"molecular property prediction" Papers

25 papers found

3D Denoisers Are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation

Sungjun Cho, Dae-Woong Jeong, Sung Moon Ko et al.

AAAI 2025paperarXiv:2309.04062
1
citations

Automatic Auxiliary Task Selection and Adaptive Weighting Boost Molecular Property Prediction

Zhiqiang Zhong, Davide Mottin

NEURIPS 2025

Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations

Pengcheng Jiang, Cao Xiao, Tianfan Fu et al.

AAAI 2025paperarXiv:2306.01631
7
citations

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models

Evan Antoniuk, Shehtab Zaman, Tal Ben-Nun et al.

NEURIPS 2025arXiv:2505.01912
7
citations

Bridging the Gap Between Cross-Domain Theory and Practical Application: A Case Study on Molecular Dissolution

Sihan Wang, Wenjie Du, Qing Zhu et al.

NEURIPS 2025

DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training

Yurou Liu, Jiahao Chen, Rui Jiao et al.

ICLR 2025
5
citations

E(n) Equivariant Topological Neural Networks

Claudio Battiloro, Ege Karaismailoglu, Mauricio Tec et al.

ICLR 2025arXiv:2405.15429
20
citations

GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks

Sarp Aykent, Tian Xia

ICLR 2025
15
citations

GraphGPT: Generative Pre-trained Graph Eulerian Transformer

Qifang Zhao, Weidong Ren, Tianyu Li et al.

ICML 2025arXiv:2401.00529
11
citations

Learning Molecular Representation in a Cell

Gang Liu, Srijit Seal, John Arevalo et al.

ICLR 2025arXiv:2406.12056
13
citations

MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights

Jingjing Hu, Dan Guo, Zhan Si et al.

AAAI 2025paperarXiv:2412.16483
6
citations

MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra

Liang Wang, Shaozhen Liu, Yu Rong et al.

ICLR 2025arXiv:2502.16284
9
citations

Random Search Neural Networks for Efficient and Expressive Graph Learning

Michael Ito, Danai Koutra, Jenna Wiens

NEURIPS 2025arXiv:2510.22520

Self-Supervised Diffusion Models for Electron-Aware Molecular Representation Learning

Gyoung S. Na, Chanyoung Park

ICLR 2025

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision

Kangjie Zheng, Siyue Liang, Junwei Yang et al.

ICLR 2025arXiv:2412.05569
5
citations

Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning

Zihao Jing, Yan Sun, Yan Yi Li et al.

NEURIPS 2025arXiv:2510.23640
1
citations

Subgraph Aggregation for Out-of-Distribution Generalization on Graphs

Bowen Liu, Haoyang Li, Shuning Wang et al.

AAAI 2025paperarXiv:2410.22228
5
citations

The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning

Toby Boyne, Juan Campos, Rebecca Langdon et al.

NEURIPS 2025arXiv:2506.07619
2
citations

TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence

Feng Jiang, Mangal Prakash, Hehuan Ma et al.

NEURIPS 2025spotlightarXiv:2506.21028
2
citations

UniGEM: A Unified Approach to Generation and Property Prediction for Molecules

Shikun Feng, Yuyan Ni, Lu yan et al.

ICLR 2025arXiv:2410.10516
22
citations

Data-Efficient Molecular Generation with Hierarchical Textual Inversion

Seojin Kim, Jaehyun Nam, Sihyun Yu et al.

ICML 2024arXiv:2405.02845
6
citations

Expressivity and Generalization: Fragment-Biases for Molecular GNNs

Tom Wollschläger, Niklas Kemper, Leon Hetzel et al.

ICML 2024arXiv:2406.08210
11
citations

Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks

Duy Nguyen, Nina Lukashina, Tai Nguyen et al.

ICML 2024arXiv:2402.01975
7
citations

Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains

Junhong Shen, Neil Tenenholtz, James Hall et al.

ICML 2024arXiv:2402.05140
54
citations

Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers

Md Shamim Hussain, Mohammed Zaki, Dharmashankar Subramanian

ICML 2024arXiv:2402.04538
17
citations