Title: 3D Interaction Geometric Pre-training for Molecular Relational Learning
Venue: NeurIPS 2025
Accepted: yes
Keywords: molecular science

molecular relational learning
Abstract: Molecular Relational Learning (MRL) is a rapidly growing field that focuses on understanding the interaction dynamics between molecules, which is crucial for applications ranging from catalyst engineering to drug discovery. 
Despite recent progress, earlier MRL approaches are limited to using only the 2D topological structure of molecules, as obtaining the 3D interaction geometry remains prohibitively expensive.
This paper introduces a novel 3D geometric pre-training strategy for MRL (3DMRL) that incorporates a 3D virtual interaction environment, overcoming the limitations of costly traditional quantum mechanical calculation methods. 
With the constructed 3D virtual interaction environment, 3DMRL trains 2D MRL model to learn the global and local 3D geometric information of molecular interaction.
Extensive experiments on various tasks using real-world datasets, including out-of-distribution and extrapolation scenarios, demonstrate the effectiveness of 3DMRL, showing up to a 24.93% improvement in performance across 40 tasks.
Our code is publicly available at https://github.com/Namkyeong/3DMRL.


Peer reviews:
comments: SUMMARY:
The paper introduces 3DMRL, a novel pre-training framework for Molecular Relational Learning (MRL) that incorporates 3D geometric information into 2D models. It constructs a virtual interaction environment to simulate molecular interactions and uses two pre-training strategies: global geometry learning and local relative geometry learning. The approach significantly improves model performance, with up to 24.93% improvement in various molecular interaction tasks.

STRENGTHS AND WEAKNESSES:
# Strengths

1. Novel Contribution: This paper introduces an innovating method for incorporating 3D geometric information into MRL. And the concept of constructing a virtual interaction environment to simulate molecular interactions is an effective and interesting solution to overcome the high computational costs of traditional methods.

2. Effective and rational framework: The use of two complementary pretraining strategies is a good approach for improving the representation of molecular interactions at both macro and micro levels.

3. Comprehensive experimental validation: Extensive experiments across multiple real-world datasets and tasks, which demonstrate that the method consistently improves performance. And this manuscript also covers ablation study, efficiency study, and so on.

4. This manuscript is well-written and logically organized.

# Weaknesses

In my opinion, this manuscript has no significant flaws. The few shortcomings are: this manuscripts lacks of discussions of the proposed approach and theoretical justification.

QUESTIONS:
See Weaknesses above.

LIMITATIONS:
No. The authors did not demonstrate the limitations of their work.
TITLE: Official Review
CLARITY: 4
IMPACT: 4
ORIGINALITY: 3
DATE: 2025-07-01

comments: SUMMARY:
The paper introduces 3DMRL, a novel 3D geometric pre-training strategy for Molecular Relational Learning (MRL). Traditional MRL approaches are limited to 2D topological structures due to the high cost of acquiring 3D interaction geometry. 3DMRL overcomes this by constructing a 3D virtual interaction environment, allowing 2D MRL models to learn global and local 3D geometric information without expensive quantum mechanical calculations.

STRENGTHS AND WEAKNESSES:
Strengths:

Proposes a cost-effective 3D virtual environment simulating real molecular interactions by arranging small molecules around a larger molecule, replacing expensive quantum calculations.

Dual Pre-training Strategies: SE(3)-Invariant Global Learning and SE(3)-Equivariant Local Learning.

Extensive experiments (15 runs per model), ablation studies, and sensitivity analyses validate the approach's robustness and generalizability.

Weakness:

Relies on RDKit-generated 3D conformations, which may introduce errors for molecules without experimental structures.

Lacks formal mathematical proof of SE(3) equivariance's necessity, relying primarily on empirical results.

QUESTIONS:
1. Incorporating molecular dynamics trajectories to simulate dynamic interactions beyond static configurations may be virtual environment realism.

LIMITATIONS:
Yes, In Section 5.3.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a training strategy for molecular relational learning that involves a 2D and a 3D molecule encoder, where the interaction geometry of two molecules is provided with a computationally efficient approximation to enhance training with notions of the 3D space that molecules live in, while avoiding computationally expensive simulations to obtain the data. The work presents improved performance over baselines, ablation studies, and sensitivity analyses.\n\nSTRENGTHS AND WEAKNESSES:\nThis is a very solid contribution to a relevant problem with (mostly) clear description and notation, convincing experimental setups, and good improvement over existing baselines. I especially appreciated the given rationale of the virtual environment in 4.1, the ablation studies, and the sensitivity analysis. I have a few clarifying questions, but would recommend to accept as this work provides an easily applicable improvement that equips molecular prediction models with general 3D geometric information.\n\nQUESTIONS:\n- The rationale for the local geometry learning is not entirely clear to me. If I understand correctly, the target relative geometry $f^i_k$ was constructed randomly in the first place (compare definitions of $R^2$ and $R^1$). Aren’t you learning random noise here?\n- What is the added computational expense for constructing the environment and how does it scale with the involved parameters?\n- Would you generally recommend to set the number of small molecules $n$ to 5? Does that depend on factors, such as the size of the molecules?\n\nThe following questions are speculative and more out of curiosity, I don’t require them to be answered:\n\n- Is this method intended for solute-solvent interactions only or do you expect it to be applicable to eg molecules of similar size or more than two interaction partners?\n- Do you expect your method to scale to large biomolecules such as proteins? (both conceptually and computationally)\n- I am curious whether the random virtual environment might already provide sufficient information to model real-world interactions (as you argue in 4.1) and what the performance delta to an expensive QM method would be. How do you think (qualitatively) would your method using the virtual environment compare to using QM-derived data?\n\nLIMITATIONS:\nThe authors discuss a limitation where molecules are placed on top of each other. I would recommend to also touch on the \"physicality\" of the virtual environment and the limitations of this approximation, as well as computational complexity of its construction (including scaling with various parameters).", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
This paper proposes 3DMRL, a pre-training framework that teaches 2 D molecular-relational models to encode 3 D interaction geometry by (a) constructing a randomized “virtual interaction environment” and (b) optimizing global contrastive and local SE(3)-equivariant losses. The expriments show consistent improvements (up to ≈25 % RMSE reduction) over five MRL backbones on eight datasets, plus better OOD generalization than prior 3 D pre-training methods.

STRENGTHS AND WEAKNESSES:
Strength
1. The expriments are thorough on 40 downstream tasks with vairous ablation and OOD tests.
2. Method pipeline and algorithm pseudocode are easy to follow, the methods are rational.
3. The proposed component virtual interaction geometry is interesting.
4. The paper is well-written, with sound motivaitons.

Weakness
1. Virtual environment lacks physical validation, where random Gaussian placement may create unrealistic interactions despite collision checks.
2. Comparisons against 3 D encoders rely on ETKDG-generated conformers, weakening those baselines.
3. The pre-training strategies are not novel, global contrastive loss similar to Infomax, thus is very incremental.
4. The efficiency of the method might be a concern.

QUESTIONS:
1. How often do generated configurations violate typical inter-molecular distance/angle distributions seen in MD trajectories?
2. As mentioned above about the efficiency concern, can the authors provide the efficiency experiment?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-04