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 spectra and atomic structures as a shared alignment-and-generation problem, with retrieval-anchored forward decoding and permutation-rectified inverse flow matching. The authors report strong cross-modal retrieval, absolute-spectrum prediction, and composition-conditioned three-dimensional structure generation on 328,839 paired structures and spectra. A common latent space makes forward and inverse spectroscopy mutually informative rather than two disconnected regressions.

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 perturbation-augmented simulated patterns, multiscale vector retrieval, Rietveld refinement, and quantitative phase fitting in one automated workflow. The authors report 91.3% top-1 and 97.2% top-10 identification accuracy after automated refinement on 875 experimental single-phase patterns. Returning refined structures and quantitative compositions within seconds addresses a practical characterization bottleneck in high-throughput and autonomous materials work.

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 extends an all-atom molecular representation from individual molecules to a diffusion transformer that models their interactions with protein targets. The authors report that calibrated structural ensembles recover 59.1% of peptide interfaces below 2 angstrom backbone RMSD, while the model also reaches state-of-the-art small-molecule docking and transfers to complex types unlike those in training. A common all-atom representation can support structure prediction across chemically distinct interface classes and then be fine-tuned for hit-to-lead enrichment.

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 Elijo\vsius, Christoph Schran,, Lars L. Schaaf

Rem3Di pools per-atom latent features from atomistic foundation models into smooth, order-invariant whole-molecule descriptors and adds pseudoscalar features that reverse sign under reflection to encode chirality. Across public drug-property benchmarks, the authors report that Rem3Di matched or exceeded published baselines without classical two-dimensional fingerprints and separated transition-metal complexes without predefined bonding rules. The representation carries information learned from quantum-mechanical simulation into property prediction and virtual screening while preserving three-dimensional structure and molecular handedness.

AI–chemistry Foundation modelsMolecular representation
Abstract brief CC0 Preprint
#05 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 learns continuous gene-expression fields in morphology-informed histological space, using optical-flow interpolation of foundation-model histology features to build a dense three-dimensional scaffold and decode sparse transcriptomic measurements. In mouse benchmarks, the authors report that Spaceland outperformed spatial-transcriptomic and two-dimensional histology baselines while resolving sub-spot organization; in planarian regeneration, four sections per stage supported time-resolved whole-organism analysis. The continuous-field formulation provides a scalable route from sparse sections to high-resolution whole-organ molecular reconstruction across tissues and regeneration stages.

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

Preprint—not peer reviewed

Transformer Atomic Cluster Expansion: TRACE

Paramvir Ahlawat

TRACE combines atomic cluster-expansion density correlations with local multihead cross-attention in an energy-conserving equivariant architecture for interatomic potentials. With the same architecture, the authors report a methyl-migration activation free energy of 27.92 plus or minus 0.03 kcal/mol, close to the experimental 29.2 plus or minus 1.1 kcal/mol, together with experimental agreement for perovskite phase behavior and liquid-water structure. An equivariant potential that spans crystallization, liquid structure, and chemical reactivity reduces the need for separate models tied to individual phases or processes.

AI–chemistryAI–materials Atomistic modelingNeural potentials
Abstract brief CC0 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 workflow diversifies a parent protoglobin active site and uses active-learning-assisted directed evolution to predict promiscuous variant libraries across carbene and nitrene transfer reactions. Across 26 reactions, the authors report improved activity and selectivity for every parent-catalyzed reaction in at least one predicted-library member, plus activity on five of ten reactions absent from the parent enzyme. The strategy produces enzyme libraries that retain parent functions while adding transformations outside the parent's catalytic repertoire.

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 queries a conditional molecule-language model with a calibrated band of spectrum-induced fingerprints, then ranks pooled candidates by generation-frequency consensus. The authors report new state-of-the-art exact-match accuracy on NPLIB1 and MassSpecGym at both top-1 and top-10 ranks. Treating the noisy fingerprint as a posterior rather than a thresholded answer preserves uncertainty where de novo spectral identification needs it most.

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 across spatial-proteomics platforms to learn protein co-expression hierarchies that remain stable across panel composition and measurement scale. The authors report that Spatium recovered cell identities with known marker patterns, separated spatial microenvironments by coherent enrichment signatures, and reconstructed missing proteins while preserving biological expression patterns. A panel-robust representation lets heterogeneous spatial-proteomics datasets support cell-identity, microenvironment, and missing-marker tasks with only lightweight adaptation.

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

A structure-aware graph neural network learns cross-functional energy residuals from 380,190 paired PBE-r2SCAN structures, converting legacy PBE formation energies onto the r2SCAN scale. The authors report a mean absolute error of 14.3 meV/atom when converting PBE energies to r2SCAN-level accuracy, compared with 18.2 meV/atom for CHGNet. Cross-functional alignment makes large legacy DFT collections usable alongside higher-fidelity calculations for materials foundation models and thermodynamic prediction.

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