BioCG: Constrained Generative Modeling for Biochemical Interaction Prediction
Authors: Amitay Sicherman
Kira Radinsky
Venue: NeurIPS 2025 poster ()
Keywords: Constrained Generative Modeling
Drug-Target Interaction (DTI)
Drug Discovery
Enzyme-Catalyzed Reactions
Primary area: machine_learning_for_sciences

Abstract: Predicting interactions between biochemical entities is a core challenge in drug discovery and systems biology, often hindered by limited data and poor generalization to unseen entities. Traditional discriminative models frequently underperform in such settings. We propose BioCG (Biochemical Constrained Generation), a novel framework that reformulates interaction prediction as a constrained sequence generation task. BioCG encodes target entities as unique discrete sequences via Iterative Residual Vector Quantization (I-RVQ) and trains a generative model to produce the sequence of an interacting partner given a query entity. A trie-guided constrained decoding mechanism, built from a catalog of valid target sequences, concentrates the model's learning on the critical distinctions between valid biochemical options, ensuring all outputs correspond to an entity within the pre-defined target catalog. An information-weighted training objective further focuses learning on the most critical decision points. BioCG achieves state-of-the-art (SOTA) performance across diverse tasks, Drug-Target Interaction (DTI), Drug-Drug Interaction (DDI), and Enzyme-Reaction Prediction, especially in data-scarce and cold-start conditions. On the BioSNAP DTI benchmark, for example, BioCG attains an AUC of 89.31\% on unseen proteins, representing a 14.3 percentage point gain over prior SOTA. By directly generating interacting partners from a known biochemical space, BioCG provides a robust and data-efficient solution for in-silico biochemical discovery.

BioCG: Constrained Generative Modeling for Biochemical Interaction Prediction
Amitay Sicherman
Kira Radinsky
Department of Computer Science Technion Israel Institute of Technology Israel amitay. seccs...sss........... technion.. ac.
Department of Computer Science
Technion Israel Institute of Technology
Israel kirarêcs. technion ac.
Abstract
Predicting interactions between biochemical entities core challenge in drug dis- covery and systems biology, often hindered by limited data and poor generalization to unseen entities. Traditional discriminative models frequently underperform such settings. We propose BioCG (Biochemical Constrained Generation), novel framework that reformulates interaction prediction as constrained sequence gener- ation task. BioCG encodes target entities as unique discrete sequences via Iterative Residual Vector Quantization (I-RVQ) and trains a generative model to produce the sequence of interacting partner given query entity. A trie rrie-guied constrained decoding mechanism, built from catalog valid target sequences, concentrates the model's learning the critical distinctions between valid biochemical options, ensuring all outputs correspond to an entity within the pre-defined target catalog. An informatioonweighheee critical decision points. BioCG achieves state-of-the-art (SOTA) performance across diverse tasks, Drug-Targe Interaction (DTT), Drug-Drug Interaction (DDI), and Enzyme- Ennzyme-Racttinn Prediction, especially in data-scarce and cold-start condi- tions. On the BioSNAP DTI benchmark, for example. BioCG attains an AUC of 89.31% 1% on unseen proteins, representing 14.3 percentage point gain over prior SOTA. By directly generating interacting partners from known biochemical space, BioCG provides robust and data-efficient solution for in silsilico biochemical discovery.
training objective further focuses learning the most
Introduction
Predicting whether two biochemical entities interact- example, whether drug will affect its protein target if an enzyme will catalyze a given reaction- pivotal for drug discovery, synthetic biology, and basic biochemistry [18 36 38) Accurate in-silico models dramatically cut experimental
cost and time while guiding the rational design of new therapeutics and pathways 2
30 4 Most
machine- Icarning systems cast interaction prediction binary classification: they embed each entity
independently and train discriminative head to score compatibility 21 33
20 While this
paradigm performs well with abundant labeled data, it breaks down in the low-data regimes common biochemical research. Interaction labels are often scarce due to the high cost of experimental
validation 34 9.. Moreover, discriminative models typically fail to generalize in cold-start scenarios, where the interacting entities have not been seen during training 6 23). Generative models are often seen promising alternative, capable of achieving strong results by learning complex underlying data distributions. However, this typically requires large training corpora, and further significant challenge is that unconstrained generative approaches tend produce outputs that violate structural or chemical rules g 19]. Biochemical interaction prediction occupies a unique position where potential interaction partners drawn from finite catalog of valid entities. For instance, predicting
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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an enzyme's 's function for a novel reaction often involves selecting a known enzyme, which is typically cataloged by its sequence and classified by an Enzyme Commission (EC) number, from a finite set such as the human proteome [1 31]. Crucially, these catalog entries are not simple labels but rich, structured objects (such as protein sequences or molecular graphs) that can be represented as token sequences, [26 39) Therefore, we propose reframing biochemical interaction prediction as constrained sequence generation over a finite set of valid, structured entities.
We present BIoCG that exploits this insight by representing every catalog entry as a compact code sequence obtained via I-RVQ. A trie built over these codes guides the generative model: during training, it directs learning to focus on distinguishing between legitimate candidates, and during inference, it ensures only sequences corresponding to entities from the catalog are emitted. A lightweight meta-calibration model then processes features derived from the generative model's process to output final interaction scores.
The contributions of this work are threefold. First, we introduce BIOCG, a novel framework refor- mulating biochemical interaction prediction constrained sequence generation over finite catalog of valid entities. Second, we develop its core technical innovations: (i) Iterative Residual Vector Quantization (I-RVQ) create unique discrete codes for target entities; (ii) trie-guided constrained decoding mechanism, active during training and inference; and (iii) an information-weighee training objective that enhances learning efficiency and generalization. Third, we demonstrate state-of-the-ar performance across key biochemical benchmarks (including DTI, DDI, and Enzyme- Reattcin Predic- tion), with up to 14 percentage points AUC improvement in challenging scenarios. To facilitate full reproducibility of all experiments, we release our open-source code (GitHub).
Related Work
Prug-Target Interaction Prediction. Predicting drug-target interactions (DTI) is crucial for drug discovery. Deep learning has significantly advanced DTII prediction using diverse architectures, in- cluding Convolutional Neural Networks (CNNs) [6]. Graph Neural Networks (GNNs) for molecular topology (22). Transformers for biochemical sequences 13] and various multimodal or attention- based approachess [3] 20). These methods frame DTI as a discriminative task. BioCG differs by employing constrained sequence generation over known targets, improving cold-start generalization.
Drug-Drug Interaction Prediction. Predicting drug-drug interactions (DDIs) crucial for patient safety. Machine learning models identify DDIs using drug similarities [29 or Graph Neural Networks (GNNs) on molecular structures (7]. These typically classify pre-defined drug pairs. BioCG's framework conceptually differs by generating interacting drug partner from a known set.
Enzyme-Reaction Prediction Identifying the specific enzyme responsible for catalyzing given bio- chemical reaction (often classified by its Enzyme Commission (EC) number) is vital for biochemical annotation and pathway understanding. The CARE benchmark [41 provides a standard evaluation setting for this task. Existing methods often use contrastive learning [4]] [1 or large language models 225 41 to align reaction and enzyme representations, typically followed by classification or similarity baased identification of the most likely enzyme/EC number. BioCG offers a distinct approach by directly generating the catalytic enzyme, conditioned on the input reaction.
Constrained Generation. Constrained sequence generation aims for valid outputs. Many approaches enforce constraints only during inference 11 [2] or require large datasets learn constraints implicitly during training 43 77). BioCG distinctively applies explicit trie constraints from finite biochemical catalogs during training and inference. This enables data-efficient learning by focusing training on the known valid space and guarantees that outputs correspond to valid entities at inference.
3 Methods
We introduce BioCG (Biochemical Constrained Generation), a novel framework designed predict biochemical entity-entity interactions by reformulating the task as constrained, autoregressive sequence generation problem. The overall framework is depicted in Figure It involves representing query entities using pre-trained encoders, transforming target entities into unique discrete sequences via i-RVQ, generating these sequences using a trie-guided constrained decoder, and converting sequence generation probabilities into interaction scores.

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A) Target Exbity RegresesSation
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The BioCG framework. (A) Target entity representation pipeline: Target entities (e.g., proteins) encoded using pre-trained model. I-RVQ iteratively clusters residuals to create unique discrete sequences for each target. (B1) Trie Construction: A prefix tree (trie) built from all discretized target sequences. Nodes with multiple children (orange) represent active decision points where the model must predict the next token among valid options. (B2) Constrained Decoding Mask: The trie determines valid next tokens at each generation step. An attention mask allows the decoder only consider valid continuations (orange cells), effectively pruning invalid generation paths. (C) Constrained Generation Process: A query entity (e.g., a drug) encoded. The autoregressive decoder uses this context and previously generated tokens to predict the next token in the target sequence, guided by the trie-based mask and incorporating information weighting.
Figure I:
Problem Formulation We reformulate biochemical interaction prediction as constrained sequence generation task. Let Ein be the set of input query entities and T = {tz f2..... ., tty } be a finite, known catalog of valid target entities. Given query E En.. objective is identify the interacting target E T. BioCG achieves this by learning to generate a unique discrete sequence representation & (wi,.... . wM) corresponding to the target entity t. The core of our model is to estimate the conditional probability P( e) of generating this sequence given the query e. Crucially, this generation is constrained such that 8; must always represent valid entity within the predefined catalog T. This is achieved by modeling the standard autoregressive probability P(st e) = = P(wx :i...., Wk-1). 1), while ensuring that only sequences corresponding to an entity T can be generated. This constrained sequence generation probability derives the final interaction likelihood between e and f.
3.1 Model Architecture and Representation
Query Entity Representation. Input query entities are represented using standard biochemical sequence formats: proteins via FASTA sequences and small molecules or reactions via SMILES strings. We leverage SOTA pre-trained sequence-based encoders that transform the input query e into fixed-size context vector h... These encoders are kept frozen during BioCG training to preserve their learned domain knowledge and prevent catastrophic forgetting. Specifically, we utilize ESM [10] for proteins, Molformer (28) for molecules, and RXNFP [32) for reactions. The impact of alternative pre-training strategies and models is explored in ablation studies (Section (6)
Target Entity Representation via I-RVQ. While target entities like proteins and molecules possess inherent discrete sequence representations, BioCG employs strategy to generate potentially more effective sequences tailored for the decoder. This involves first obtaining continuous embeddings for each target entity E T using an appropriate pre-trained encoder. Subsequently, we introduce and utilize I-RVQ, our adaptation of RVQ specifically designed to generate unique discrete code sequences from these embeddings for the entire target set. Our I-RVQ approach leverages the standard mechanism of RVQ: it iteratively quantizes residual vectors using k-means clustering (K clusters per layer). each layer m, an entity's 's residual embedding e (m-1) is assigned the nearest centroid (m)
((m) yielding the code (m)) for that layer, and the new residual (m) is calculated by subtracting the
assigned centroid vector. The key adaptation defining I-RVQ lies not in the per-layer quantization step but in its overall iterative application and objective. We apply these I-RVQ layers sequentially (M = 1,2,...) to achieve representation uniqueness across the entire finite target set 'T The process continues, adding layers and thus lengthening the code sequences (1(1) ( ((i)) precisely until every target entity E T possesses distinct code sequence. (See example in Figure
3

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Residaata &
Codks
tasibabb Layer Codes
14:02
0.0
1120
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Figure 2: Simulation of I-RVQ with five target entities, using k-means clusters per layer. Each panel depicts a k-means clustering step where shaded areas represent cluster boundaries and red X's mark centroids aapplids-applied to the residual vectors from the previous layer. Labels indicate each target's ID and the progressively forming code sequence. The iterative process culminates in unique codes for all five targets.
The motivation for generating these learned I-RVQ codes instead of raw sequences is that they can capture salient features in structured, often more compact format suitable for the Transformer decoder, potentially using a more uniform vocabulary and sequence length. This uniqueness-driven I-RVQ process guarantees the bijective mapping essential for our framework. As validated in ablation studies (Section (6) these I-RVQ codes prove more effective for the generative decoder than alternatives such raw biological sequences, random assignments, or standard sub-word tokenization techniques like Byte Pair Encoding (BPE).
Decoder Architecture with Trie-Guided Constrained Decoding, BioCG's generative core, Trans- former decoder [35), autoregressively generates the target's 's unique code sequence s = (Wi...., WMr conditioned on the query context han- A key feature is its rigorous output validity, achieved by con- fining generation (during training and inference) strictly to known valid target sequences in T (Fig. [T]. This constrained approach involves:
Trie Construction: A pre-constructed prefix tree (trie) Gr, built from all valid target code sequences in T (Fig. [B1), defines the valid output space by encapsulating their vocabulary and structure. 2. Masking: At each generation step k, the decoder produces raw logits 4g (over I-RVQ vocabulary V) based on hin and preceding tokens (WI.... Wk-1). The trie GT. queried with this partial sequence, provides a binary mask my E {0. 1] where mg = token is a valid continuation (Fig. BB2). 3. Constraint Application: This mask my is applied to logits l (in training and inference) via: py: = softmax(4
A(1 m²)) where a large À (e.g., 109) effectively zeros out probabilities of invalid next tokens. Thus, py non-zero only for valid continuations from T, focusing learning gradients onto valid pathways.
4. Constrained Training Loss: The training loss uses the constrained probability pi: for the ground- truth token w This directs gradients exclusively towards valid transitions, training the model to learn T's structure and make informed choices among legitimate candidates. 5. Information- seffrmaatioo-Weighhed Loss: Standard cross-entropy is suboptimal for steps with varying branching factors (BFx). Steps with BFg (single valid option) offer no discriminative learning, while multi-option steps risk imbalanced loss aggregation. Our Information- information-Weightee Loss assigns negligible weight to BF; = 1 steps. For multi- steps, it applies weight W (m+) small constant, e.g., 10 9. for numerical stability). This logarithmic scaling by BF, (number of valid choices) focuses learning on the most informative and challenging decision points. The overall training objective minimizes the total information -eeighted negative log-likelihood for a ground-truth target sequence s (wf,...,,0 w.T. given query e:
FF, + 1 + <) (where c is
M
((s]ke) - Σ
(ineo(m))) log pie [e(1)]]]
where Pk [w]] is the constrained probability of the true token W at step k. The efficacy of BioCG's core components, including trie-guided constrained generation and coormation-weighte.. loss, is validated in our ablation studies (Section 66.
Converting Generation Probabilities to Interaction Scores. Once the BioCG generative model trained, a method is required to derive an interaction score for given query entity and a specific

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target entity t (represented by its ground-truth I-RVQ sequence st) for evaluation on benchmark datasets. We first obtain the conditional probability of generating si given e achieve this. This is done using teacher-forcing approach by feeding the query e and the ground-truth target sequence si through the trained generative model. This process yields the sequence of step-wise probabilities Phe [wr]] for each token in sj along with other potential features from the generation process. From this information, we employ two primary methods to compute a final interaction score between e and t:
Log Probability Score: A straightforward score is derived from the average log probability of the ground-trufi truth target sequence s; typically focusing on 'active' generation steps where the trie- guided BF > 1. This score is calculated as: Swogrod = (l[k= BF > 1}1) 1 log pk [ut]- A
higher score suggests stronger predicted interaction.
2. Meta-Model Calibration: A lightweight Transformer-based meta-model is utilized for potentially more nuanced binary classification. This meta-model is trained to take a sequence of features extracted from the teacher-forced generation of sf (the token probability pa [w its rank among valid alternatives, the branching factor Bk, and entropy at each active step k) and output single, calibrated interaction probability for the pair (e,
4 Experiments
4.1 Datasets
Trrg-Target Interaction (DTI) Prediction. For DTI, we use the BioSNAP dataset 44 (derived from DrugBank [14), featuring 13,741 positive interactions between 4,510 drugs and 2,181 proteins, with randomly sampled negatives. We follow established protocols B3 I3 for three splits. In the random splir, where entities are generally seen during training, BioCG generates a known partner (e.g.. protein from ) for a given query (e.g., drug), with T comprising all relevant entities from the training interaction data. For the unseen protein splir, proteins absent from training interactions serve as query entities; BioCG then generates interacting drug from Tangs (all drugs seen in training). Symmetrically, in the unseen drug split, new drugs (absent from training interactions) are queries, and BioCG generates interacting protein from Tproteins (all proteins seen in training). In these cold-start splits, query entities are novel to the training pairs, while the target catalog T is built from entities that were part of training interactions.
Drug-Drug Interaction (DDI) Prediction. DDI prediction is evaluated using data from DrugBank [1], similar to 42). containing 1.95 million interaction pairs among 2,755 unique drugs. To assess cold-start capabilities, our cold-start drug query split involves test pairs where one drug is "unseen" (the query, absent from training interactions) and the other is "seen." BioCG's task is to generate this "seen" partner drug from the target catalog Tangs. which consists of all unique drugs in the dataset (as they are potentially "seen" partners if not the cold query) when given the unseen query drug.
Enzyme-Reacting Reaction Prediction The Enzyme- Reaction Prediction task seeks to identify the enzyme catalyzing given biochemical reaction. We use the CARE benchmark [41] for evaluation, which contains 61,766 reaction-enzyme pairings. Enzymes are often classified by EC numbers, where each EC number can represent class of enzymes performing the same function; thus, any enzyme in that EC class is a correct match. The dataset features 4,960 unique EC numbers, with enzymes typically represented by their protein sequences. We follow CARE's unseen reaction query split, where novel test reactions are input queries. BioCG's 's goal is to generate the correct protein sequence (corresponding to enzyme/EC number) from the target catalog, TFC TrC_proteins.
4.2 Baselines
To evaluate BioCG's performance rigorously, we compare against a range of established and state- of-the-art baseline methods specific to each of the three prediction tasks. Drug-Targes Interaction (DTI) Baselines: Baselines for DTI prediction on the BioSNAP dataset include classic machine learning models (Random Forest, SVM) and prominent deep learning architectures. These deep learning methods encompass Convolutional Neural Networks (CNNs) like DeepConv-DTi [16]
Graph Neural Networks (GNNs) such as GraphDTA (22), Transformer-bases models like MolTrans
13. Various attention or multimodal fusion approaches including DrugBAN [.. DLM-DTI 17]
Top-DTI [33), and DrugLAMP (20).
5

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Drug-Drug Interaction (DDI) Baselines: For binary DDI prediction on DrugBank, baselines include Logistic Regression (LR) and Random Forest (RF) using chemical structural similarity profiles (SSP). Deep learning comparators are DeepDDI 29 which uses SSPs with a deep neural
network; MR-GNN 40,, multi-resolution graph neural network; SSI-DDI (24, a knowledge-driven
model learning substructure interactions; and DDI-GCN
42). which employs Graph Convolutional
Networks on chemical structures.
Deezyee-Reaction Prediction Baselines: BioCG is compared against several established methods. These include a Random classification baseline, a DRFP-based reaction similarity baseline [27]. an LLM-based approach (GPT3 [25 41) and contrastive learning frameworks designed for this task,
namely CREEP 41 and CLIPZyme 21.
4.3 Implementation Details
Our BioCG framework uses Transformer architecture. It integrates frozen pre-trained models for entity representation: ESM2-650M [10) for protein sequences, Mol.Forme--XI [28] for molecules, and RXNFP 32 for reactions. The I-RVQ module utilizes k-means clustering for target discretization.
The main model is trained using the AdamW optimizer with a constant learning rate, employing early
stopping based on validation AUC. A I-layer Transformer meta-model calibrates generation statistics into interaction scores via a sigmoid binary classification head. Hyperparameters were optimized through greedy search with cross-validation to maximize validation AUC; detailed information is available in Appendix B Training is conducted on an L40 GPU (64GB), with the main model requiring approximately hours and the meta- model around minutes. The complete implementation of BioCG is publicly available at GitHub
Computational Complexity BioCG's computational profile has two phases. Offline Preprocessing involves encoding all target entities in T, running I-RVQ, and constructing the prefix trie Gr. This a one-time, potentially intensive cost. Online Inference for query e involves fast encoding of e and autoregressive generation via the constrained decoder. Trie lookups for masking add negligible overhead to the Transformer's s per-step complexity. The main memory consideration is the storage of the trie Gr. Its size scales with the total number of nodes, which depends [7] and the sequence length M, potentially becoming large for huge target sets. However, for moderately sized biochemical catalogs (e.g.. thousands to tens of thousands of proteins or known drugs), the trie often remains manageable relative to the size of large pre-trained encoders or the decoder itself. Addressing scalability to truly massive target sets is an area for future work (Section 6))
4.4 Statistical Analysis
Specific statistical tests were employed determine the statistical significance of performance differences between BioCG and baseline methods tailored to the metric type. The Two-Proportion Z-Test was used for proportion-basee metrics (e.g., Accuracy, Sensitivity, Specificity). This test. assesses whether the observed success rates of two models (BioCG and a baseline) are significantly different under the null hypothesis that their actual success rates are equal. DeLong's test (5) was used for AUC comparisons. This non-parametric method is designed to compare the AUCS of ROC curves when the models are evaluated on the same test set (leading to correlated results). It tests the null hypothesis that the AUCs of the two models are equal. For all tests, a p-value less than an alpha level of 0.05 was considered to indicate a statistically significant difference. This supports claims of BioCG's superiority, typically assessed using a one-tailed test where the alternative hypothesis is that BioCG's performance is greater than the baseline's.
The
5 Results
The evaluation of BioCG's performance across the different tasks utilizes metrics commonly es- tablished and reported in previous works within each domain, ensuring robust and comparable assessment. We report the Area Under the AUC, Sensitivity, and Specificity for Drug- Target Inter- action prediction conducted on a balanced dataset. Due to the imbalanced nature of the Drug-Drug Interaction dataset, our evaluation includes the AUPR and Accuracy, in addition to AUC, to provide comprehensive view of performance. For the Enzyme-Reatd Reaction Prediction task, where the goal is often to identify single correct enzyme for given reaction, Top-k Accuracy is the primary metric.

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Table Drug- Target Interaction Prediction performance on the BioSNAP dataset across seen, unseen protein, and unseen drugs scenarios. Results are reported as AUC, Sensitivity (Sens), and Specificity (Spec). Performance figures in bold indicate that the result is the best in its respective column and represents a statistically significant improvement (p < 0.05) over the next best baseline method, with significance determined as detailed in Section
Seen
Unseen Protein
Unseen Drug
Method
AUC
Sens
Spec
AUC
Sens
Spec
AUC
Sens
Spec
Random Forest
86.01
82.37
78.63
68.71
65.82
62.93
83,51
79.87
76.32
SVM
86.21
71.13
84.17
68.83
56,91
67.34
83.74
69.07
81.64
DeepConv-DTI
88.61
76.07
85.14
69.21
60.81
68.14
85.61
73.72
82.54
GraphDTA
88.73
74.51
85.42
70.43
59.62
68.32
85,82
72.31
82.87
MolTrans
89,52
81.83
83.19
71.42
65,43
66.52
85.63
79.46
80.63
DrugBAN
90.31
82.03
84.72
71.03
65.67
67.83
88.61
80.47
83.01
DLM-DTI
91.42
84.87
84.43
73.04
67.82
67.53
88.73
82.37
81.93
Top-DTI
93.91
86.63
85.73
75.01
69.31
68.67
91,25
84.03
83.17
DMM
91.73
84.43
85.57
73.23
67.54
68.42
89.06
81.92
82.93
BioCG (Our)
96.61
90.27
92.43
89.31
72.92
93.34
91.21
77.83
89.71
Drug-Target Interaction Prediction As shown in Table [ BioCG consistently outperforms all baseline methods across all three BioSNAP dataset splits. In the standard "seen" scenario, BioCG achieves an AUC of 96.61%. representing a statistically significant improvement of 2.7% over the previous state-of-the-art method, Top-DTI (93.91%). This improvement is also reflected in the sensitivity (90.27%) and specificity (92.43%) metrics. The most striking results are observed in the "unseen" scenarios. For unseen protein, where the model must predict interactions for entirely unseen protein targets. BioCG achieves an AUC of 89.3 99.31% %. a substantial 14.3% increase compared to the best baseline (Top-DTI, 75.01%). ()). This highlights BioCG's s strong generalization capability to novel protein entities. Similarly, in the unseen drug scenario. BioCG reaches an AUC of 91.21% %, surpassing most baselines, though Top-DTI also performs well here. The performance gains in unseen settings underscore the advantage of BioCG's constrained generative approach, which leverages the structured nature of the target space to make predictions for unseen entities more effectively than discriminative models reliant on embedding similarity.
Drug-Drug Interaction Prediction In predicting Drug-Drug Interactions on the DrugBank dataset, BioCG demonstrates superior performance across all evaluated metrics compared to established baseline methods. The results are detailed in Table 2 BioCG achieves an Accuracy of 77.23%, which is notable improvement over the best-performing baseline in this metric, DDI-GCN (74.05%). This indicates a higher overall correctness in classifying drug pairs as interacting or non-interacting. For the AUC metric, BioCG scores 84.12%, significantly surpassing the highest baseline score of 80.91% % achieved by MR-GNN. This highlights BioCG's enhanced ability to discriminate between positive and negative interaction pairs across various thresholds. Furthermore, BioCG obtains an AUPR of 72.16%, which is also higher than the best baseline AUPR of 69.47% (from MR-GNN), underscoring its robustness in scenarios with potentially imbalanced class distributions. These consistent improvements across ACC, AUC, and AUPR suggest that BioCG's constrained generative approach, modeling one drug as a query and generating the potential interacting partner from known set, effectively captures complex interaction patterns for DDI prediction.
ReeectetReationn Prediction For the task of Enzyme-Rcaction Reaction Prediction from biochemical reactions, evaluated on the CARE benchmark, BioCG demonstrates strong performance, particularly in accurately identifying the correct enzyme within the top few predictions. The results are presented in Table 3
BioCG achieves Top@1 accuracy of 67.93%, significantly outperforming the next best baseline, CREEP, which scored 60.32%. This represents substantial improvement in correctly identifying the exact enzyme as the top prediction. The advantage of BioCG extends across other Top-k metrics. For instance, BioCG achieves a Top@2 accuracy of 82.32% and a Top@3 accuracy of 89.21 %, surpassing CREEP (79.46% Top@2, 87.58% Top@3). While CREEP shows strong performance at higher k values, BioCG maintains competitive edge comparable performance, for example,
7

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Table Drug-Drug Interaction Prediction performance on the DrugBank dataset. Results are reported as Accuracy (ACC), AUC, and AUPR. Performance figures bold indi- cate that the result is the best in its respective column and represents a statistically significant improvement (p < 0.05) over the next best baseline method, with significance deter-
mined as detailed in Section 4.4
Method
ACC
AUC
AUPR
LR
51.73
64.57
50.69
RF
60.78
73.42
60.15
DeepDDI
29
72.24
80.86
69.03
MR-GNN
40
71.69
80.91
69.47
SSI-DDI
71.08
75.34
68.62
DDI-GCN
42
74.05
79.82
69.08
BioCG (Ours)
77.23
84.12
72.16
achieving 94.22% at Top@5 and 98.52% at Top@ 10. By Top@50, both BioCG and CREEP approach near near-perfect prediction accuracy (99.32% and 99.15%, respectively). These results highlight BioCG's capability to effectively learn the mapping from reactions to their corresponding enzymes by generating unique protein representations associated with EC numbers, leading improved prediction accuracy, especially at the critical top ranks.
Table 3: Enzyme-Reaction Prediction performance the CARE benchmark dataset, measured by Top-k Accuracy. Performance figures in bold indicate that the result is the best in its respective column and represents a statistically significant improvement (p < 0.05) over the next best baseline method. Top@k values for k>1 are not reported for the LLM and Random baselines, as these methods are designed to produce single best prediction, making ranked- list comparison methodologically distinct.
Method
Top@1
Top@2
Top@
Top@4
Top@5
Top
Top@50 50
Random 41
0.00
DRFP Similarity 27
59.39
70.01
78.46
80.92
81.98
85.56
93.42
CREEP 41
60.32
79.46
87.58
90.39
92.10
96.72
99.15
LLM 25
13.74
CLIPZyme
21
12.27
21.43
26.73
32.31
35.96
53.70
78.40
BioCGGOuur)
67.93
82.32
89.21
90.52
94.22
98.52
99.32
6
Ablations
To validate the contributions of BioCG's key components and design choices, we conducted compre- hensive ablation studies. These systematically evaluated the impact different pre-trained encoders and their training strategies, various methods for target entity representation, and alternative con- strained decoding approaches. All ablation experiments were performed on the BioSNAP dataset's "unseen protein" split. The findings are summarized in Table 4
Table 4: Ablation Study Results on BioSNAP Unseen Protein Dataset (AUC). Bold indicates the optimal configuration or best performing model in category for BioCG,
Pre-training Model
Pre-training Strategy
Target Representation
Constraint Strategy
ESM (protein) (89.31)
Tine-tuned (81.17)
Raw (82.06)
Unconstrained (75.21)
ProteinBERT (protein) (88.43)
Scratch (86.26)
Random (79.32)
Inference-only (77.43)
GearNet (protein) (87.54)
Frozen(BioCG) (89.31)
BPE (84.75)
Cross-Entron Loss (86. (86.13) Full (BioCG) (89.31)
MolFormer (molecule) (89.31)
I-RVQ-15 15 (89.31)
ChemBERTa (molecule) (88.29)
Log Proh BioCG (87.73)
I-RVQ-25 (88.03)

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The encoder integration strategy critically affected performance. Optimal AUC (up to 89.31%, Table 4 was achieved using frozen high-quality pre- trained encoders (e.g., ESM, MolFormer), which supply rich, stable features. Conversely, fine-tunin encoders substantially degraded performance (81.17% AUC). Significantly, training encoders from scratch still produced a respectable 86.26% AUC. This indicates that while pre-trained models are beneficial for top results, BioCG's core framework is robust, performing strongly even without them-an advantage if suitable pre-trained models are unavailable. Furthermore, the specific choice among tested pre-trained models (ESM, ProteinBERT, GearNet; MolFormer, ChemBERTa), when used frozen, exerted relatively minor influence AUC scores (ranging 87.54%-89.311 This observation reinforces BioCG's architectural robustness to the precise encoder selection, assuming a strong foundational representation.
Query Entity Encoders and Training Strategy:
Target Entity Representation: The method for creating discrete sequence representations of target entities for the decoder is crucial. Our I-RVQ with K = 15 clusters (I-RVQ-15) proved optimal (89.31% AUC). This significantly outperformed using raw biological sequences 82.06% AUC when adapted for comparable baseline) random sequence assignments (79.32% AUC), highlighting the need for structured, learned representations. Further comparisons demonstrated the superiority of I-RVQ-15: it outperformed Byte Pair Encoding (BPE) with 256 tokens (learned from raw sequences like SMILES or FASTA, yielding 84.75% AUC) and other I-RVQ configurations such as I-RVQ with K 25 clusters (I-RVQ-25, which achieved 88.03% AUC).
Constrained Decoding and Training Strategy: BioCG's approach of integrating constraints directly into training is vital. The full model with trie-guided decoding and information-weigher loss (Full (BioCG)) achieved 89.31% AUC. In contrast, unconstrained generation during training (75.21 % AUC) or applying constraints only at inference (77.43% AUC) performed substantially worse, confirming that the decoder must learn within the valid output space. Using a standard Cross-Entropy weighted loss, which better handles varying step complexity. Furthermore, the meta-model for calibrating scores (part of "Full (BioCG)") improved results over using direct log probabilities from the generator ("Log Prob BioCG," 87.73% AUC).
Loss 13% AUC) was less effective than our information- anfornation-weighreed
Conclusion
BioCG redefines biochemical interaction prediction as a constrained sequence generation task. By representing target entities as unique discrete sequences via I-RVQ and using a trie-guided constrained decoding mechanism, BioCG ensures valid outputs and focuses learning. An information -eeormation-weightedd training objective further boosts efficiency. Empirical results show BioCG's superiority across diverse tasks like DTL, DDL, and Enzyme- Reaction Prediction, especially in cold-start settings (e.g., 14.3% AUC rise on BioSNAP DTI for unseen proteins). This highlights the benefit of generating valid partners from known biochemical space, offering robust, data-efficient alternative discriminative models, particularly for novel entities.
Limitations BioCG, despite its advantages, has specific limitations. Firstly, a fundamental con- straint is the requirement that side of the interacting pair (the target entity) must be drawn from a pre-defined, closed set. This means our model is not directly applicable to scenarios where both interacting entities are novel, such as predicting interactions between two entirely new drugs. Fur- thermore, BioCG is designed for binary interaction prediction. Extending the framework to generate richer, quantitative outputs, such as binding affinity or the 3D structure of the binding complex, remains an important direction for future research. Secondly, the optimal performance of BioCG, as demonstrated in our ablation studies, is achieved when utilizing pre-trained embeddings. While the framework can operate with encoders trained from scratch or by using raw sequences for target representation, its effectiveness may be reduced in scientific domains where high-quality, relevant pre-trained models are not readily available.
Future Work Future research will focus on extending BioCG's capabilities and scope. One key direction is the exploration of alternative discretization techniques for target entities, beyond I-RVQ, and thorough investigation of how these different methods affect the model's predictive performance and representational efficiency. Another important avenue to extend the BioCG framework to other sub-fields within biochemical research and to assess its applicability and potential benefits in entirely

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different scientific disciplines, such as physics materials science. Furthermore, we aim to adapt and expand the framework accommodate more complex interaction types, for example, by enabling the prediction of interactions involving multiple entities simaltaneously.
10

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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification:
The abstract and introduction clearly state the problem, introduce BioCG as novel framework, outline its key technical components (constrained generation, I-RVQ, trie-guided decoding). highlight its advantages (data efficiency, cold-start performance), and summarize the main experimental results. These claims supported by the results presented Section and the discussion of limitations in Section 6
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
The paper includes dedicated subsection titled "Limitations" within the conclusion (Section(6), where discusses challenges related to the requirement for a closed set of target entities, the offline nature of 1-RVQ/trie construction for dynamic/large sets, potential trie memory scaling, and dependence on pre-trained encoder quality.
Justification:
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
The paper does not present formal theoretical results or proofs; its contributions are primarily empirical and methodological.
Justification:
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided not)?
Answer: (Yss]
Justification:
Section 4 describes the datasets and experimental splits. Section 4.3 and
Appendix B provide detailed information about the model architecture, I-RVQ configuration, training procedures, and hyperparameters. GitHub link is provided.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, described in supplemental material?
Answer: [Yes]
Justification:
The datasets used (BioSNAP, CARE) are publicly available and cited. The paper states that the complete implementation of BioCG is publicly available at the provided GitHub URL, which is intended to include instructions for reproducing results.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Section details datasets and data splits. Section 4.3 and Appendix cover model architecture, training, optimizer, and hyperparameter selection.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
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Answer: [Yes]
Justification: The paper assesses statistical significance for performance comparisons using the Pro-Prrppotion Z-Test for proportion-basee baased metrics and DeLong's test for AUC com- parisons (with a = 0.05), as described in Section 4.4| Statistically significant improvements are indicated in the results tables and discussion.
8. Experiments compute resources
Question: For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Answer: [Yes]
Justification: Section 4.3 specifies the type of GPU used (L40 GPU with 64GB memory) and
the approximate training time required for the main model (~~6 hours) and the meta-model (~5 minutes).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics http:p:://eerppps..c//uble/////e
Answer: [Yes]
Justification: The authors have reviewed the NeurIPS Code of Ethics and believe that the research presented in the paper conforms to its guidelines, without introducing significant ethical concerns or problems.
10. Broader impacts
Question: Does the paper discuss both potential positive socictal impacts and negative societal impacts of the work performed?
Answer: [NA]
The work focuses on improving in-silico methods for drug discovery and bio- chemical research. The primary societal impacts anticipated be positive advancements in these scientific areas. The authors assess that this specific research does not have direct, significant foresecable negative societal impacts requiring detailed discussion beyond the technical limitations.
Justification:
Safeguards
Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Answer: [NA]
The paper's core contribution is model for biochemical interaction prediction using publicly available biological data. The nature of the model and data does not pose generative
Justification:
high or direct risk for broad societal misuse in the manner of models or sensitive personal datasets.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: The creators of all existing assets used, including datasets (BioSNAP. Drug- Bank, CARE), pre-trained models (ESM, MolFormer, RXNFP), and libraries, are properly credited through citations. The authors confirm that the licenses and terms of use for these assets understood and respected.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
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Justification: The paper introduces the BioCG model and its implementation. The complete implementation stated be publicly available specified GitHub URL. The paper itself (Sections
4.3 and Appendix B) serves as primary documentation.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [NA]
Justification: The research presented in the paper does not involve crowdsourcing or research with human subjects.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: The research presented in the paper does not involve human subjects; therefore, IRB approval or equivalent review is not applicable.
Declaration LLM usage
Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: General purppse Large Language Models (LLMs) are not used as core compo- nents of the proposed BioCG methodology. Domain-speciffe pre-trained biological sequence encoders (like ESM, MolFormer) are utilized, which standard and distinct practice in the field. A general LLM (GPT-3) is included only as a baseline for comparison in one of the tasks, not as part BioCG itself.
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A Additional Experiments and Analyses
A.1 Impact of Encoder Scale on Performance
To further clarify the relationship between encoder quality and model performance, we conducted an additional ablation study using ESM2 models of varying sizes the BioSNAP unseen protein split. The results, presented in Table demonstrate clear, positive correlation: BioCG's performance robustly scales with the quality of the feature encoder. Notably, the architectural advantage of BioCG so significant that even when trained from scratch (see Table 4 it surpasses the previous fully-equipped SOTA. This analysis confirms that while better encoders provide better features, substantial performance lift comes from BioCG's novel constrained generative formulation.
Table 5: Performance (AUC) on the BioSNAP unseen protein split with ESM2 encoders of varying sizes.
Encoder
Parameters
AUC (unseen protein)
ESM2 (used paper)
650M
89.31%
ESM2
150M
87.14%
ESM2
35M
80.92%
ESM2
8M
74.33%
Top-DTI (Previous SOTA)
N/A
75.01%
A.2 Quantitative Failure Case Analysis
To better understand the model's failure modes, we performed an analysis of token index at occurs during the generation of an incorrect I-RVQ sequence. In the which the first prediction correspond to coarse-grained clusters representing broad biochemical RVQ structure, carlier tokens properties, while later tokens make fine-grainee distinctions between highly similar entities. Our analysis reveals that the majority of errors occur late in the sequence:
First Quarter of sequence: 12% of errors
Second Quarter of sequence: 18% of errors
Third Quarter of sequence: 29% of errors
Final Quarter sequence: % of errors
This distribution strongly suggests that BioCG correctly identifies the general biochemical "neighbor- hood" of the target and typically only fails at the final, most difficult step of distinguishing between very closely related entities. This indicates the model making meaningful "close misses" rather than random errors.
A.3 Conceptual Comparison with Contrastive Learning
While contrastive learning has proven effective for many representation learning tasks, its application to interaction prediction has inherent challenges that BioCG's generative framework is designed to overcome. Contrastive learning aims to learn general similarity metric by pulling positive pairs closer and pushing negative pairs apart in an embedding space. However, it can struggle with "hard negatives" negatives"-nnitics entiticcs that are structurally or biochemically very similar to a true interacting partner but do not actually interact. The model may incorrectly learn to place these entities close together, leading to false positives.
BioCG avoids this pitfall by reframing the problem. Instead of learning a global similarity score, learns sequential decision process. The trie-guided training forces the model make explicit choices between valid, and often highly similar, candidates at each step of the generation. The cnoomattion-weightee loss further sharpens this focus on the most ambiguous and difficult decision points (i.e., where the branching factor is high). This approach directly trains the model to learn the fine-grained, discriminative features necessary distinguish between "close-miss" candidates, key reason for its strong performance, especially in cold-start scenarios.
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Figure 3: Conceptual illustration comparing contrastive learning approach with BioCG's generative approach. (Left) Contrastive learning may cluster a "hard negative" close to the positive target the embedding space. (Right) BioCG learns decision path to distinguish the correct target from other valid but incorrect options within the trie.
A.4 Quantitative Analysis of Constrained Decoding Space
We analyze the structural properties of our trie-based generation space. Active Sequence Length: For a generation sequence, active positions are those with multiple valid next tokens requiring prediction. The active sequence length is:
Lractive
1 IT
Σ SE
[k: BFL()) > f}1
where BF (t) the branching factor at position k for sequence Mean Branching Factor (Active Tokens Only): The overall branching complexity for active positions is:
BF
1 Σ T
Σ
Bx(t)
22)
[1: (F 1H
k:BFA(()>
where the sum is taken only over positions with multiple valid choices.
40%
Molecules
Proteins
Molecules
Proteins
30%
Percentage
20%
10%
0%
2.5
5.0
10.0 2
8
5
10
15
Active Sequence Length
Mean Branching Factor
Figure 4: Constrained generation space structure analysis: Distribution active sequence lengths for molecules and proteins (left), and mean branching factors for molecules and proteins (right). Active sequence length represents the number of positions requiring actual prediction decisions, while branching factor indicates the number of valid token choices at each active position.
Each figure below shows the distribution of active sequence lengths (number of positions requiring actual prediction decisions) and branching factors (number of valid token choices at each active position)
40%
Molecules
Proteins
Molecules
30%
Percentage
20%
10%
10
20
10
20
10
15
20
Active Sequence Length
Branching
Figure 5: Active sequence lengths and branching factors for BioSNAP dataset with raw representa- tions.
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40%
Molecules
Proteins
Molecules
Proteins
30%
Percentage
20%
10%
0%
10
20
10
15
15
Active Sequence Length
Mean Branching Factor
Figure 6: Active sequence lengths and branching factors for DrugBank dataset with raw representa- tions.
Molecules
Proteins
Molecules
Proteins
30%
Percentage
20%
10%
0%
10
Active Sequence Length
Mean Branching Factor
Figure 7: Active sequence lengths and branching factors for DrugBank dataset with quantized representations.
B Hyperparameter Selection
Model hyperparameters were selected using a greedy search approach. This process involved iteratively tuning individual hyperparameters based their performance the validation set, aiming to maximize the validation AUC metric. The chosen values for the main model, meta-model, and training process are detailed below.
B.1 Main Model Hy perparaaaeee
Model hyperparameters were selected using a greedy search approach. This process involved iteratively tuning individual hyperparameters based their performance the validation set, aiming to maximize the validation AUC metric. The chosen values for the main model, meta- model, and training process detailed below. In addition to the parameters listed in the tables, the main Transformer model utilized a hidden dimension of 512. The encoder outputs were processed using global average pooling. The I-RVQ module employed the standard k-means algorithm from scikit-learn [5] with 15 clusters. Both the main model and the meta-model were trained using the A optimizer: The main model training for up 25,000 steps, with early stopping based performance on the validation AUC. The meta-model was trained similarly, with its own validation criteria (details in Table S). Training was performed main model training required approximately 6 hours, and the meta-model training took around minutes.
L40 GPU with 64GB of memory. The
Table 6: Main Model Hyperparameters
Parameter
Selected Value
Search
Number of Layers (Decoder)
2,4,6,8,12
Number of Attention Heads (Decoder)
8
2,4,8, 16
Feedforward Dimension (Decoder)
2048
4 * Hidden Size
Dropout
0.2
0.0.1,0.2.033.0.5
Bottleneck Dimension
128
-1.64,128,256.51
Pooling
True
Frue,
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B.2 Main Model Training Hyperparameters
The training process for the main generative model used these hyperparameters, selected based on validation performance:
Table Main Model Training Hyperparameters
Parameter
Selected Value
Search/Notes
Learning Rate
le-4
1e-3,1e-4,1e-5
LR Scheduler
Constant
Constant.Linear
Batch Size
64
16,64,256,107
B.3 Meta-Model Hyperparameters
The discriminative meta-model, used for calibrating generation statistics into interaction scores, was configured with the following hyperparameters:
Table 8: Meta-Model Hyperparameters
Parameter
Selected Value
Search/Notes
Number of Layers
1,2,3,4.5
Hidden Dimensions
128
32,64,128,256
Number of Attention Heads
4
2,4,8
Learning Rate
le-4
1e-3,1e-4,1e-3
LR Scheduler
Constant
Lonstant.LLnear
Batch Size
64
16,64,256, 1024
20


Reviews:
[{"review_id": "8fYXJtgCFN", "reviewer": "Reviewer_yZ9s", "summary": "The paper presents **BioCG**, a novel framework for biochemical interaction prediction that reframes the problem as a constrained sequence generation task. Unlike traditional discriminative models, BioCG uses Iterative Residual Vector Quantization (I-RVQ) to encode target biochemical entities as unique discrete sequences, and trains a Transformer decoder to generate valid interaction partners under strict constraints enforced by a trie-based decoding mechanism. This ensures outputs remain within a catalog of biochemically valid targets. An information-weighted loss further directs learning toward informative decisions. Extensive experiments across Drug-Target Interaction, Drug-Drug Interaction, and Enzyme-Reaction Prediction benchmarks show that BioCG achieves state-of-the-art performance. The approach is particularly notable for its strong generalization, efficient data usage, and compatibility with pre-trained molecular encoders.", "questions": "1. From Table 4, it seems that the quality of the pretrained encoders doesn’t affect the performance much, which is unexpected. Could you establish a clearer relationship between the quality of BioCG and its encoders—for example, by comparing models like ESM and MolFormer with different sizes?\n2. Can BioCG take 3D information of biological entities into account? Is this dependent on the encoders?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\nThe idea of formulating interaction prediction tasks as sequence generation is interesting, and BioCG effectively leverages the fundamental knowledge learned from pre-trained models in each domain.\n\n### Weaknesses\n\n1. The performance advantages of BioCG on unseen samples, especially in cold-start settings, may largely come from the pre-trained encoders, which might already include information about those entities. Therefore, comparisons between BioCG and models trained only on downstream datasets may not be entirely fair.\n2. Interaction learning has progressed beyond binary prediction to more informative outputs. For example, in drug-target interaction modeling, models like AlphaFold3 [1] and Boltz-2 [2] can predict binding complex structures and binding affinity. The current architecture of BioCG seems limited in handling such tasks, and this limitation should at least be discussed in the paper.\n3. In line 120, the authors explain \"the motivation for generating these learned I-RVQ codes instead of raw sequences.\" However, prior methods that use raw sequences for interaction modeling, such as 3DMolFormer [3], are not cited.\n4. Minor issues: The font size in Figure 2 should be increased for readability. Additionally, the section number of the first appendix section is missing. The checklist should also be placed after the references and before the appendix.\n\n[1] Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 2024.\n\n[2] Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction. 2025.\n\n[3] 3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery. ICLR 2025.", "was_revised": true, "final_justification": "The authors' detailed and comprehensive response has thoroughly addressed all of my concerns. Given the rebuttal’s effectiveness, I have updated my rating to 4.", "rebuttal": "We thank Reviewer yZ9s for their insightful feedback and constructive criticism. We are encouraged that the reviewer found our generative formulation interesting and effective. We address the identified weaknesses and questions below.\n\n### 1. On the Contribution of Pre-trained Encoders (Weakness 1 & Question 1)\n\nWe deliberately compared BioCG against contemporary state-of-the-art methods like DLM-DTI, DrugLAMP ,Top-DTI ,CREEP, CLIPZyme, which, like our model, leverage these same powerful pre-trained encoders. This was done precisely to ensure the comparison presented in our paper is fair and directly evaluates our framework's contributions over and above the use of standard pre-trained backbones.\n\nHowever, the primary driver of our performance gain is the BioCG framework itself, not just the shared encoders. The most direct evidence is in Table 4: our model with encoders trained from scratch on only the downstream dataset achieves an AUC of 86.26%. This result, obtained without any external pre-training knowledge, still outperforms the previous SOTA, Top-DTI (75.01%), by a significant margin of over 11 AUC points. This conclusively demonstrates the fundamental advantage of our constrained generative approach.\n\nTo further clarify the relationship between encoder quality and our model's performance, we conducted the new ablation study the reviewer suggested, using ESM2 models of varying sizes on the BioSNAP unseen protein split. The results demonstrate a clear, positive correlation, as expected:\n\n| **Encoder** | **Parameters** | **AUC (unseen protein)** |\n| :----------------------- | :------------- | :----------------------- |\n| ESM2 (used in paper)     | 650M           | **89.31%** |\n| ESM2                     | 150M           | 87.14%                   |\n| ESM2                     | 35M            | 80.92%                   |\n| ESM2                     | 8M             | 74.33%                   |\n| *Top-DTI (Previous SOTA)*| *N/A* | *75.01%* |\n\nThese results show two things:\n1.  BioCG's performance logically and robustly scales with the quality of the feature encoder.\n2.  The architectural advantage of BioCG is so significant that even when trained from scratch, it surpasses the previous fully-equipped SOTA.\n\nThis analysis confirms that while better encoders provide better features, the most substantial performance lift comes from BioCG's novel formulation. We will add this new table and analysis to the appendix.\n\n### 2. On the Scope of Interaction Prediction (Weakness 2 & Question 2)\n\nWe agree these are highly informative tasks. Our work focuses on the fundamental binary interaction prediction task, which remains critical for large-scale initial screening where structure or affinity prediction for millions of pairs can be computationally infeasible. \n\nRegarding 3D information, BioCG’s framework is model-agnostic and has already demonstrated its ability to leverage 3D structural information. In our ablation studies (section 6), we tested GearNet, an encoder that processes 3D protein structures, which achieved a strong AUC of 87.54%. This confirms the framework's versatility. Our choice to feature sequence-based encoders like ESM and MolFormer was deliberate, as these models are not only commonly used but also consistently deliver state-of-the-art results, making them the standard for this line of research.\n\nWe believe extending BioCG to these richer tasks is a promising direction for future work. For instance, the generated sequence could be designed to represent a discretized binding pose or affinity level. We will add a discussion on these limitations and future directions to the final paper, citing the suggested works.\n\n### 3. On Missing Citation and Minor Issues (Weakness 3 & 4)\nThank you for the reference; we will cite 3DMolFormer, clarifying that it generates novel 3D structures using continuous coordinates, whereas BioCG predicts interactions by generating a discrete code to select a known entity from a finite catalog. We will also correct the font size in Figure 2, add the missing appendix section number, and relocate the checklist to appear after the references as requested.\n\nWe hope these clarifications and new results adequately address the reviewer's concerns and reinforce the value of our contributions."}, {"review_id": "YjrIkbSE7E", "reviewer": "Reviewer_xzGP", "summary": "This paper proposes BioCG, a new approach for biochemical interaction prediction (e.g., drug-target, drug-drug, enzyme-reaction) by treating it as a constrained generation task over a fixed set of known targets. It uses an iterative vector quantization (I-RVQ) to encode discrete target codes, and a Transformer decoder with a trie constraint to generate only valid targets. An information-weighted loss is added to balance frequent and rare classes. The method is tested on multiple datasets and shows good performance, especially under cold-start settings.", "questions": "Please check the Weakness part.", "limitations": "N/A", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths\n- The idea of framing biochemical interaction prediction as constrained generation over a fixed set is natural and makes sense for this type of task.\n- The use of a trie is practical and ensures valid outputs without relying on post-filtering.\n- The method shows strong gains in cold-start settings (e.g., unseen proteins), with proper significance testing.\n- The ablation studies are clearly done and show the contributions of I-RVQ, constrained decoding, and the information-weighted loss.\n- The experiments cover a diverse set of tasks (DTI, DDI, enzyme-reaction), which supports the generality of the approach.\n\n\nWeakness\n1. While the method is clearly presented, most of the core components — I-RVQ, trie-based decoding, and meta calibration — are already known techniques. The main contribution seems to be how these are combined. It would help if the authors clearly stated which parts are novel and which are adapted from prior work. Also, could a simple retrieval or prefix-constrained generation baseline achieve similar results? A more direct comparison would strengthen the claim.\n2. The generated I-RVQ codes are learned, but the biological semantics of the codes are hard to understand. Can we trust that a predicted sequence has meaningful biochemical relevance? Is it possible to visualize or analyze what structural patterns the codes actually capture?\n3. It’s unclear how often the constrained decoder fails, even when the correct target exists in the trie. Some failure analysis would help.→ For example, how often does it predict a similar but wrong entity? Are these “close misses” or random errors?\n4. The model can only generate from a fixed set of known targets, which limits generalization. → This is briefly mentioned but not really evaluated. What happens if the input doesn’t match well with anything in the target set? Or would it be possible to combine this with an open-vocabulary model or retrieval-based fallback?", "was_revised": true, "final_justification": "I will maintain my score and thank the authors for their insightful replys such as failure cases.", "rebuttal": "We sincerely thank Reviewer xzGP for the positive and thorough assessment of our work. We are delighted the reviewer found our core idea natural, our experimental support strong, and our originality excellent. We address the valuable suggestions for strengthening the paper below.\n\n### 1. On Novelty and Comparison to Simpler Baselines (Weakness 1)\n\nWhile the components are adapted from existing techniques, their synthesis and application to create a data-efficient, constrained generative framework for biochemical interaction prediction is novel. Specifically:\n* Our I-RVQ is not standard RVQ; its iterative nature is a crucial adaptation to guarantee a unique code for every entity in the target catalog—a hard requirement for our framework.\n* Applying trie-constraints during training (not just inference) combined with our information-weighted loss is a novel approach that forces the model to learn the critical distinctions between valid biochemical options, which is key to its data efficiency.\n\n\nRegarding simpler baselines, we thank the reviewer for this suggestion and are glad to confirm that our original submission contains baselines that directly address these points:\n* **Retrieval Baseline:** Our evaluation on the Enzyme-Reaction benchmark (Table 3) includes CLIPZyme, a strong baseline that uses contrastive learning and retrieval. BioCG significantly outperforms it (e.g., 67.93% vs. 12.27% Top@1 accuracy).\n* **Prefix-Constrained Generation:** Our ablation study (Table 4) includes a model where constraints are applied only at \"Inference-only\", which is functionally a prefix-constrained generation baseline. This model achieves an AUC of 77.43%, which is massively outperformed by our full model (89.31% AUC) that integrates constraints directly into the training process.\n\nThese existing results demonstrate that our generative learning paradigm is substantially more powerful than simpler similarity-based retrieval or prefix-decoding approaches.\n\n### 2. On Interpretability of I-RVQ Codes (Weakness 2)\n\nThe biological semantics are not learned by the I-RVQ codes from scratch, but are inherited from the powerful pre-trained embeddings (e.g., from ESM) that serve as the input to the quantization process. The rich biological information and interpretability of these pre-trained representations have been extensively studied and validated in the literature across many downstream tasks (e.g., \"A Survey on Protein Representation Learning: Retrospect and Prospect\").\n\n#WE WILL ADD VISSUALTION TO THE APPENDIX - SIMILER TO TODO\n\nThe role of our I-RVQ module is to learn a structured, hierarchical partitioning of this already meaningful embedding space. It learns the decision boundaries that group biochemically similar entities, but the underlying meaning that makes these groupings relevant comes directly from the state-of-the-art encoders we employ.\n\n### 3. On Failure Analysis (Weakness 3)\n\nThank you for this excellent suggestion. We performed a quantitative analysis of the failure cases by examining at which token index in the generated I-RVQ sequence the first error occurs. The results show a clear trend:\n* **12%** of errors occur in the first quarter of the sequence.\n* **18%** of errors occur in the second quarter.\n* **29%** of errors occur in the third quarter.\n* **41%** of errors occur in the final quarter (with 13% in the very last token).\n\nThis distribution is highly informative. In the RVQ structure, earlier tokens correspond to coarse-grained \"big picture\" clusters, while later tokens make fine-grained distinctions between highly similar entities. The fact that most errors occur late in the sequence indicates that BioCG correctly identifies the general biochemical \"neighborhood\" of the target and typically only fails at the final step of distinguishing between very closely related entities. This confirms the model is making meaningful \"close misses\" rather than random guesses. We will add this analysis and discussion to the appendix.\n\n### 4. On Generalization to Open Sets (Weakness 4)\nThis is a key limitation that we acknowledge, and one we are transparent about in the Conclusion of our paper (Section 5). However, we wish to clarify that this closed-set approach is a very common and practical setting for many key problems in computational biochemistry, making this limitation less restrictive in practice than it might initially appear.\n\nThank you again for the constructive feedback, which will significantly improve our paper."}, {"review_id": "eliQigHquW", "reviewer": "Reviewer_femx", "summary": "The paper rethinks the approach to predicting whether two biomolecules interact. Previous methods mostly focus on contrastive learning, while BioCG, the method proposed here, reframes the problem as generating a target molecule conditioned on the query molecule, with the generative process designed to only produce samples from a predefined set of targets. The results appear strong and the idea is novel, but the clarity of the paper could be improved.", "questions": "1. The paper evaluates enzyme-reaction prediction on Task 2 of the CARE benchmark. What would the performance be on Task 1, “Enzyme Classification,” compared to CREEP, which may be more interesting in practice?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n- State-of-the-art performance across three well-established benchmarks\n- Ablation studies demonstrating the key components of BioCG design\n\n**Weaknesses**\n\n1. Clarity of the paper could be significantly improved. For example:\n  - [Line 11]. The abstract and introduction keep using the term “biochemically valid,” which is unclear. Please clarify that all outputs are generated from the predefined set of targets.\n  - [Line 36]. The following motivation is not entirely accurate because EC numbers do not capture all potential enzyme activities and only provide a taxonomy for existing ones: “Predicting an enzyme’s function for a novel reaction involves selecting from a closed set of Enzyme Commission (EC) numbers.”\n  - [Figure 1]. Panel A shows numbers next to the tree nodes, but Panel B shows letters. If I understand correctly, they represent the same categories and should be denoted consistently. Panel C contains a typo: “with Constrained.”\n  - [Figure 2]. The visualization is not very informative and does not help much in understanding the method. It could replaced with a figure comparing the proposed generative method against conventional contrastive learning (please see Weakness 2).\n  - [Line 148]. What is epsilon?\n  - [Line 164]. It seems “typically” should be left out, as the further text suggests it is “always” the case.\n  - [Table 3]. Why do some rows have missing values? At least, a random baseline could be evaluated under higher Top@k values.\n  - [Table 4]. In the left column, “Pre-training Model,” protein and small molecule encoders seem to be mixed together. It is unclear which is which.\n2. The intuition behind the effectiveness of the proposed approach compared to contrastive learning is not fully clear. The paper would be much stronger if it provided case studies or examples where contrastive learning fails but the proposed approach does not. It could also be helpful to discuss the pitfalls of contrastive learning to provide intuition for why constrained generation works better.", "was_revised": true, "final_justification": "I am keeping my \"Borderline accept\" score. I acknowledge the strong performance of BioCG on several benchmarks. Nevertheless, the performance gain comes with limitations, such as the inability to model a joint latent space between interaction modalities (e.g., Question 1).", "rebuttal": "We are grateful to Reviewer femx for their feedback and for recognizing the strength of our results and the novelty of our idea. We apologize for the lack of clarity in some areas and have addressed each point below to improve the paper.\n\n### 1. On Clarity of the Paper (Weakness 1)\n\nThank you for the detailed and actionable list of clarity issues. We will revise the paper thoroughly based on your feedback.\n- **“biochemically valid”**: We will revise this throughout the paper to the more precise phrasing: “ensuring all outputs correspond to an entity within the pre-defined target catalog.”\n- **EC number motivation**: You are correct. We will revise the sentence to be more accurate, e.g., “predicting an enzyme's function for a novel reaction often involves selecting a known enzyme, which is typically cataloged by its sequence and classified by an EC number, from a finite set such as the human proteome.”\n- **Figure 1**: We apologize for the inconsistency and typo. We will correct Figure 1 to use consistent notation in panels A and B and fix the typo “with Constrained” in panel C.\n- **Figure 2**: We appreciate the feedback. Figure 2's goal is to provide a simple visual intuition for the iterative quantization of I-RVQ, not the full framework. Per your suggestion, we will add a new figure to the appendix that provides a conceptual comparison between our generative approach and conventional contrastive learning to better highlight our contribution.\n- **Epsilon (line 148)**: Epsilon ($ \\epsilon $) is a small constant (e.g., 1e-9) added for numerical stability to avoid taking the log of zero when the branching factor is 1. We will explicitly state this in the text.\n- **“typically” (line 164)**: You are correct; this should be stronger. We will adjust the wording for accuracy.\n- **Missing values in Table 3**: Thank you for this question. The missing values for the LLM and Random baselines are due to their method of operation. Generative models are designed to produce a single output, not a ranked list of top k candidates. Generating multiple samples is not equivalent to how other methods calculate Top@k scores, making a direct comparison for k>1 methodologically unfair. For this reason, we retained only the reported Top@1 result from the source. We will add a note clarifying this in the final paper.\n- **Table 4 encoders**: This is a great point. We will reorganize Table 4 with clear sub-headings (“Protein Encoders”, “Molecule Encoders”) to eliminate ambiguity.\n\n### 2. On Intuition vs. Contrastive Learning (Weakness 2)\n\nThis is a critical distinction. Contrastive learning learns a general similarity metric, often struggling with \"hard negatives\"—entities that are structurally similar to the correct partner but do not interact. In contrast, BioCG's trie-guided generative approach inherently forces the model to learn fine-grained distinctions by choosing between valid, often highly similar, candidates at each step. Our information-weighted loss further sharpens this focus on the most difficult decisions.\n\nTo make this point concrete, we will add this discussion to the paper and include a new appendix section with intuitive examples and a case study illustrating this key advantage.\n\n### 3. On CARE Benchmark Task 1 (Question 1)\n\nThis is an excellent question that highlights a core design principle of our framework. BioCG is not directly applicable to CARE's Task 1 (Enzyme Classification) because of the nature of the target entities.\n\nOur framework is built on the premise that both the query and target entities are complex biochemical structures that can be represented by meaningful sequences (e.g., FASTA for proteins, SMILES for molecules) and processed by powerful pre-trained encoders. This is how the model captures the rich information needed for prediction.\n\nIn CARE's Task 1, the target is an Enzyme Commission (EC) number, which is a hierarchical classification label (e.g., '1.1.1.1'), not a structural sequence. As such, an EC number cannot be meaningfully processed by the foundation models (like ESM or MolFormer) that are integral to our method. For this reason, we focused our evaluation on tasks like CARE Task 2 (reaction-to-enzyme sequence), where both interacting partners are complex entities that fit our model's requirements.\n\nWe believe these revisions will significantly improve the paper's clarity and impact."}, {"review_id": "6uBi0PXg1K", "reviewer": "Reviewer_fZBQ", "summary": "In this paper, the authors propose a framework named BioCG to solve the biochemical interaction prediction task via a generative model. It uses pre-trained encoders to get molecule embeddings and quantize them via I-RVQ. A trie is then built based on all target entities to constrain the autoregressive decoder to generate biochemically valid results. Another small model is then used to convert the outputs into a single prediction score, reflecting the interaction potential. A hierarchical loss function is also proposed to weight the cross-entropy by branching factors. Experiments on drug-target interaction, drug-drug interaction, and enzyme-reaction prediction tasks indicate the effectiveness of the proposed framework.", "questions": "please refer to weakness", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n- The idea of combining techniques such as quantization and hierarchical constraint to protein analysis tasks is very interesting.\n- The performance is good.\n\nWeaknesses:\n- The primary weakness is the lack of specific ablation studies that would help to isolate the contributions of the novel components. To fully substantiate the claims, I would recommend the authors include the following comparisons:\n  - The proposed I-RVQ v.s. a traditional RVQ;\n  - The Information-Weighted Loss v.s. a standard cross-entropy loss;\n  - The score generation meta model v.s. the baseline log probability approach.\n- It is better to include some related works about the proposed method, such as quantization and trie building, to help readers understand the context.\n- If the I-RVQ is just using RVQ to quantize all of the targets, I definitely would not recommend giving it a new name.", "was_revised": true, "final_justification": "The authors' response has clarified most of my concerns. However, the central point regarding the justification for I-RVQ in comparison to standard RVQ remains insufficiently addressed. I believe this is the most significant unresolved issue, as it is directly tied to the novelty and impact of the work. Overall, I like the idea but I also maintain that a full revision of the manuscript is needed to make the paper's methodology and results easier to follow.", "rebuttal": "We thank Reviewer fZBQ for their positive feedback on our idea and performance. We appreciate the specific suggestions for improving the ablation studies and clarifying our contributions.\n\n### 1. On Specific Ablation Studies (Weakness 1)\n\nThe reviewer raises excellent points about isolating the contributions of our key components. We are happy to clarify that these ablations are already present in our original submission, as detailed below.\n* **I-RVQ vs. Traditional RVQ:** A fixed-layer RVQ is suboptimal: using too few layers causes code ambiguity (e.g., 10% of our protein targets become duplicates), which limits performance, while using too many layers is computationally wasteful as the extra steps offer no learning. Our I-RVQ method iteratively finds the minimal number of layers required for uniqueness, providing a simple and efficient way to enforce this critical property for our framework. We will clarify this distinction and its motivation in the revised manuscript.\n    Because a direct comparison with a method that produces ambiguous outputs is conceptually challenging, we instead validated our I-RVQ representation strategy against several other well-defined alternatives in our original submission (Table 4). Our I-RVQ-15 approach (89.31% AUC) significantly outperformed using raw biological sequences (82.06% AUC), standard BPE tokenization (84.75% AUC), and random sequence assignments (79.32% AUC), demonstrating the effectiveness of our chosen representation scheme.\n\n\n* **Information-Weighted Loss vs. Standard Cross-Entropy:** This comparison is included in Table 4. The row \"No Info. Weighting” represents training with a standard cross-entropy loss, which achieves an AUC of 86.13%. This is significantly lower than the 89.31% AUC achieved by our full model (“Full (BioCG)”), demonstrating a +3.18 AUC improvement from our information-weighted loss.\n\n* **Meta-Model vs. Log Probability Score:** This comparison is included in Table 4. The row “Log Prob BioCG” shows the performance using the simple log probability score from the generator, yielding an AUC of 87.73%. Our full model, which uses the meta-model for calibration, achieves 89.31%. The +1.58 AUC gain validates the effectiveness of the meta-model.\n\n\n\n### 2. On Related Work (Weakness 2)\n\nWe will add a dedicated subsection to the \"Related Work\" section (Sec. 2) discussing the background of vector quantization in representation learning. We would also like to clarify that we already discuss the use of trie-based decoding in the \"Constrained Generation\" subsection, which covers its use for enforcing constraints during training and inference.\n\n### 3. On the Naming of I-RVQ (Weakness 3)\n\nWe chose the name Iterative Residual Vector Quantization (I-RVQ) to highlight the specific, crucial adaptation we made to the standard RVQ process for our framework to function. As we state in the paper, \"The key adaptation defining I-RVQ lies not in the per-layer quantization step but in its overall iterative application and objective\". The novelty is the iterative application of quantization layers with the explicit goal of achieving 100% representation uniqueness across a finite target set. As our ablation studies show, this uniqueness is essential for our framework's performance. We believe this distinction is significant enough to warrant a specific name, and we will further clarify this motivation in Section 3.1 of the final manuscript.\n\nWe hope that these clarifications and references to the existing results in our paper fully address the reviewer's concerns and underscore the novelty and effectiveness of our proposed components."}]

Author rebuttal:

Meta-review: This paper introduces BioCG: a framework for biochemical interaction prediction that casts the problem as a generative task instead of a discriminative one. Experiments across various tasks (Drug-Target Interaction, Drug-Drug Interaction, and Enzyme-Reaction Prediction) show favourable performance. 

 

On the positive side, reviewers praised the interesting formulation of the method, and strong performance across a diverse set of downstream tasks. They noted good generalization capability, and potential compatibility with various pretrained encoders. Finally, most reviewers also liked the extensive ablation studies. Overall, the paper stood out in terms of strong empirical performance, as well as novelty of the approach.


On the other hand, reviewers raised several questions about clarity and presentation, but they deemed authors' attempts to resolve them during rebuttal period satisfactory. Moreover, Reviewer yZ9s raised a point around interaction prediction beyond binary outputs, which is supported by modern models of biological complexes such as AlphaFold 3 and Boltz-2, but not currently supported by BioCG. Authors have acknowledge this limitation in the current form of the framework, and considered this as a direction for future work. 

 

After the rebuttal period, all reviewers agreed that this paper should be accepted, with Reviewer xzGP championing the work with a stronger acceptance score. Siding with the unanimous reviewer opinion, I also recommend acceptance. I encourage authors to incorporate feedback from reviewer discussion into the camera-ready version of the paper.
Decision: Accept (poster)