Title: BioCG: Constrained Generative Modeling for Biochemical Interaction Prediction
Venue: NeurIPS 2025
Accepted: yes
Keywords: Constrained Generative Modeling

Drug-Target Interaction (DTI)

Drug Discovery

Enzyme-Catalyzed Reactions
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.


Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:
- The idea of combining techniques such as quantization and hierarchical constraint to protein analysis tasks is very interesting.
- The performance is good.

Weaknesses:
- 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:
  - The proposed I-RVQ v.s. a traditional RVQ;
  - The Information-Weighted Loss v.s. a standard cross-entropy loss;
  - The score generation meta model v.s. the baseline log probability approach.
- It is better to include some related works about the proposed method, such as quantization and trie building, to help readers understand the context.
- If the I-RVQ is just using RVQ to quantize all of the targets, I definitely would not recommend giving it a new name.

QUESTIONS:
please refer to weakness

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-28

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\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?\n\nQUESTIONS:\nPlease check the Weakness part.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}