achieves strong performance with minimal parameter cost.
3.3 FAST INFERENCE WITH RANDOM FOURIER FEATURES AND VECTOR SEARCH
Kernel approximation with Random Fourier Features. We approximate ka using Random
Fourier Features (RFF) (Rahimi and Recht 2007). Given d' target frequencies, let W e xd and define
(=z)
v coo W sin (Wz) =)] R222"
that have factorized form approximate the kernel
ki (x, y) A (ax) T (t))
(8)
To construct W efficiently, use Structured Orthogonal Random Features (SORF) (Yu et al. A SORF block of size x is
2016).
W Va HD,HD,HD,
where H is the normalized Malsh-Hadamard matrix and D; are diagonal Rademacher sign-flip matrices. The rows of W satisfy E[ww = : --2 matching the Gaussian second moment and providing low-variance RFF approximation of the Gaussian kernel. To obtain d' frequencies, we generate independent SORF blocks and concatenate their first d' rows to form gd'xd,
Fast retrieval via factorizable scoring.
Using Eq. 8 and the r=1 attention, we obtain
LA
Lg
E(A, B)
8ū ka (2.
j=1
LA LB
WA, 16 (z (((B,))
(10)
:=
LA
LB Σ
WA. (Z
WB,j v v(zB.))
Define the per-protein embeddings
LA
LB
hA
WA, (=
hn =
Σ wnJ (
(11)
which yields the factorizable approximation that computes the logit {(A, B) in a dot-product form: {(A, B) == (hA, hB)-
(12)
We fix the same SORF transform W at training and inference for alignment, and store h for approximate k- k-nearest neighbor search with HNSW (Malkov and Yashumin] (020) (inner-product retrieval in the transformed space).
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3.4 TRAINING OBJECTIVE
As noted in the Introduction, experimentally verified non-interactions are rare, so negatives are often constructed and vary widely in informativeness (Neumann al. 2022 Zhao al., 2022). Heuristic constructions (e.g., enforcing different cellular compartments) often produce overly easy, biased negatives, leading overly optimistic results. Mcanwhile, co-localized, functionally related, or lopology-awan choices tend to be harder and more informative (Ben-Hur and Noble 2006) Park and Marcotte 2011 2012 Zhang et al. 2018). Motivated by this, we adopt adaptive negative weighting, where the relative contribution of each negative is automatically determined by the model's 's own confidence, allowing harder negatives to exert greater influence.
Let ((A, = (hA. hg)) denote the logit for a pair (A, B). Over minibatch, let P and be the index sets of positive and negative pairs, respectively. Inspired by self-adversarial training in knowledge-gret graph reasoning (Sun et al. 2019), we define temperature-scaled weights over negatives
exp(T()) Σ exp(TTt) JEN
=
iEN, 7 2 0,
(13)
and stop gradients through in practice. Intuitively, reflects the model's (normalized) confidence that negative pair is actually positive, i.e. higher p: thus identifies harder negatives. We then combine a standard positive term with an adaptively reweighted negative term:
=
1 2
Σ PEP
log (lp)
Σ iEN
Pt log (
(14)
P
When т 0, Eq. reduces to balanced BCE; T 00, it focuses the hardest negative. In practice, T = 4 offers good trade-off, as shown in Appendix B.4
3.5 COMPUTATIONAL COMPLEXITY
Consider a proteome with N proteins of average length L. PLM embedding dominates at O(N Our mapping/aggregation adds QT. log d + d')) per protein (vs. O(Ldd') for dense RFF), which is linear in L and minor in practice. After caching h. HNSW indexing is O(N log N), queries grow polylogarithnically N, and memory is (NN. Hence the end-to-end complexity O(NL2), with indexing/search negligible; empirically, top-20% retrieval completes in ~6 minutes on an A100 (,
3.3 minutes on an Intel Xeon CPU) for N x= 10 (see 44.3
Table 3).
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. As discussed in (Bernett al., 2024 naïve PPI splits and datasets prone data leakage and confounding from sequence similarity and node-degree biases, which can yield over- optimistic performance and poor transfer. We therefore adopt the processed datasets/splits in (Bernett et al. 2024) that (i) remove near-duplicate or homologous sequences across train/validation and test sets, minimizing the impact of raw sequence similarity, and (ii) control protein occurrence frequency so that hub proteins do not trivially inflate accuracy via degree priors. This setting makes PPI prediction more realistic and challenging. The datasets span two species: yeast (Guo, Du) and human (Huang, D-SCRIPT, Pan, Richoux, Gold). Their statistics are shown in Table [ In Appendix B.6 we further evaluate RaftPI and baselines on the larger-scale PiNUT-human and PiNUI-ycast datasets [Dubourg-Felonneau et al. 2023).
Baselines. We evaluate 10 PPI classifiers spanning classical sequence models (D-SCRIPT (Sledzieski
et al.] 2021). DeepFE (aao al.] 2019), Richoux-FC/LSTTI
(Richoux al. 2019),
(Singh et al. 2022). SPRINT (Li and llee 2017)) and PLM-bbsed methods (ESM2- MLP (Sledzieski et al. 2024). TUnA (Ko et al. 2024). PLterrrtract (Liu al. 2024)). For all
PLM baselines and for RaftPPI, we use ESM2-8M as the backbone; we also include an unsupervised
ESM2-NoFT baseline (dot product over [CLS] embeddings). Prior work (Fournier et al. 2024

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Table Dataset statistics of seven PPI datasets spanning two species (human and yeast),
Dataset
Species
Train
Val
Test
Total
Pos
Neg
Total
Pos
Neg
Total
Pos
Neg
Total
GUO
Yeast
2,088
2,088
4,176
232
232
464
861
861
1,722
6,362
DU
Yeast
6,536
6,486
13,022
698
748
1,446
2,421
2,421
4,842
19,310
HUANG
Human
1,094
1,075
2,169
111
130
241
713
713
1.426
3,836
D D-SCRIPT
Human
12,218
122,165
134,383
1,356
13,575
14,931
8,467
84,670
93.137
242,451
PAN
Human
14,069
14,022
28,091
1,537
1,584
3,121
4,575
4,575
9,150
40,362
RICHOUX
Human
17,873
17,798
35,671
1,944
2,019
3,963
5,167
5,167
10,334
49,968
GOLD
Human
81,596
81,596
163,192
29.630
29,630
59,260
26,024
26,024
52,048
274,500
and our analysis at Appendix [.1] show that scaling ESM2 (35M/150M/650M) does not consistently improve these tasks; moreover, proteome-scale retrieval with per-pair PLM inference is already expensive at the 8M scale, consuming GPU-months computation time (see Table 3p. In this case, we ESM2-8M for the best performance//troughpuu trade-off in our evaluations.
4.2 PROTEIN-PROTER INTERACTION CLASSIFICATION
Table Test AUROC Performance (%) of competing methods on the seven PPI datasets. Higher is better; the rightmost column shows the mean across collections.
Method
D-SCRIPT
Huang
Pan
Richoux
Gold
Guo
Du
Average
D-SCRIPT (leedzieki et al.
2021
81.99
65.72
68.44
59.15
49.91
47.14
50.92
60.47
DeepFF el al. 2019)
61.66
56.93
53.70
57.43
53.21
58.21
$6.05
56.74
Richoux- Richoou el 2019
47.53
59.06
52.39
59.72
53.53
55.81
$9.89
Richoux-LSTM [Kichoux
50.67
56.56
47.81
50.37
49.16
51.55
56.37
51.78
SPRINT (Li und Ile 2017
64.62
46.71
43.39
55.71
51.50
48.80
51.80
51.79
Topsy-Turvy (Singh et al 2022
75.38
55.52
65.80
48.67
58.74
43.65
61.60
58.48
ESM2-NoFT [L ale 20.6
75.01
58.63
59.51
63.26
57.85
62.87
$7.36
62.07
ESM2-MI.P (Sedzzeskk
2024
82.83
73.34
$6.35
83.54
73.34
TUnA (Ko
83.38
66.66
77.06
76.73
52.55
69.81
69.37
70.79
PLM-Interact (Luu 2024
84.77
69.69
73.03
78.66
65.00
79.60
75.20
75.14
RaftPP
82.06
72.20
74.21
69.88
68.69
84.93
75.06
75.29
The PPI classification results across the seven datasets are reported in Table 2 We observe that methods without pretrained S.LMs-D-SCRIP DeepFE, PPRINT-Perfone substantially worse than ESM2-based models, all falling below even the unsuper- vised ESM2-NoFT baseline. This because they rely largely on sequence similarity and node-degree information, which fails under controlled splits (Bernett et al.) (024), whereas ESM-based models benefit from large-scale pretraining, where structural properties such as secondary structure can be inferred from embeddings (Rives et 2021).
Topsy-Turvy, and
For ESM2-based baselines, all models finetuned for PPI outperform ESM2-NoFT. PLM-Interact achieves the strongest results, likely due to its farly-fusion design, which allows deeper layers to jointly model cross-pre pross-protein interactions. In contrast, TUnA and ESM2-MLP fuse only at intermediate final layers, limiting their ability to capture joint interactions. This echoes the carly-fusion advantage reported in other domains (Snoek et al. (005). Meanwhile, RaftPPI attains the best average performance, attributable residue-level interaction modeling and adaptive negative weighting, which we further discuss in $4.4
4.3 PROTEOME INTERACTION RETRIEVAL
Compared with binary PPI classification, PPI retrieval more closely reflects real-world applications: interactions in proteomes are sparse and highly imbalanced (negatives dominate), and one must screen entire candidate proteome to identify true interactors for query protein. For computational tractability, we sample 100 query proteins per dataset and use the models trained in Section 4.2 to retrieve positives the test split. We compare RaftPPI to PLM (ESM2) baselines NoF.. TUnA, PLM-Interaa ESM2-MLPP and to RaftPPI-P. special version that removes the residue-level design of RaftPPI, which predicts PPI using the dot-product of (CLS) token embeddings.
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Recaliga%
Recallga%
ReceligaN
09
FO0
AT
25ND
352
TPA-
30
13
15
29
TMMA
15.4
23
52
50
TAM- 2411
13
13
63
59
384
Recallg10%
Roc
Ave rage Curves
362
200
255
1S0 KuT
PM40 NTT
403
24.7
26.3
L93
26-2
MD
28.
312
362
325
-2
324
1811
22.0
28.9
217
02.3
0340977
Figure 2: Proteome retrieval with residue-level fidelity and scalable retrieval. Heatmaps report Recall @ K% for K (1, 5, 10, 20) across methods and datasets, with per-dataset normalized color scales (best=100% worst=0%). Red rectangles mark the best method; purple rectangles mark the second best. The sixth panel shows average recall curves across datasets.
Type
Model
Encoding (s)
Recall@ 1/3/5/10/20% (s)
AUROC (%)
Recall@20% est. full (%)
ESM2-MLP
NA
1766576
74.25
27.77 / NA
Unfactorizuble
TUnA
NA
3833646
70.79
30.44/ NA
PLM-Interact
NA
12827660
75.14
30.81 NA
ESM2-NoFT
105
54/ / 170 259
62.07
42.37 44.72
Factorizable
RafiPPI-P
54
40/
118
187
71.90
45.73 43.83
Raf1PI
102
/ 98 157 241
75.29
48.33 / 47.91
Table 3: Human proteome retrieval efficiency (Recall @ K% end-to-end time) and average classifi- cation/retrieval performance. Factorizable methods reuse single-protein embeddings to build the HNSW index once (encoding time is one-time cost). For unfactorizable methods, recall (est.) and time are estimated using 100 query proteins; reported times are total seconds for the full proteome, measured on one A100 GPU. More runtime settings and discussion are in Appendix A
Retrieval performance. As shown in Figure 2 non-factorizable methods-i.e. models that jointly encode protein pairs such as ESM2-MLP. TUnA, and PLM-Interact-achiee strong binary PPI classification but do not outperform the simple ESM2-NoFT baseline in retrieval. We hypothesize that this gap arises because the data splits are explicitly designed to minimize sequence similarity (Bernett al. 2024), limiting the models' ability exploit correlations between sequence similarity and structura interaction. In contrast, factorizable approaches naturally capture sequence similarity through embedding dot products. Among these, RaftPPI consistently ranks first or second across datasets and recall thresholds, and its improvements over RaftPPI-P highlight the benefit of explicitly modeling residue-level interactions.
Retrieval efficiency. Table 3 reports runtime comparisons. Non-factorizable
TUnA, and PLM-Interact are prohibitively slow because they require per-pair inference. The strongest of these, PLM-Interact performs well on both classification and retrieval, yet demands 148.47 1100 GPU-days (f:4.9 months) to screen the human proteome, underscoring the impracticality of exhaustive search. By contrast, RaftPPI compresses each protein and performs approximate
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Classification [AUROC; normalized best=100)
Performnte
Dreed
Dataents
Ablasion Retrioval (Re (2g22%0 normalized to bests 106)
Nomelaad
MA
Deupp
Detansta
Figure 3: Ablation Study. Scores are normalized to the best method per dataset (100%=peak). Top: classification AUROC. Bottom: retrieval Recall 20%. The full model (RaftPPI) attains the best or second-best performance in every case; removing any single design choice causes a clear drop.
nearest-neighbor search, reducing proteome retrieval from GPU months minutes, Overall, RaftPPI delivers the best balance of retrieval accuracy, classification performance, and efficiency.
4.4 ABLATION STUDY
focus on four research questions and validate our core designs. RQ1 (residue-level modeling): Does explicit residue-level modeling help? RQ2 (kernel): Does replacing linear dot product with Gaussian kernel (approximated via SORF) improve performance? RQ3 (aggregation): How should residue scores be aggregated most effectively? RQ4 (adaptive negative weighting): Is adaptive negative weighting effective in helping the model assign greater weight to harder negatives?
We perform ablation studies these research questions and show the results in Figure RQ1 (residue-level modeling): The coarse ESM2-MLP baseline encodes proteins and predicts interactions from concatenated embeddings. Even when augmented with our adaptive loss (ESM2-MLP-ANWW
Loss), it competes on small classification splits but collapses on retrieval (e.g.. - 466 Recall@ 20% on Gold), confirming the necessity of residue-level reasoning for canedcandiddte screening. RQ2 (kernel): Replacing the Gaussian kernel with linear dot product (Raft- (oott))oSORF) consistently reduces AUROC and Recall @ 20%% showing the benefit of kernelized interactions. We additionally discuss the impact of Gaussian bandwidth in Appendix B.2 RQ3 (aggregation): Averaging (Raft- are slightly weaker than attention, indicating that attention helps identify PPIs. RQ4 (adaptive negative weighting): Switching to uniform BCE (Raft-BCE) degrades AUROC and Recall @ 20%, especially on D-SCRIPT dataset where negatives outnumber positives by roughly 10x, highlighting the value of prioritizing hard negatives (we provide a detailed ablation study the temperature т in Appendix B.4). Collectively, these ablations validate each design choice in RaftPII, demonstrating the effectiveness of kemnelized residue interactions, SORF-bar b-based kernels, attention-base aggregation, and adaptive negative weighting.
Avg-Agg) or using a [CLS] ] score (Raft-CLS-AA)
5 CONCLUSION
We introduced RaftPPI, a residue-aware yet retrieval-friendll framework that factorizes kernelized residue interactions with structured random Fourier features and low-rank attention produce compact per-protein embeddings for ANN search. With adaptive negative weighting, RaftPP achieves the best average classification and retrieval performance across seven sequence- and degree- controlled benchmarks while enabling minutes-level screening on the human proteome.

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ACKNOWLEDGMENT
This project is supported by the Intel-MILA partnership program, the Natural Sciences and En- gineering Research Council (NSERC) Discovery Grant, the Canada CIFAR AI Chair Program, collaboration grants between Microsoft Research and Mila, and a NRC Collaborative R&D Project (AI4DCORE-O) -6). This project was also partially funded by IVADO Fundamental Research Project grant PRF-2019-3588 Québec and the Digital Research Alliance of Canada and Intel Corporation. For several intermediate approaches, a cluster consisting of Intel Xeon Scalable CPUs (Intel Xeon R Intel Max GPUs (Intel Data Center Max 1550) was used for training and inference.
The computation resource of this project is supported by Mila, Calcul
CPU Max 9470C) and
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A IMPLEMENTATION DETAILS
Experimental settings. All experiments use five random seeds 2,3,, 3,4: unless noted, we report the mean (and standard deviation when available) across seeds. Results for D-SCRIPT (Sledzieski et al., 2021), DeepFE (Yao et al., 2019). Richoux-FC/LSTMM (Richoux et al.) 2019), and Topsy- Turvy (Singh al. 2022) are taken from the benchmarking study Berntt et al. (2024); following their guidance, we apply minimal tuning to baselines based on validation performance. Unless otherwise specified, we use single configuration across datasets: AdamW (Ir 10 4), 2048 random Fourier features, Gaussian kernel bandwidth & = 0.5 (App. B.2), and adversarial temperature 7 = 4 (App. B.4). Software environment: Python 10; PyTorch 2.5 with CUDA 11.8.
Hardware and runtime reporting. GPU timings in Table 3 are measured on single NVIDIA A100 GPU. CPU timings are measured on an Intel Xeon 6980P consisting of 128 cores the human- proteome experiment in $4.3 (N x 20,000 proteins), retrieving top-20% candidates corresponds to querying K = 0.2NV nearest neighbors per protein (about 4,000), yielding R3 40M unique pairs after symmetrization. While fantorictre methods must iterate over all candidate pairs (= 200M), RaftPPI enables fast proteome-seae retrieval via pre-built HNSW index over cached single-protein embeddings. On CPU, the end-to-end time is 200 seconds (3.3 minutes): 71 seconds for encoding all proteins and 129 seconds for top-20% HNSW retrieval. On GPU, the end-to-end time is 343 seconds (5.7 minutes): 102 seconds encoding and 241 seconds retrieval (Table 3).
B ADDITIONAL EXPERIMENTS
B. MODEL-SCALE SELECTION
ESM Nodzl Scaling Andlysi Classifiction (TSSLAROC)
Eecret
11.ngg
kchru
Txx
$150
1120
11:00
60.0
Figure 4: ESM2 model-scaling analysis for PPI binary-classificttion (Test AUROC) across model sizes and datasets. Increasing the parameter count beyond 8M does not consistently improve performance.
Following the observation of Fournier et al. (2024) that larger protein LMs do not necessarily yield better results, we conduct a systematic scaling study on ESM2 (I.in et al. 2023). We evaluate four
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ESM Model Scaliin ysls
hodds-Proteseee Proteome PPI Retr evbl
Derter
Haro
1200
Auempe Acoan Darivess
Figure 5: Scaling analysis of ESM2 checkpoints on proteome-level PPI retrieval tasks (Recall K%).
Performance remains largely unchanged when increasing model size.
checkpoints: 8M, 35M, 150M, and 650M parameters across seven PPI datasets spanning human and yeast. Protein pairs scored by the dot product of their [CLS] embeddings, providing unsupervised measure of scaling performance. As shown in Figures and both Test AUROC and Recall @ K% change little with model size on nearly all datasets.
Given that inference time for pairwise-encoding models rises steeply with both model size and quadratic pairwise scoring (c.g.. PLM-Interact (Liu et al.) (024) requires GPU-months search the human proteome even with an 8M model due to its pairwise encoding; see Table we adopt the 8M ESM2 checkpoint as the backbone for all PL.M Maseedd baselines and for RaftPPI, balancing predictive performance and efficiency.
B.2 ABLATION ON GAUSSIAN KERNEL BANDWIDTH
Coursian-Bandwiddd (c) Ablation Study
Clacsiiaate Hanandiaastt
Casol Yeaut deaasss
Rationd Human datasets
Rutiooal Yeait daaads
Figure 6:
Gaussian bandwidth ablation: We vary the kernel width from 0. 1.5 to 8 (log-scale w-axis) Gaussian-bandwidtth
and report the averaged AUROC/RecalleG @ 20% across human and yeast datasets.
The Gaussian kernel Eq. controls how strictly residue-level interactions are determined: smaller values confine each residue to interact only with very close neighbors (capturing sharp. local interfaces), whereas larger à allows larger interaction scores over a broader neighborhood. Figure sweeps à E (0.125, 0.25, 0.5. 1, 4, 8) and averages the metrics within human and yeast datasets. From the results of both PPI classification and retrieval tasks, both very small and very large bandwidths degrade performance. For example, tiny à encourages sparse interactions, yet leads to a clear drop in performance when the bandwidth too small; very large over-smooths the residue-level structure and significantly hurts both PPI classification and retrieval performance. As = 0.5 consistently achieves good performance across species, we adopt this value for all reported experiments.
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Table 4: Impact of attention rank on AUROC and retrieval (macro-average across seven benchmarks). Higher is better, bold marks the best value and underline the second best per metric.
Rank
AUROC (%)
@ecall 1
Recall 3%
Recall@5%
Recall @ 10%
Recall 20%
75.29
10.89
18.19
23.31
33.95
48.33
2
74.56
11.33
18.89
24.28
33.72
47.24
4
69.49
10.23
17.12
22.10
31.47
43.89
8
66.13
8.00
13.95
18,65
27.84
40.39
63.86
7.08
12.86
17.16
25.50
37.91
32
64.71
7.46
12.98
17.37
25.76
38.19
B.3 ABLATION ON LOW-RANK ATTENTION
Section 3.2 defines rank-r attention as r independent softmax pools (columns sum to onc), and Eq. 4 combines the pooled descriptors with the Gaussian feature map in Eq. 3. For completeness, derive the rank-r retrieval formulation implied by that construction. Let V A and V B R LB x2d' stack the Random Fourier Features for proteins A and B, and let the attention matrices be WA ERR4AX and WE R with columns that sum to one. The pooled embeddings for each rank the rows of
H W V E Rex2d
Hn W v E Brx2d'
(15)
and the proteome-scan scale logit becomes
(AA
Σ
(h (8)
(1) (
(HHH (vve((HA), vec(H))))
(16)
where vec(.) denotes vectorization. Computing retrieval scores for 1 therefore amounts to concatenating the heads into a single embedding dimension 2d'r per protein. Higher ranks can provide stronger expressiveness, but the embedding dimension, memory footprint, and dot- dot-product cost all grow linearly with r because each additional head contributes another 2d' Random Fourier Features (d' for each sin/cos feature).
Table(4reports the seven-dataset macro-average. Rank already achieves the best AUROC (75.29) and Recall @ 20% (48.33). Moving to rank 2 mildly improves the very top of the ranking Recall @ 1%, Recall @ @3,, and Recall @ 5% increase by less than I % without enhancing AUROC, indicating that the extra head enables slightly stronger early recall capabilities. Larger ranks, however, consistently overfit: AUROC drops below 70 at rank and to 63.86 at rank 16, while Recall @ 20% degrades from 48.33 (rank 1) 37.91 (rank 16) despite the 16x increase in memory footprint. Rank 32 further underperforms every metric. Since rank-one attention is effective enough while achieving the best efficiency compared higher ranks, we keep as the default.
B.4 ABLATION ON ADAPTIVE NEGATIVE WEIGHTING Loss
As introduced in 3.4 adopt adaptive negative weighting to mitigate the challenge of constructed negatives in PPI datasets. We use a weighted BCE loss that softly prioritizes harder negatives through temperature parameter (Eq. [3 When
0, the objective reduces to uniform BCE; when - xo it focuses entirely on the single hardest negative in the batch. Figure shows that neither extreme is optimal. Across species and protocols, performance peaks around 4: increasing from 0.125 to 4 improves average test AUROC by ss 2-3 points and Recall @ 20% by Rs 3 points, while larger values degrade results by overfitting to outliers. This ablation highlights that moderately emphasizing harder negatives can improve sittome-scalee retrieval.
B.5 ABLATION ON RANDOM FOURIER FEATURES DIMENSION
The Random Fourier Features (RFF) embedding dimension (where the number of frequencies in Eq. { controls the expressiveness of the Gaussian kernel approximation. Higher dimensions provide more accurate kernel approximation but increase computational cost and memory footprint linearly with the embedding dimension (the sin/cos feature dimension scales O(2d") and storage
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Published as a conference paper at ICLR 2026
Adversarial Temperature (11 Ablaloon Study
Cloeelt catien Homan Datanek
Clasedicatie Yoest Datasets
Retivead Huree Delieeis
Retreaa: Yeast Balands
Figure 7: Adversariial-temprrtuuu ablation. We vary the temperature T in the adaptive negative weighting loss (Eq. 14) from 0.125 to 32 (log-scale z-axis) Each panel reports the average metric the indicated datasets. A moderate value of (vertical peak) consistently maximizes both classification AUROC (left two panels) and retrieval Recall @ 20% (right two panels) on human and yeast benchmarks.
Fourler Dimension Clamiitcation Yeeal Dataves
Chasel Hhrmnn Detaves
Retreval Huren Dekants
Reteeal Teaat Dalanes
Figure 8: Random Fourier Features dimension ablation. vary the RFF embedding dimension d' from 256 to 4096 (log-scale r-axis; the corresponding sin/cos feature dimension is 2d') and report the averaged AUROC/Recall @ 20% across human and yeast datasets. In contrast to other hyperparameters, performance remains remarkably stable across all tested dimensions, with changes of less than 1.1 points, demonstrating that d' not an important parameter.
scales as O(Nd') for N proteins). Figure sweeps d' (256, 512, 1024, 2048, 4096) and averages the metrics within human and yeast datasets.
Compared the previous hyperparameter ablations, the RFF dimension d' is a sensitive parameter within our tested range. Performance remains remarkably stable across all tested dimensions: varying d' from 256 4096 changes average AUROC by less than points and Recall @ 20% by less than 1.1 points on human datasets, with similarly small variations on yeast datasets. These variations are substantially smaller than the change performance when varying â (Appendix B.2h and varying (Appendix B.4): for example, human datasets, â 0.5 achieves 73.1 AUROC while ô 8.0 drops to 56.5 AUROC, 16.7 point difference. The stability RFF dimension across a 16x range demonstrates that RaftPPI is robust to the RFF dimension choice, and any reasonable value (e.g.. 512-2 2048) works well in practice.
B.6 EVALUATION ON PINUI DATASETS
We additionally evaluate our method on the PiNUI datasets Duuboug-Feelonee et al., 2023) which provide additional sarge-scale PPI classification benchmarks for human and yeast. After cleaning, PiNUT-human contains 684, 444.448 protein pairs (228,317 positive, 131, negative) and PiNUI-yeast contains 157,035 pairs (53,037 positive, 103,998 negative). Both datasets are split randomly into train/validation/tess sets with 60/20/20 ratio; Table 5 summarizes these statistics.
Table reports Test AUROC and AUPRC results across competing methods. The unsupervised ESM2 baseline, i.e.. ESM2-NoFT, performs poorly both datasets, highlighting the importance of fine-tuning for PPI prediction. RaftPPI achieves the best performance on both datasets. These results consistent with our findings the seven-dataset benchmark discussed in $4.2]. demonstrating that the proposed residue-level interaction modeling and adaptive negative weighting in RaftPPI generalize well larger-scale PPI datasets. M- and ESM2-ML. appear to also have strong
apperr
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Published as a conference paper at ICLR 2026
Dataset
Species
Train
Val
Test
Total
Pos
Neg
Total
Pos
Neg
Total
Pos
Neg
Total
PINUI-HUMAN PINUI-YEAST
Human Yeast
136.812 31.797
273,858 62,424
410,670
45,684 10,569
91.205 20,838
136,889 31,407
45,821 10,671
91,068
136.889
684,448
94,221
20.736
31.407
157,035
Table 5: Dataset statisties for PiNUI-human and PiNUl-yeast.
PiNUT-human
PiNUI-yeast
Model
AUROC
AUPRC
AUROC
AUPRC
ESM2-NoFT
59.14 0.00
42.45 0.00
+.90000000
38.74 0.00
ESM2-MLP
75.37 + 0.66
61.97 + 1.03
77.52 = 0.76
63.01 + 0.81
PLM-Interact
= 0.188
62.71 + 0.35
76.77 1 0.96
61.97 1.97=10 1.71
TUnA
64.53 1 0.34
17.31 1 0.55
69.79 1.08
54.29 1 0.67
RaftPPI-P
73.61
61.06
0.31
72.53
0.62
58.64 0.67
RaftPPI
77.92 + 0.39
69.33
0.34
77.87 0.41
69.24 + 0.47
Table 6: Test AUROC and AUPRC performance (%) on PiNUI datasets.
performance. Besides, the improvements of RaftPPI v.s. RaftPPI-P (protein-level interaction only) further demonstrate the effectiveness of our design that considers residue-level interaction.
C USE OF LARGE LANGUAGE MODELS
We used large language model help polish the writing. We take full responsibility for all content in this paper.
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Reviews:
[{"review_id": "vM0CQD0b4j", "reviewer": "Reviewer_6gN1", "summary": "The paper introduces RaftPPI, a sequence‑only framework that preserves residue‑level fidelity yet enables proteome‑scale retrieval. The method (i) models residue–residue scores by a Gaussian kernel on PLM residue embeddings and (ii) uses low‑rank (separable) attention so that the aggregation over residue pairs becomes a dot product between per‑protein embeddings after a Structured Orthogonal Random Features (SORF) transform; this permits HNSW‑based ANN search using one‑time per‑protein encodings. The training objective applies adaptive negative weighting to emphasize hard negatives. On seven-degree and homology‑controlled datasets, RaftPPI attains the best mean AUROC and best mean Recall@20%, while reducing whole‑human‑proteome retrieval from ~148.5 A100 GPU‑days for PLM‑Interact to minutes using a single GPU.", "questions": "- **(Q1)** Please detail the negative sampling protocol(s) used in training across datasets (e.g., random, compartment‑aware, topology‑aware) and the ratio of negatives to positives per batch, so others can reproduce the adaptive weighting behavior.\n\n- **(Q2)** Did the authors compare inner‑product vs. cosine retrieval (with/without L2 normalization) in HNSW, and if so, how did this choice affect recall and calibration of scores? A short note or table would guide practitioners.\n\n- **(Q3)** Did the authors try index‑time compression (e.g., product quantization) to reduce memory for whole‑proteome indices, and how does it trade a small loss in recall for a large RAM reduction? It would be valuable for deployment at a larger N.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- **(S1) Effective methods.** Modeling residue interactions via a Gaussian kernel and approximating it with RFF/SORF yields an inner‑product surrogate for the attention‑weighted residue sum. The low‑rank attention gives interpretable residue weights while keeping the index dimension small.\n\n- **(S3) Strong experimental results.** Firstly, the comparison experiment results are impressive. On seven datasets curated to mitigate sequence/degree leakage, RaftPPI achieves the SOTA results, while compressing human‑proteome retrieval from ~148.47 A100 GPU‑days for PLM‑Interact to 241s for Recall@20% after a one‑time encoding. Meanwhile, the paper evaluates against classical and PLM baselines with a shared ESM2‑8M backbone where applicable, runs five seeds, and includes ablations showing that kernelization (SORF), attention pooling, and adaptive negative weighting are each necessary. The Appendix scaling study (Figs. 4–5) justifies the 8M backbone for throughput without sacrificing accuracy.\n\n- **(S3) Clear motivation and professional presentation.** The manuscript is well organized and written in clear language with comprehensive figures and tables. The methodology section is well structured. The technical descriptions are coherent and self-contained.", "weaknesses": "- **(W1) Approximation fidelity lacks characterization.** The end‑to‑end error induced by rank‑1 separable attention and finite‑feature RFF is not quantified; no bounds or diagnostics relate the surrogate $<\\hat{h}_A, \\hat{h}_B>$ to the original attention‑weighted kernel sum. This leaves open whether interfaces requiring multiple spatial patches(multi‑epitope interactions) demand r>1 or larger feature budgets.\n\n- **(W2) Retrieval evaluation underrepresents full proteome use‑cases.** The protocol uses 100 query proteins per dataset rather than all queries in a proteome or cross‑species searches (Fig. 2), so robustness of HNSW hyper‑parameters, index memory/latency vs. N, and failure modes at scale remain unclear. End‑to‑end curves vs. database size or per‑species full‑coverage runs would better support the proteome‑scale claim.\n\n- **(W3) Metrics for extreme imbalance are incomplete.** The paper reports AUROC and Recall@K%, which are informative but insufficient in heavily imbalanced retrieval; AUPRC/average precision, MAP, and NDCG would make performance at the head of the ranking clearer and facilitate comparisons with practical screening budgets.\n\n- **(W4) Limited sensitivity analysis of key design knobs.** The model fixes r=1, d′=2048, and a single bandwidth, and it stops gradients through the negative weights. However, there is no study of how rank r, feature dimension d′, or bandwidth selection trades accuracy, memory, and latency, nor a comparison to allowing gradients through $p_i$. These are central for practitioners who must size the index and allocate compute.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Y1a0uJ9KX5", "reviewer": "Reviewer_nANi", "summary": "This paper introduces RaftPPI, a method that is a computationally efficient framework which is designed to quickly and accurately predict protein-protein interactions (PPIs) on massive protein datasets. The central problem it solves is that existing high-accuracy models are too slow, requiring months of computation to screen an entire dataset. RaftPPI approximates complex, residue-level (amino acid) interactions using an efficient method that encodes each protein into a single, compact, and indexable embedding. This \"factorizable\" approach, which uses a Gaussian kernel approximated by random Fourier features, allows the model to replace exhaustive pairwise comparisons with a fast nearest-neighbor search. As a result, RaftPPI can bring multiple folds of speedup, while achieving superior accuracy in both PPI classification and retrieval.", "questions": "Please look at the limitation section", "limitations": "", "rating": 4, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "Here are some strengths of the paper:\n\n1. $\\textbf{Computational Efficiency}$\n\nThe paper's main strength is that it directly tackles the most significant bottleneck in protein-protein interaction, which is computational cost. Traditional high-accuracy models are too slow, taking \"GPU-months\" as described in the paper. This paper proposes a method that reduces this significantly by multiple folds. This makes large-scale interaction screening very practical.\n\n2. $\\textbf{Proposed Method}$\n\nThe proposed method solves the efficiency problem in a clever way without compromising the performance of protein-protein interaction performance. Instead of comparing every residue on one protein to every residue on another protein for all possible pairs ($O(N^2L^2)$), it creates a single, compact embedding for each protein which allows the problem to be re-framed as an efficient nearest-neighbor search with $O(NL^2)$ complexity.\n\n3. $\\textbf{Superior Results }$\n\nThe authors show that in average their method performs better than baselines in different datasets.", "weaknesses": "Here are some weakness/questions:\n\n1. $\\textbf{Additional datasets}$\n\nHow will the proposed method perform on datasets like the one shown in [1] which is a dataset proposed for protein-protein interaction for ML pipeline?\n\n2. $\\textbf{Comparison with bigger models}$\n\nIs it possible that your model misses subtle, complex, or non-linear interactions that a full, slower model (like AlphaFold) might capture? Is there a way to compare this quantitatively?\n\n3. $\\textbf{Limitations of the work}$\n\nI don't see any place where the limitation of the proposed work being mentioned. What are some discussions/limitations of this approach? It would be beneficial to include those in the paper or appendix as well.\n\n4. $\\textbf{Interpretability}$ \n\nHow interpretable is your approach? Can it be used with works that require interpretability?\n\n\nReferences\n\n1. PiNUI: A Dataset of Protein-Protein Interactions for Machine Learning; Geoffroy Dubourg-Felonneau and Eyal Akiva and Daniel Wesego and Ranjani Varadan; NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development; 2023", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "axktvnBKug", "reviewer": "Reviewer_3PC9", "summary": "SummaryThis paper proposes RaftPPI, a method to speed up proteome-scale protein-protein interaction (PPI) screening. The main problem it addresses is that existing sequence-based models are too slow for an all-against-all search, often requiring $\\mathcal{O}(N^{2}L^{2})$ computation. RaftPPI approximates a residue-level interaction model with a factorizable one. It models residue interactions with a Gaussian kernel, approximates this kernel using random Fourier features (SORF), and uses a low-rank (rank-1) attention mechanism to pool these features. This allows each protein to be encoded into a single embedding. The interaction score is then just a dot product of these embeddings, which enables fast retrieval using Approximate Nearest Neighbor (ANN) search. The authors also use an adaptive negative weighting loss to focus on harder negative examples during training. The method is shown to be much faster than prior work, reducing screening time from months to minutes on the human proteome, while achieving strong performance on several classification and retrieval benchmarks.", "questions": "1. The use of a rank-1 ($r=1$) approximation for the attention mechanismseems like a major simplification. Did you experiment with higher ranks (e.g., $r=4, 8, 16$)? What is the performance-vs-computation trade-off here?\n2. How was the Gaussian kernel bandwidth $\\hat{\\sigma}^{2}$ selected? Can you provide a sensitivity analysis for this hyperparameter?\n3. The justification for using the small ESM2-8M model rests on an unsupervised analysis of [CLS] token dot products. Why should this analysis apply to the full RaftPPI model, which is finetuned and uses all residue embeddings in a kernelized way? Have you tried finetuning RaftPPI with a larger PLM (e.g., ESM2-35M) to see if performance improves?\n4. The adaptive negative weighting loss is one of several ways to handle hard negatives. How does it compare, both in performance and simplicity, to a more standard, well-tuned focal loss?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The primary strength is the massive, practical speedup. Reducing a computation from 4.9 GPU-months to 6 GPU-minutes is a game-changer for the field and makes routine proteome-scale screening a reality.\n- The experimental setup is rigorous. By using 7 datasets that are explicitly controlled for sequence similarity and degree bias, the authors provide a robust and trustworthy evaluation of their model's performance.\n- The paper is very clear, especially Figure 1, which provides an excellent intuitive explanation of how the factorization is achieved and why it leads to a speedup.\n- The ablation study confirms that the different components of the model (the kernel, the attention, and the loss function) all positively contribute to the final performance.", "weaknesses": "1. The main weakness is the limited novelty of the individual components. The work is a clever integration of existing techniques: PLM embeddings, kernel approximation with Random Fourier Features (specifically SORF), and low-rank (rank-1) attention. This makes the contribution feel more incremental and engineering-focused rather than a fundamental breakthrough.\n2. The use of a rank-1 approximation for the attention mechanism ($r=1)$ is a very strong simplification. It's surprising this works so well and it's not well-justified beyond just stating it \"achieves strong performance\". This could be a significant limitation, as a rank-1 matrix is unlikely to capture complex residue-residue interaction patterns.\n3. The justification for using the ESM2-8M model is weak. The appendix (B.1) bases this choice on an unsupervised scaling analysis of [CLS] token dot products. This is not a good proxy for the full, finetuned RaftPPI model, which uses all residue embeddings in a complex, kernelized way. It's very possible the model's performance is being left on the table by not using a larger, more powerful PLM.\n4. There is no discussion of how the Gaussian kernel bandwidth $\\hat{\\sigma}$ was selected. This is a critical hyperparameter for kernel methods, and its tuning (or lack thereof) could significantly impact performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ShXdimyIm1", "reviewer": "Reviewer_DB9S", "summary": "This work proposes RaftPPI, which models PPI by approximating residue-level interactions while enabling scalable protein retrieval. RaftPPI computes residue–residue scores using a Gaussian kernel and aggregates them into a protein-level score. The non-linear kernel is efficiently approximated via random Fourier features, and pooling is implemented with a low-rank factorized attention mechanism that allows linear-time approximation during inference. Each protein is encoded once into an indexable embedding suitable for ANN search, enabling retrieval of likely interactors while preserving residue-level interactions and avoiding explicit per-pair computation.", "questions": "Please refer to the above \"weakness\" part.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "This work transforms nonlinear residue-level interaction modeling into a factorizable embedding form, substantially enhancing the scalability of proteome-level retrieval. It demonstrates outstanding computational efficiency, achieving an acceleration from GPU-months to minutes.", "weaknesses": "1.\tThe results in Table 2 show no significant improvement, with RaftPPI surpassing PLM-Interact by only 0.15 on the Average metric.\n2.\tRaftPPI, short for Residue-interaction Approximation with Fourier FeaTures, only validates the effectiveness of the residue-interaction approximation in the ablation studies, lacking evidence for the contribution of the Fourier features.\n3.\tThe paper lacks a discussion on limitations and future research directions, which would help readers gain a more comprehensive understanding of the work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)