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arxiv: 2603.21942 · v4 · submitted 2026-03-23 · ⚛️ physics.chem-ph · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Suiren-1.0 Technical Report: A Family of Molecular Foundation Models

Junyi An , Xinyu Lu , Yun-Fei Shi , Li-Cheng Xu , Nannan Zhang , Chao Qu , Yuan Qi , Fenglei Cao
Authors on Pith no claims yet

Pith reviewed 2026-05-15 01:10 UTC · model grok-4.3

classification ⚛️ physics.chem-ph cs.AI
keywords molecular foundation modelsconformation compression distillationSE(3)-equivariantquantum property predictionmolecular graphsDFT pre-trainingintermolecular interactions
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The pith

Suiren-1.0 distills 3D molecular conformations into lightweight 2D foundation models via a new compression process.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents Suiren-1.0 as a family of three molecular foundation models that connect detailed three-dimensional conformational data with efficient two-dimensional representations. A large base model is pre-trained on seventy million density functional theory calculations using equivariant architectures for quantum property tasks. A dimer variant continues training on intermolecular interaction data, after which a diffusion-based distillation step compresses the three-dimensional information to produce a compact model that works from standard SMILES strings or molecular graphs. This pipeline is intended to deliver accurate predictions across chemistry tasks while reducing the computational cost of handling explicit three-dimensional structures.

Core claim

Suiren-1.0 bridges 3D conformational geometry and 2D statistical ensemble spaces through pre-training on a 70M-sample DFT dataset with SE(3)-equivariant architectures, continued pre-training on 13.5M intermolecular samples, and Conformation Compression Distillation that produces the lightweight Suiren-ConfAvg variant capable of generating high-fidelity representations directly from SMILES or molecular graphs.

What carries the argument

Conformation Compression Distillation (CCD), a diffusion-based framework that converts complex 3D structural representations into 2D conformation-averaged representations.

Load-bearing premise

The Conformation Compression Distillation process preserves high-fidelity 3D structural information in the resulting 2D representations without meaningful loss for downstream tasks.

What would settle it

A benchmark test in which the Suiren-ConfAvg model shows clear performance drops relative to explicit 3D conformation models on properties that depend strongly on specific molecular geometries, such as certain stereoselective reaction outcomes or conformational energy differences.

read the original abstract

We introduce Suiren-1.0, a family of molecular foundation models for the accurate modeling of diverse organic systems. Suiren-1.0 comprising three specialized variants (Suiren-Base, Suiren-Dimer, and Suiren-ConfAvg) is integrated within an algorithmic framework that bridges the gap between 3D conformational geometry and 2D statistical ensemble spaces. We first pre-train Suiren-Base (1.8B parameters) on a 70M-sample Density Functional Theory dataset using spatial self-supervision and SE(3)-equivariant architectures, achieving robust performance in quantum property prediction. Suiren-Dimer extends this capability through continued pre-training on 13.5M intermolecular interaction samples. To enable efficient downstream application, we propose Conformation Compression Distillation (CCD), a diffusion-based framework that distills complex 3D structural representations into 2D conformation-averaged representations. This yields the lightweight Suiren-ConfAvg, which generates high-fidelity representations from SMILES or molecular graphs. Our extensive evaluations demonstrate that Suiren-1.0 establishes state-of-the-art results across a range of tasks. All models and benchmarks are open-sourced.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces Suiren-1.0, a family of molecular foundation models with three variants: Suiren-Base (1.8B parameters pre-trained on a 70M-sample DFT dataset using spatial self-supervision and SE(3)-equivariant architectures for quantum property prediction), Suiren-Dimer (continued pre-training on 13.5M intermolecular interaction samples), and Suiren-ConfAvg (a lightweight model obtained via the proposed Conformation Compression Distillation (CCD) diffusion-based process that maps 3D conformational ensembles to 2D conformation-averaged representations from SMILES or graphs). The central claim is that this algorithmic framework bridges 3D geometry and 2D ensemble spaces, with extensive evaluations establishing state-of-the-art results across tasks; all models and benchmarks are open-sourced.

Significance. If the SOTA performance claims and the fidelity of the CCD 3D-to-2D distillation are substantiated with quantitative benchmarks, this work would offer a meaningful advance in molecular foundation modeling by enabling efficient inference on 2D inputs while retaining accuracy on quantum properties and intermolecular interactions, potentially broadening accessibility for large-scale organic system simulations.

major comments (2)
  1. [Abstract] Abstract: The assertion that 'Suiren-1.0 establishes state-of-the-art results across a range of tasks' is unsupported by any quantitative metrics, baselines, error bars, evaluation protocols, or specific task results, which is load-bearing for the central performance claim and prevents verification of the reported superiority.
  2. [Conformation Compression Distillation] Conformation Compression Distillation section: The claim that CCD yields 'high-fidelity' Suiren-ConfAvg representations from 3D structures lacks any supporting reconstruction metrics (e.g., RMSD to original conformers, KL divergence on property distributions) or ablation studies demonstrating parity with Suiren-Base on 3D-sensitive benchmarks; without these, the bridging mechanism cannot be distinguished from effects of dataset size or architecture alone.
minor comments (1)
  1. [Abstract] Abstract: The training dataset sizes (70M for Base, 13.5M for Dimer) and parameter count (1.8B) are stated but would benefit from a summary table comparing the three variants' architectures, pre-training objectives, and intended use cases for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which help clarify how to better substantiate our central claims. We address each point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The assertion that 'Suiren-1.0 establishes state-of-the-art results across a range of tasks' is unsupported by any quantitative metrics, baselines, error bars, evaluation protocols, or specific task results, which is load-bearing for the central performance claim and prevents verification of the reported superiority.

    Authors: We agree that the abstract should contain concrete quantitative support for the SOTA claim. In the revised manuscript we will insert the key performance numbers (with baselines, error bars, and a concise statement of the evaluation protocol) directly into the abstract so that the superiority statement can be verified without reading further sections. revision: yes

  2. Referee: [Conformation Compression Distillation] Conformation Compression Distillation section: The claim that CCD yields 'high-fidelity' Suiren-ConfAvg representations from 3D structures lacks any supporting reconstruction metrics (e.g., RMSD to original conformers, KL divergence on property distributions) or ablation studies demonstrating parity with Suiren-Base on 3D-sensitive benchmarks; without these, the bridging mechanism cannot be distinguished from effects of dataset size or architecture alone.

    Authors: We accept that explicit fidelity metrics and ablations are needed to isolate the contribution of CCD. In the revised section we will report RMSD between original and reconstructed conformers, KL divergence on property distributions, and ablation results comparing Suiren-ConfAvg against Suiren-Base on 3D-sensitive tasks. These additions will allow readers to distinguish the distillation effect from dataset or architecture differences. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivations rely on external DFT datasets and standard equivariant architectures

full rationale

The paper describes pre-training Suiren-Base on an external 70M-sample DFT dataset using spatial self-supervision and SE(3)-equivariant architectures, with Suiren-Dimer using continued pre-training on intermolecular samples and CCD as a proposed diffusion-based distillation step. No equations or claims reduce any prediction to a fitted input by construction, no self-citations provide load-bearing uniqueness theorems, and no ansatz is smuggled via prior work. The chain is self-contained against external benchmarks and data sources, consistent with the reader's assessment of score 2.0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Based solely on the abstract, the claims rest on standard DFT data as input, SE(3)-equivariant architectures (domain standard), spatial self-supervision (standard technique), and the newly proposed CCD distillation method. No explicit free parameters, unstated axioms, or invented physical entities are detailed.

invented entities (1)
  • Conformation Compression Distillation (CCD) no independent evidence
    purpose: Distill complex 3D conformational representations into 2D conformation-averaged representations for efficient use
    New framework introduced in the paper; no independent evidence or external validation is mentioned in the abstract.

pith-pipeline@v0.9.0 · 5534 in / 1196 out tokens · 46676 ms · 2026-05-15T01:10:48.877620+00:00 · methodology

discussion (0)

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

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supports
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extends
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Reference graph

Works this paper leans on

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