Preprint scanner project / Issue 2026-07-29

Useful Chemistry

A weekly review of arXiv, ChemRxiv, and bioRxiv for ai-enabled methodological advances. Up to twenty papers reviewed every week; a quiet week may publish none.

10 selected / 218 reviewed Jul 22, 2026–Jul 28, 2026
Groups
Group roll call
96 followed groups Research-group roll call
  • Milad Abolhasani Abolhasani Lab North Carolina State University Site ↗
  • Surl-Hee (Shirley) Ahn Ahn Lab University of California, Davis Site ↗
  • Mohammed AlQuraishi AlQuraishi Laboratory Columbia University Site ↗
  • Sayan Banerjee Banerjee Group University of Tennessee, Knoxville Site ↗
  • Christopher J. Bartel Bartel Research Group University of Minnesota Twin Cities Site ↗
  • Jan-Niklas Boyn Boyn Research Group University of Minnesota Twin Cities Site ↗
  • Ting Cao Cao Group University of Washington Site ↗
  • Timothy Cernak Cernak Lab University of Michigan Site ↗
  • Ming Chen Ming Chen Group Purdue University Site ↗
  • Po-Yen Chen Chen Research Group University of Maryland, College Park Site ↗
  • Bingqing Cheng Cheng Group University of California, Berkeley Site ↗
  • Brian Cleary Algorithmic Lens on Experimental Biology Laboratory Boston University Site ↗
  • Connor W. Coley Coley Research Group Massachusetts Institute of Technology Site ↗
  • Yamil J. Colón Colón Group University of Notre Dame Site ↗
  • César de la Fuente Machine Biology Group University of Pennsylvania Site ↗
  • Julia Dshemuchadse Cape Crystal Cornell University Site ↗
  • Thomas P. Fay Fay Group University of California, Los Angeles Site ↗
  • Kara D. Fong Fong Lab California Institute of Technology Site ↗
  • Thomas E. Gartner III Gartner Group Lehigh University Site ↗
  • Rafael Gómez-Bombarelli Learning Matter Massachusetts Institute of Technology Site ↗
  • Jonathan Gootenberg Gootenberg Laboratory Harvard University Site ↗
  • Prashun Gorai 3D Materials Lab Rensselaer Polytechnic Institute Site ↗
  • Diptarka Hait Hait Group Columbia University Site ↗
  • Brian Hie Laboratory of Evolutionary Design Stanford University Site ↗
  • Guoxiang Hu Hu Group Georgia Institute of Technology Site ↗
  • Yong-Jie Hu Materials Computation and Informatics Group Drexel University Site ↗
  • Yifei Huang Huang Lab Pennsylvania State University Site ↗
  • Yunha Hwang Hwang Lab Massachusetts Institute of Technology Site ↗
  • Nicholas E. Jackson AI for Materials Group University of Illinois Urbana-Champaign Site ↗
  • William M. Jacobs Jacobs Group Princeton University Site ↗
  • Dipti Jasrasaria Jasrasaria Group University of Chicago Site ↗
  • Anupama Jha Jha Lab Yale University Site ↗
  • Kaiyi Jiang Jiang Lab Princeton University Site ↗
  • Adrian Jinich Jinich Lab University of California, San Diego Site ↗
  • Felipe Jornada Jornada Research Group Stanford University Site ↗
  • Kalli Kappel Kappel Lab University of California, Los Angeles Site ↗
  • Joshua Kretchmer Kretchmer Research Group Georgia Institute of Technology Site ↗
  • Aditi S. Krishnapriyan Krishnapriyan Research Group University of California, Berkeley Site ↗
  • Sebastian Kube Kube Lab University of Wisconsin–Madison Site ↗
  • Heather J. Kulik Kulik Research Group Massachusetts Institute of Technology Site ↗
  • Ambarish R. Kulkarni Kulkarni Research Group University of California, Davis Site ↗
  • Joseph S. Kwon Kwon Research Group The Ohio State University Site ↗
  • Joonho Lee Lee Group Harvard University Site ↗
  • Can Li Li Research Group Purdue University Site ↗
  • Wanlu Li Wanlu Li Research Group University of California, San Diego Site ↗
  • Rebecca K. Lindsey Lindsey Lab University of Michigan Site ↗
  • Ge Liu Ge Liu Group University of Illinois Urbana-Champaign Site ↗
  • Yuanyue Liu Yuanyue Liu Group The University of Texas at Austin Site ↗
  • Yang Lu Lu Lab University of Wisconsin–Madison Site ↗
  • Jiankun Lyu Evnin Family Laboratory of Computational Molecular Discovery The Rockefeller University Site ↗
  • Cong Ma Cong Ma Lab University of Michigan Site ↗
  • Arkajit Mandal Mandal Group Texas A&M University Site ↗
  • Andrew J. Medford Medford Research Group Georgia Institute of Technology Site ↗
  • Ilias Mitrai Systems and AI Lab The University of Texas at Austin Site ↗
  • Jeetain Mittal Mittal Group Texas A&M University Site ↗
  • Joel A. Paulson Paulson Lab University of Wisconsin–Madison Site ↗
  • Elisa Pieri Pieri Lab University of North Carolina at Chapel Hill Site ↗
  • Doran Raccah MesoScience Lab The University of Texas at Austin Site ↗
  • Phillip Rauscher Rauscher Group New York University Site ↗
  • Wesley Reinhart Reinhart Group Pennsylvania State University Site ↗
  • Gabriel J. Rocklin Rocklin Lab Northwestern University Site ↗
  • Andrew S. Rosen Rosen Research Group Princeton University Site ↗
  • Grant M. Rotskoff Rotskoff Group Stanford University Site ↗
  • Janani Sampath Sampath Research Group University of Florida Site ↗
  • Elvira Sayfutyarova Sayfutyarova Group Pennsylvania State University Site ↗
  • Martin Seifrid Seifrid Group North Carolina State University Site ↗
  • Thomas P. Senftle Senftle Group Rice University Site ↗
  • Karthik Shekhar Shekhar Lab University of California, Berkeley Site ↗
  • Zachary M. Sherman Z Lab University of Washington Site ↗
  • Krishna Shrinivas Shrinivas Lab Northwestern University Site ↗
  • Rohit Singh Singh Lab Duke University Site ↗
  • Micheline Soley Soley Group University of Wisconsin–Madison Site ↗
  • Kevin V. Solomon Solomon Laboratory University of Delaware Site ↗
  • Jeff Spence Spence Lab University of California, San Francisco Site ↗
  • Kayla G. Sprenger Rational Design of Interfaces Lab University of Colorado Boulder Site ↗
  • Chong Sun Sun Lab Rutgers University–New Brunswick Site ↗
  • Yidan Sun Sun Lab Washington University in St. Louis Site ↗
  • Daniel Tabor Tabor Research Group Texas A&M University Site ↗
  • Ming Tang Mesoscale Materials Science Group Rice University Site ↗
  • Roel Tempelaar Tempelaar Team Northwestern University Site ↗
  • Erik Thiede Thiede Lab Cornell University Site ↗
  • Pratyush Tiwary Artificial Chemical Intelligence@Maryland University of Maryland, College Park Site ↗
  • Brian Trippe Trippe Lab Stanford University Site ↗
  • Alexander Urban Urban Research Group Columbia University Site ↗
  • David Van Valen Van Valen Lab California Institute of Technology Site ↗
  • Vojtech Vlcek Vlcek Group University of California, Santa Barbara Site ↗
  • Allon Wagner Wagner Lab University of California, Berkeley Site ↗
  • Shunzhi Wang Wang Lab New York University Site ↗
  • Michael A. Webb Webb Research Group Princeton University Site ↗
  • Mingjian Wen Wen Research Group University of Houston Site ↗
  • Hong-Zhou Ye Ye Group University of Maryland, College Park Site ↗
  • Shuwen Yue Yue Research Group Cornell University Site ↗
  • Daiwei (David) Zhang Daiwei Zhang Lab University of North Carolina at Chapel Hill Site ↗
  • Hongbo Zhao Zhao Research Group University of California, San Diego Site ↗
  • Jian Zhou Zhou Lab University of Chicago Site ↗
  • Tianyu Zhu Zhu Group Yale University Site ↗

Issue 2026-07-29

Highlights from this week

#01 arXiv v1 / preprint

Preprint—not peer reviewed

Uni-XAS: Alignment-Driven Bidirectional Multimodal Learning for X-ray Absorption Spectroscopy

Suyang Zhong, Yuhao Zhao, Boying Huang, Fanjie Xu, Pengwei Xu, Haoyi Tao, Xi Fang, Jun Cheng, Fujie Tang

Uni-XAS treats bidirectional XAS modeling through cross-modal alignment and conditional generation. On its standardized benchmark, the authors report accurate absolute-spectrum prediction, strong cross-modal retrieval, and composition-conditional structure generation. The framework could provide a common basis for multimodal learning and standardized spectroscopy evaluation.

AI–materials Datasets + benchmarksGenerative designMaterials representation
Abstract brief CC0 Preprint
#02 arXiv v2 / preprint

Preprint—not peer reviewed

MatDiffract: A Material-Informed Automated Analysis Platform for X-ray Powder Diffraction

Hongqing Wang, Mingwei Chen, Hongjie Luo, Wen Yin, Fazhi Qi, Xuqing Chai, Miao Liu, Fengyao Hou

MatDiffract combines a simulated diffraction database, hierarchical vector retrieval, and Rietveld refinement for automated X-ray powder diffraction analysis. The authors report high top-ten identification accuracy after automated refinement of 875 single-phase experimental patterns. The workflow can return structural models, fitted profiles, and quantitative compositions for high-throughput characterization.

AI–materials Autonomous labsMaterials representation
Abstract brief CC0 Preprint
#03 arXiv v1 / preprint

Preprint—not peer reviewed

Accurate structural modeling of chemically diverse molecular interfaces with Vilya-2

Vilya Research, :, Pascal Sturmfels, Naozumi Hiranuma, Milad Salem, Benjamin D. Sellers, Stephen Rettie, CJ San Felipe, Chase A. P. Wood, Jeffrey K. Holden, Adam P. Moyer, Patrick J. Salveson, Ivan Anishchanka

Vilya-2 is a diffusion transformer that applies an all-atom representation to interactions between molecules and protein targets. The authors report sub-two angstrom backbone root mean square deviation for 59.1 percent of peptide interfaces. This makes a common structural treatment plausible across peptides, small molecules, and macrocycles.

AI–biochemistryAI–chemistry Biomolecular structureFoundation models
Abstract brief CC0 Preprint
#04 arXiv v1 / preprint

Preprint—not peer reviewed

Rem3Di: Learning smooth, chiral 3D molecular descriptors from atomistic foundation models

Steffen Wedig, Felix Burton, Rokas Elijošius, Christoph Schran, Lars L. Schaaf

Rem3Di aggregates per-atom features from atomistic foundation models into fixed-length descriptors for whole molecules. The authors report performance matching or exceeding published baselines on public drug-property benchmarks without classical two-dimensional fingerprints. This provides a representation that can carry three-dimensional structure and molecular handedness into prediction tasks.

AI–chemistry Foundation modelsMolecular representation
Abstract brief CC0 Preprint
#05 arXiv v1 / preprint

Preprint—not peer reviewed

Transformer Atomic Cluster Expansion: TRACE

Paramvir Ahlawat

TRACE couples atomic cluster expansion density correlations to local multihead cross-attention in an energy-conserving architecture. The authors report a Gibbs-free-energy crossing between non-perovskite and perovskite forms of cesium lead iodide near experimental observations. This suggests one architecture can address phase changes, liquid structure, and chemical reactivity.

AI–chemistryAI–materials Atomistic modelingNeural potentials
Abstract brief CC0 Preprint
#06 bioRxiv v1 / preprint

Preprint—not peer reviewed

Spaceland: Histology-Guided Reconstruction of High-Resolution Whole-Organ 3D Molecular Atlases from Sparse Spatial Transcriptomics

Xu, F., Zhuang, Z., Zhu, Y., Ying, B., Hou, N., Lin, W., Wang, L., Yang, C., song, j.

Spaceland maps sparse spatial transcriptomic sections onto continuous gene-expression fields guided by histological morphology. The authors report that mouse benchmarks bridged molecular gaps of 400 m and resolved sub-spot organization. This offers a route toward organ-scale molecular reconstruction without exhaustive sampling.

AI–biochemistry Foundation modelsMolecular representation
Abstract brief CC BY Preprint
#07 bioRxiv v1 / preprint

Preprint—not peer reviewed

Machine Learning-Assisted Evolution of Broadly Functional Enzyme Libraries

Lal, R., Yang, J., Zhang, Z., Arnold, F. H.

The study diversifies an active site and uses machine learning to create informed libraries of promiscuous protein variants. The authors report that these variants catalyze five of ten reactions not catalyzed by the parent protoglobin. This focuses enzyme-library construction on broader catalytic starting points.

AI–biochemistryAI–chemistry Biomolecular designProtein engineering
Abstract brief CC BY Preprint
#08 arXiv v1 / preprint

Preprint—not peer reviewed

MS-GPT: Rethinking MS/MS De Novo Structure Elucidation as Spectrum-Induced Posterior Querying of a Molecule-Language Model

Xin Zhao, Yumin Liu, Zhuo Li, Weichu Zheng, Feng Zhu, Xiaokang Yang, Yaohui Jin, Yanyan Xu

MS-GPT turns spectrum-induced fingerprint posteriors into a calibrated band of queries for a conditional molecule-language model. The authors report new state-of-the-art exact-match accuracy on both NPLIB1 and MassSpecGym. The approach addresses the gap between oracle fingerprints used in training and noisy spectra at inference.

AI–chemistry Molecular representation
Abstract brief CC0 Preprint
#09 bioRxiv v1 / preprint

Preprint—not peer reviewed

Spatium: A Protein Language Foundation Model for Spatial Proteomics

Wang, T., Wu, S., Huang, L., Liu, J., Huang, K., Zhou, X.

Spatium is a protein language foundation model trained on more than 51 million cells from spatial proteomics platforms. The authors report reconstruction of missing protein measurements while retaining meaningful expression patterns. Such panel-robust representations could support analyses across heterogeneous spatial assays.

AI–biochemistry Foundation modelsMolecular representation
Abstract brief CC BY Preprint
#10 arXiv v1 / preprint

Preprint—not peer reviewed

Aligning Heterogeneous DFT Datasets: A Graph Neural Network Approach to Cross-Functional Formation Energies

Yidong Huang, Tenglong Lu, Hanwen Kang, Junfeng Huang, Sheng Meng, Miao Liu

Using paired PBE-r2SCAN entries, the approach learns cross-functional energy residuals with a structure-aware graph neural network. The authors report that the model converts conventional PBE energies to r2SCAN-level accuracy and has lower mean absolute error than CHGNet. This could allow legacy PBE datasets to inform phase stability, battery voltage profiles, and reaction thermodynamics.

AI–materials Datasets + benchmarksElectronic structureMaterials representation
Abstract brief CC0 Preprint