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

arXiv: https://arxiv.org/abs/2603.21942 · HTML: https://mlsanity.com/paper/2603.21942/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 13 of 13)

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- [EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations](https://mlsanity.com/paper/2306.12059/) (2023)
- [Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs](https://mlsanity.com/paper/2302.03655/) (2023)
- [UMA: A Family of Universal Models for Atoms](https://mlsanity.com/paper/2506.23971/) (2025)
- [Uni-Mol2: Exploring Molecular Pretraining Model at Scale](https://mlsanity.com/paper/2406.14969/) (2024)
- [Uni-QSAR: an Auto-ML Tool for Molecular Property Prediction](https://mlsanity.com/paper/2304.12239/) (2023)
- [Equivariant Masked Position Prediction for Efficient Molecular Representation](https://mlsanity.com/paper/2502.08209/) (2025)
- [Equivariant Spherical Transformer for Efficient Molecular Modeling](https://mlsanity.com/paper/2505.23086/) (2025)

## Built on by (most cited first, 1 of 1)

- [Monkey King Bang: A Unified Scientific Multimodal Foundation Model](https://mlsanity.com/paper/2607.20557/) (2026)

## Abstract (arXiv)

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.