Preprint scanner project / Issue 2026-08-05

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 / 304 reviewed Jul 29, 2026–Aug 4, 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 2026-08-05

Highlights from this week

Preprint—not peer reviewed

Structure-free, site-resolved contrastive learning extends small-molecule discovery beyond the reach of structure-based modeling

Ptarmigan-1 co-embeds protein residues and candidate small molecules from sequence and two-dimensional chemistry without constructing a binding pose. The authors report that it localizes reversible and covalent inhibitors on withheld targets and screens the human proteome against a compound library in under a day. A pose-free index could extend virtual screening to cryptic and disordered sites where well-defined pockets are unavailable.

AI–biochemistryAI–chemistry Molecular representation
Abstract brief CC BY Preprint

Preprint—not peer reviewed

Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations

The authors replace explicit neural-network layer stacks with self-consistent fixed-point equations that reuse intermediate representations between molecular-dynamics timesteps. Across invariant and equivariant graph neural network classes, they report two- to five-fold reductions in compute and memory while retaining full atomistic resolution. Reusing trajectory state addresses a practical cost barrier without resorting to spatial or temporal coarse graining.

AI–biochemistryAI–chemistryAI–materials Atomistic modelingNeural potentials
Abstract brief CC0 Preprint

Preprint—not peer reviewed

When May a Model Replace the Experiment? Audits, Licenses, and the Price of Trust in Surrogate-Driven Design

The authors analyze surrogate-led design using selection-aware audits, allowing predictions to propose and train while requiring true evaluations for certified conclusions. Across 432 surrogate fits, they report that the audit statistic tracks deployed search performance more closely than R squared does. The framework separates a surrogate's role in search from the evidence needed to trust its selected candidates.

AI–chemistryAI–materials Datasets + benchmarksSurrogate modeling
Abstract brief CC0 Preprint

Preprint—not peer reviewed

Seed-Guided De Novo Design Expands the Structural Diversity of Antitoxin Protein Binders

The authors guide diffusion-based binder generation with protein fragments selected for geometric complementarity to a target surface. Screening of 1,402 RelE designs identified multiple functional binders, with reported nanomolar to low-micromolar affinity for some variants. Seed-derived contacts offer a route to more diverse protein interfaces at difficult multi-site targets.

AI–biochemistry Biomolecular designGenerative designProtein engineering
Abstract brief CC BY Preprint

Preprint—not peer reviewed

Fast and Accurate Equivariant Foundation Models for Atomistic Simulation

The authors examine equivariant interatomic potentials at very large dataset scale and present foundation models in the NequIP and Allegro architectures. They report leading inference speed, strong scalability, and high accuracy across community benchmarks spanning materials discovery, thermal conductivity, and near-equilibrium properties. The work sharpens a useful design question for foundation potentials: when data are abundant, dataset diversity may matter more than further gains in data efficiency.

AI–chemistryAI–materials Atomistic modelingFoundation modelsNeural potentials
Abstract brief CC0 Preprint

Preprint—not peer reviewed

A Unified Graph Neural Network Framework for Non-Equilibrium Carrier and Lattice Dynamics Driven by Electric Fields

The authors develop an electric-field-response graph neural network that predicts forces, electric responses, and atom-resolved electronic states during molecular dynamics. They report field-driven carrier, phonon, and ionic behavior in MgO, GaAs, and silver iodide, including agreement with experiment for phonon dephasing in GaAs. The unified model could help connect field effects on atoms and localized carriers within finite-temperature simulations.

AI–materials Atomistic modelingElectronic structureNeural potentials
Abstract brief CC0 Preprint

Preprint—not peer reviewed

SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups

SE(3)-MeanFlow extends mean-flow training to protein-frame geometry, using closed-form average velocities for rotations and translations to supply simulation-free targets. For protein backbone generation, the authors report that it matches or exceeds flow-matching baselines using several times more sampling steps, although diversity is modestly reduced. The few-step formulation could make backbone generation more practical for high-throughput design while keeping its diversity tradeoff visible.

AI–biochemistry Biomolecular designGenerative design
Abstract brief CC0 Preprint

Preprint—not peer reviewed

ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density

The authors pretrain a physics-guided diffusion transformer on electron-density point clouds, reconstructing corrupted and masked log-density fields while enforcing electron-number consistency. Across six EDBench tasks, the authors report that the pretrained encoder outperforms the same architecture trained from scratch, especially with limited supervision. This makes electron density a shared training signal for several molecular prediction and retrieval tasks.

AI–chemistry Electronic structureFoundation modelsMolecular representation
Abstract brief CC0 Preprint

Preprint—not peer reviewed

Conditional grain-graph diffusion for property-guided inverse design of polycrystalline microstructures

The authors combine a grain graph neural network for stress prediction with conditional graph diffusion to generate dual-phase titanium alloy microstructures for prescribed property targets. Finite element validation of the five best candidates in each primary case gave a reported maximum relative error of 1.0 percent in mean properties. The approach couples property targeting with crystallographic checks, which is useful when a plausible microstructure must satisfy more than one constraint.

AI–materials Generative designMaterials representationSurrogate modeling
Abstract brief CC0 Preprint

Preprint—not peer reviewed

DASH: Decoupled Adaptive Surrogate - Acquisition Harness for Automated Bayesian Optimization

DASH chooses surrogate models using predictive reliability, uncertainty calibration, and ranking consistency, then adapts acquisition quotas before final selection by a language model. Across four chemical optimization tasks, the authors report gains over the best automated Bayesian optimization baseline in both trajectory and endpoint measures. Separating model reliability from campaign-level acquisition control may make automated optimization more responsive to changing conditions.

AI–chemistry Scientific agentsSurrogate modeling
Abstract brief CC0 Preprint