Poster "molecular property prediction" Papers
20 papers found
Conference
Automatic Auxiliary Task Selection and Adaptive Weighting Boost Molecular Property Prediction
Zhiqiang Zhong, Davide Mottin
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
Evan Antoniuk, Shehtab Zaman, Tal Ben-Nun et al.
Bridging the Gap Between Cross-Domain Theory and Practical Application: A Case Study on Molecular Dissolution
Sihan Wang, Wenjie Du, Qing Zhu et al.
DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training
Yurou Liu, Jiahao Chen, Rui Jiao et al.
E(n) Equivariant Topological Neural Networks
Claudio Battiloro, Ege Karaismailoglu, Mauricio Tec et al.
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks
Sarp Aykent, Tian Xia
GraphGPT: Generative Pre-trained Graph Eulerian Transformer
Qifang Zhao, Weidong Ren, Tianyu Li et al.
Learning Molecular Representation in a Cell
Gang Liu, Srijit Seal, John Arevalo et al.
MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
Liang Wang, Shaozhen Liu, Yu Rong et al.
Random Search Neural Networks for Efficient and Expressive Graph Learning
Michael Ito, Danai Koutra, Jenna Wiens
Self-Supervised Diffusion Models for Electron-Aware Molecular Representation Learning
Gyoung S. Na, Chanyoung Park
SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision
Kangjie Zheng, Siyue Liang, Junwei Yang et al.
Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning
Zihao Jing, Yan Sun, Yan Yi Li et al.
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Toby Boyne, Juan Campos, Rebecca Langdon et al.
UniGEM: A Unified Approach to Generation and Property Prediction for Molecules
Shikun Feng, Yuyan Ni, Lu yan et al.
Data-Efficient Molecular Generation with Hierarchical Textual Inversion
Seojin Kim, Jaehyun Nam, Sihyun Yu et al.
Expressivity and Generalization: Fragment-Biases for Molecular GNNs
Tom Wollschläger, Niklas Kemper, Leon Hetzel et al.
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
Duy Nguyen, Nina Lukashina, Tai Nguyen et al.
Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains
Junhong Shen, Neil Tenenholtz, James Hall et al.
Triplet Interaction Improves Graph Transformers: Accurate Molecular Graph Learning with Triplet Graph Transformers
Md Shamim Hussain, Mohammed Zaki, Dharmashankar Subramanian