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 discretizes molecular coordinates, topologies, and periodic connectivity into a shared vocabulary whose structural tokens remain addressable evidence during model reasoning. Across 86 benchmarks, the authors report state-of-the-art results on 67 tasks, and double-blind experts judged its reasoning traces preferred or comparable to a frontier language model in 98 percent of cases. A common but inspectable structural language could make predictions across proteins, molecules, and crystals easier to connect to the physical evidence that supports 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 framework constrains frontier language models to reason over explicit reaction networks and couples their hypotheses to human review while preserving the network topology. For carbon-dioxide electroreduction, the authors report identification of selectivity-controlling pathways and levers that guided prospective synthesis of a copper-iron oxide catalyst with threefold higher acetate selectivity than matched copper-rich baselines. Reasoning over pathway competition can make an AI proposal experimentally useful by tying a materials choice to a mechanism that can be perturbed and tested.

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 Transferable Water Implicit Network represents aqueous environments with an equivariant graph-neural-network potential trained only on ab initio calculations and experimental labels. Across drug-like molecules, peptides, and proteins, the authors report better crystallographic and nuclear-magnetic-resonance results than earlier learned implicit-solvent or coarse-grained models, with timestep evaluation two orders of magnitude faster than explicit-solvent density-functional-theory potentials. A transferable implicit solvent at this accuracy and speed could extend first-principles-quality biomolecular simulation toward the longer timescales required in practice.

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 analysis derives an information-theoretic ceiling in which squared prediction-truth correlation is bounded by the variance explained by an orthonormal projection basis, so discarded signal cannot be recovered by model complexity. On chemical perturbations, the authors report that a gene-network eigenbasis captured only 10-12% of response variance while PCA captured 90-99%; graph wavelets recovered about 88%, and the ordering reversed for CRISPRa perturbations. Projection choice therefore determines which chemical or genetic response signal remains available to any downstream model.

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 the rate function of an inhomogeneous counting process so protein length can be generated jointly with structure or sequence rather than fixed before sampling. The authors report exact recovery of the length distribution in unconditional design and first-place performance on 10 of 16 structure-based motif-scaffolding tasks, with more unique successes than fixed-length baselines. Allowing length to emerge from the design objective removes an artificial oracle from protein generation and enlarges the space of viable solutions.

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

Adaptive Multi-Teacher Routing calibrates several pretrained interatomic potentials with a small set of high-fidelity labels, then accepts or rejects each proposed pseudo-label according to structure, teacher identity, and model disagreement. The authors report consistent gains over unrouted controls on held-out structures and stable finite-temperature trajectories in systems where baseline simulations collapse. Explicit rejection turns uncertainty from a descriptive score into a data-construction decision, which is the more useful role when one bad configuration can destabilize a long simulation.

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

conDitar-dev combines a pretrained multiscale pocket representation, a pocket-conditioned diffusion model, and generation-time developability optimization to produce ligands with strong affinity and favorable ADMET properties. After synthesis and testing, the authors report two generated PD-L1 ligands with SPR-derived binding values of 3.49 and 3.75 micromolar and selective CSF1R inhibitors active at concentrations as low as 200 nanomolar. The modular design brings binding and developability into one generative workflow and carries selected candidates through synthesis and biological testing.

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 hybrid pipeline couples a generative adversarial network to latent vectors sampled from a photonic quantum processor to design MHC class I-binding peptides. Across 131 HLA alleles, the authors report that quantum-derived priors increased predicted strong-binder yield, with the largest gains for understudied alleles, and peptide-MHC stability ELISAs confirmed potent stabilizers among designs for three selected alleles. Hardware-derived nonclassical priors provide a structured way to widen sequence exploration when biological training data are sparse while preserving allele-specific anchor constraints.

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 1,035 pairwise solid-state reactions executed by a self-driving laboratory, with thermal histories, masses, instrument settings, raw diffraction, refined structures, and reviewer annotations linked by provenance. The authors report 1,351 diffraction scans and 1,950 automated refinement cases, each evaluated by human experts on a three-tier quality scale. This combination of autonomous experiments and auditable raw-to-assignment records supplies the kind of training target that predictive models of solid-state reactivity have largely lacked.

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

scVision uses optimal transport to place genes at fixed positions on a shared pan-tissue map, renders each transcriptome as a continuous image, and applies a masked-image-trained vision transformer as a frozen encoder. In zero-shot tests on six independent held-out studies, the authors report that scVision was the most accurate cell-type annotator, recovered gene programs without supervision, and lost sharply in accuracy when the gene layout was permuted. The fixed spatial map preserves gene relationships and expression magnitude in a representation that can reuse mature computer-vision methods across single-cell studies.

AI–biochemistry Foundation modelsMolecular representation
Abstract brief CC0 Preprint