Here, we present a platform built on our inverted Graph Transformer Network, IMPRESSION-G2, which can accurately and rapidly reconstruct molecular bonding directly from experimental nuclear magnetic resonance (NMR) spectroscopic information. It comprises three interconnected stages: a one-shot model that predicts bond connectivity between atoms; a structure-correction stage that corrects the predicted structures by removing uncertain bonds and iteratively reassigning them; noise-augmented multi-shot prediction, generating an ensemble of candidate structures, which are ranked to identify the best-fit structure.
arXiv:2512. 18531v2 Announce Type: replace-cross Abstract: One-dimensional NMR spectroscopy is one of the most widely used techniques for the characterization of organic compounds and natural products.
By Frank Hu, Jonathan M. Tubb, Dimitris Argyropoulos, Sergey Golotvin, Mikhail Elyashberg, Grant M. Rotskoff, Matthew W. Kanan, Thomas E. Markland
arXiv:2606. 19374v1 Announce Type: cross Abstract: Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially reflect the principles governing protein folding.
By Mohamed Mouhajir, Limei Wang, El Houcine Bergou, Hajar El Hammouti, Lamiae Azizi, Dongqi Fu
arXiv:2607. 19816v1 Announce Type: cross Abstract: Determining molecular structures from spectroscopic data remains fundamentally challenging because the inverse problem is intrinsically underdetermined: individual spectra are sparse, low-dimensional, and encode only partial structural evidence relative to the vast space of possible molecules.
By Chengchun Liu, Zhiyuan Yan, Li Yuan, Hao Li, Boxuan Zhao, Yonghong Tian, Bartosz A. Grzybowski, Fanyang Mo
WEECFP-SuRGE introduces a position‑aware substructure encoding method that combines tokenized hierarchical Morgan fingerprints with graph‑distance‑dependent rotations applied at the input and within transformer self‑attention. The approach captures local chemistry, long‑range interactions, and molecular topology without requiring external pretraining or 3‑D conformer generation. Benchmarks on MoleculeNet and the Therapeutic Data Commons ADMET datasets show competitive performance, and a reconstruction procedure correctly identifies constitutional isomers for 92.6% of a 4,200‑molecule library.
By Robert Epps
arXiv:2510.07289v2 Announce Type: replace
Abstract: Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated s...
By Xingtong Yu, Chang Zhou, Xinming Zhang, Yuan Fang
arXiv:2511. 03170v3 Announce Type: replace-cross Abstract: The quantitative structure-activity relationship assumes a smooth mapping between molecular structure and biological activity.
By Hajung Kim, Jueon Park, Junseok Choe, Seungheun Baek, Hyeon Hwang, Jaewoo Kang
arXiv:2509. 22468v2 Announce Type: replace-cross Abstract: High-quality molecular representations are essential for property prediction and molecular design, yet large labeled datasets remain scarce.
By Boshra Ariguib, Mathias Niepert, Andrei Manolache
arXiv:2608. 09099v1 Announce Type: new Abstract: Quantitative estimation of protein-ligand binding affinity from three-dimensional complex structures is a fundamental task in structure-based computational chemistry and molecular modeling.
By Qingyang Zou, Jiaye Huang, Hangbo Xie, Jiayue Yin, Youyi Song, Jinfeng Liu
arXiv:2609.05694v1 Announce Type: new
Abstract: Predicting olfactory qualities from molecular structure is an open problem in chemoinformatics. Although linear models can link molecular features to o...
By Mrityunjay Sharma, Sarabeshwar Balaji, Valentina Parma, Ritesh Kumar
arXiv:2602. 20573v3 Announce Type: replace Abstract: Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction.
By Rajan, Ishaan Gupta
arXiv:2604. 06336v2 Announce Type: replace-cross Abstract: Fragment-level representations provide a natural way to capture recurring molecular substructures and reuse their learned representations across molecules.
By Yi Yang, Ovidiu Daescu