Preprint scanner project / Issue 2026-07-15

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 / 217 reviewed Jul 8, 2026–Jul 14, 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 2026-07-15

Highlights from this week

#01 arXiv v1 / preprint

Preprint—not peer reviewed

Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning

Chen Tang, Yizhou Wang, Jianyu Wu, Lintao Wang, Shixiang Tang, Pengze Li, Encheng Su, Jun Yao, Jiabei Xiao, Yuqi Shi, Jielan Li, Hongxia Hao, Zhangyang Gao, Fang Wu, Ben Fei, Xiangyu Yue, Pan Tan, Bozitao Zhong, Jinouwen Zhang, Aoran Wang, Yan Lu, Jiaheng Liu, Xinzhu Ma, Liang Hong, Mingyue Zheng, Phil Torr, Bowen Zhou, Wanli Ouyang, Lei Bai

SciReasoner converts coordinates, topologies, and periodic connectivities into a shared structure-aware vocabulary with addressable evidence units. The authors report leading performance on many benchmarks and expert ratings that its reasoning traces were preferred or comparable in most cases. Addressable structural evidence could help connect predictions to the chemical, biological, or materials features used to justify them.

AI–biochemistryAI–chemistryAI–materials Biomolecular structureFoundation modelsMaterials representationMolecular representation
Abstract brief CC0 Preprint
#02 arXiv v1 / preprint

Preprint—not peer reviewed

Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses

Sutanay Choudhury, Anwesha Banerjee, Udishnu Sanyal, Jorin Dawidowicz, Chiezugolum Ijeoma Odilinye, Jesun Firoz, Liney Arnadottir, Simone Raugei, Johannes Lercher, Arnab Dutta

The authors present a human-AI framework that places frontier language models within explicit reaction networks, drawing testable hypotheses from complex chemical graphs. The authors report a copper-iron oxide catalyst with acetate selectivity threefold greater than in matched Cu-rich baselines. This may provide a route from pathway competition to experimentally testable catalyst-design hypotheses.

AI–chemistryAI–materials Reaction modelingScientific agents
Abstract brief CC0 Preprint
#03 arXiv v1 / preprint

Preprint—not peer reviewed

Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy

Jan Eckwert, Julija Zavadlav

The authors introduce an implicit water machine-learning potential built with an equivariant graph neural network and trained on ab initio and experimental labels. They report transfer across drug-like molecules, peptides, and proteins, while closely matching explicit-solvent models with much faster timestep evaluation. Such an implicit treatment could make aqueous biomolecular modeling more practical where long simulations are needed.

AI–biochemistryAI–chemistry Atomistic modelingNeural potentials
Abstract brief CC0 Preprint
#04 bioRxiv v1 / preprint

Preprint—not peer reviewed

The projection basis determines the information ceiling for perturbation prediction

BIANCO, S.

The study derives a projection-specific information ceiling that bounds prediction-truth correlation by variance explained. The authors report that the appropriate basis changes with perturbation modality, with graph wavelets recovering chemical-response signal discarded by a low-pass network basis. This makes representation choice a testable limit on perturbation prediction rather than a detail left to model complexity.

AI–biochemistryAI–chemistry Datasets + benchmarksMolecular representation
Abstract brief CC BY Preprint
#05 arXiv v1 / preprint

Preprint—not peer reviewed

Variable-Length Generative Protein Design via Generalized Poisson Flow

Chaoran Cheng, Zhanghan Ni, Yanru Qu, Yuxin Chen, Ruihan Guo, Jiajun Fan, Ge Liu

Generalized Poisson Flow learns a rate function for variable-length protein generation rather than requiring protein length before sampling. The authors report improved unconditional structural designability, recovery of the length distribution, and leading results in several motif-scaffolding tasks. Jointly choosing length and molecular content can enlarge the feasible design space when the right size is unknown.

AI–biochemistry Biomolecular designGenerative designProtein engineering
Abstract brief CC0 Preprint
#06 arXiv v1 / preprint

Preprint—not peer reviewed

Active rejection enables reliable generalization of universal machine-learning interatomic potentials

Mingxiang Luo, Xinnan Mao, Lu Wang, Lei Bai, Feng Ding, Yuqiang Li

The authors describe a routing approach that estimates the reliability of each structure–teacher pair and rejects structures without a sufficiently reliable teacher. Using merely 0.2% real r²SCAN labels from the candidate structures, the authors report a set of 2.89 million traceable pseudo-labels and stable trajectories in multiple material systems. Active rejection could help expand high-fidelity training data while keeping reliability within the selection step.

AI–materials Atomistic modelingDatasets + benchmarksNeural potentials
Abstract brief CC0 Preprint
#07 arXiv v1 / preprint

Preprint—not peer reviewed

Generating Developable 3D Molecules via Pocket-Conditioned Diffusion and Property-Aware Optimization

Ruoxi Gao, Jiangweizhi Peng, Ziqi Chen, Frazier N. Baker, David C. Kombo, John L. Kane, Andrew A. Scholte, Yi Li, Matthew J. LaMarche, Luigi I. Iconaru, Hans-Peter Biemann, Mingyi Hong, Xia Ning

The authors introduce conDitar-dev, which combines multi-scale pocket representations, pocket-conditioned diffusion, and generation-time optimization of ligand developability. They report experimental testing of designed molecules and SPR-derived values of 3.49 and 3.75 μM for two PD-L1 molecules from conDitar-dev. This framework could help bring developability into pocket-conditioned ligand generation.

AI–biochemistryAI–chemistry Generative designMolecular representation
Abstract brief CC0 Preprint
#08 bioRxiv v1 / preprint

Preprint—not peer reviewed

Hybrid quantum-classical de novo design of MHC-binding peptides

Engdal, E. S., Funk, J., Bacarreza, O., Machado, L., Johansen, K. H., Kemming, J., Farnsworth, T., Brasas, V., Lefevre-Morand, R. Y. L., Slysz, M., Noerregaard, O. L., Sandberg, O. A. D. A., Makarovskiy, A., Lodahl, P., Acevedo-Rocha, C. G., Kurowski, K., Hadrup, S. R., Clements, W. R., Jenkins, T.

The authors couple a generative adversarial network to latent vectors from a photonic quantum processor for de novo peptide design. Across tested alleles, they report more predicted strong binders from quantum-derived priors and in vitro confirmation for selected designs. Hardware-derived priors may broaden sequence exploration when training data for a target are limited.

AI–biochemistry Biomolecular designGenerative designProtein engineering
Abstract brief CC BY Preprint
#09 arXiv v1 / preprint

Preprint—not peer reviewed

The Precursor Genome: A Pairwise Reaction Dataset for Solid-State Synthesis

Lauren N. Walters, Matthew J. McDermott, Bernardus Rendy, Yuxing Fei, Kristin A. Persson, Gerbrand Ceder

The Precursor Genome records autonomously generated pairwise solid-state reactions with experimental metadata and raw diffraction-derived product assignments. The authors report that automated Rietveld refinements were independently assessed by human experts on a three-tier quality scale. Its traceable ledger can support models that connect precursor choices, experimental histories, and final phases.

AI–materials Autonomous labsDatasets + benchmarksReaction modeling
Abstract brief CC0 Preprint
#10 arXiv v1 / preprint

Preprint—not peer reviewed

A vision foundation model for single-cell biology via spatial gene cartography

Ridvan Yesiloglu, Sakib Mostafa, James Zou, Ash Alizadeh, Jiajun Wu, Lei Xing, Ehsan Adeli, Md Tauhidul Islam

The authors render each transcriptome as a continuous image by placing genes on one fixed pan-tissue layout using optimal transport. In held-out zero-shot studies, they report the most accurate cell-type annotation and unsupervised recovery of gene programs. A fixed gene map makes biological relationships available to visual representations without requiring batch labels.

AI–biochemistry Foundation modelsMolecular representation
Abstract brief CC0 Preprint