Preprint scanner project / Issue 2026-07-01

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 / 247 reviewed Jun 24, 2026–Jun 30, 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 2026-07-01

Highlights from this week

#01 arXiv v1 / preprint

Preprint—not peer reviewed

Contrastive Regularization of Machine Learning Potentials

Dimitrios Tzivrailis, Georgios Sotiropoulos, Alberto Rosso, Eiji Kawasaki

The authors introduce contrastive regularization after training, using persistent Langevin chains to raise the energy of configurations where an interatomic potential drifts. On ethanol and aspirin, they report near-quantitative recovery of energy, distance, and dihedral free-energy distributions while preserving force accuracy. The work makes equilibrium sampling a separate test of a potential, rather than treating pointwise force accuracy as sufficient.

AI–chemistryAI–materials Atomistic modelingNeural potentials
Abstract brief CC0 Preprint
#02 arXiv v1 / preprint

Preprint—not peer reviewed

Autoregressive Boltzmann Generators

Danyal Rehman, Charlie B. Tan, Yoshua Bengio, Avishek Joey Bose, Alexander Tong

Autoregressive Boltzmann Generators depart from flow-based generators, avoiding their topological constraints while allowing sequential inference-time interventions. The authors report that their transferable model reduced zero-shot energy error by more than sixty percent on 8-residue systems. This supplies a likelihood-bearing route to equilibrium sampling that is not limited by flow topology.

AI–biochemistryAI–chemistry Atomistic modelingGenerative design
Abstract brief CC0 Preprint
#03 arXiv v1 / preprint

Preprint—not peer reviewed

Towards Generalizable and Evidential Nuclear Magnetic Resonance-Based Molecular Structure Elucidation via Large Language Model Agent

Zheng Fang, Chen Yang, Yusen Tan, Yunpeng Zhao, Fanjie Xu, Hongxin Xiang, Hanyu Sun, Hanyu Gao, Xiaojian Wang, Wenjie Du, Yuqiang Li, Jun Xia

The agent combines specialized spectral tools with chemical knowledge graphs to plan an elucidation, propose structures, check peak-atom consistency, and revise misaligned substructures. On a scaffold-split benchmark with novel scaffolds in the test set, the authors report improved top-ranked accuracy and Tanimoto similarity. Its explicit checks link a structural proposal to the spectral evidence used to refine it.

AI–chemistry Molecular representationScientific agents
Abstract brief CC0 Preprint
#04 bioRxiv v1 / preprint

Preprint—not peer reviewed

BoltzProt-1: Towards Efficient De Novo Binder Design with Good Developability

Ucar, T., Bates, J., Fu, Y., Shi, W., Stark, H., Nava, D., Cavalleri, L., Wohlwend, J., Corso, G., Passaro, S.

The BoltzProt-1 pipeline combines a refined BoltzGens generator with BoltzPPI to rank nanobody designs. The authors report a confirmed-binder rate rising from 3.3% to 8.0% on 10 novel targets. The study may link binder generation to screening outcomes and developability assessment.

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

Preprint—not peer reviewed

Shape-Constrained Bayesian Active Learning of Self-Limiting Saturation Curves

Pouyan Navabi, Christos G. Takoudis

The active-learning platform uses Bayesian monotonic I-spline regression so posterior curves begin at zero, never decrease, and receive their plateau from a maximum-exposure measurement. Across five kinetic families, the authors report recovery within measurement noise without a non-monotone posterior draw, reaching noise-floor accuracy within 20 measurements in every regime. The fixed shape constraint makes scarce measurements useful across self-limiting responses without choosing a kinetic model in advance.

AI–chemistryAI–materials Surrogate modeling
Abstract brief CC0 Preprint
#06 arXiv v1 / preprint

Preprint—not peer reviewed

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design

Sk Md Ahnaf Akif Alvi, Jan Janssen, Danny Perez, Douglas Allaire, Raymundo Arroyave

The authors place a Gaussian-process acquisition gate between generated structures and a property oracle. At a four-call budget, the authors report that ranking surpassed arbitrary selection; the gate was within about 9% of exhaustive spending using roughly one-fifth as many calls. The approach may reduce the expensive evaluation burden in generative materials discovery.

AI–materials Generative designSurrogate modeling
Abstract brief CC0 Preprint
#07 bioRxiv v1 / preprint

Preprint—not peer reviewed

Identifying and Addressing Systematic Data Leakage in Protein-Ligand Affinity Benchmarks

Mattsson, B., Walters, W.

The authors test whether protein-sequence splits prevent target mirroring in affinity benchmarks. Their meta-analysis finds over 6,000 assay pairs and leakage at sequence-identity thresholds as low as 0.2. The tiered benchmark may give affinity-model evaluations a clearer test of genuine generalization.

AI–biochemistryAI–chemistry Biomolecular structureDatasets + benchmarks
Abstract brief CC BY Preprint
#08 arXiv v2 / preprint

Preprint—not peer reviewed

Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents

Kyungmin Nam, Seunghee Han, Jihan Kim

The authors introduce LLM4MOF, a closed-loop multi-agent framework that turns a natural-language target into chemical hypotheses, constraints, diagnostic tests, and feedback. They report enrichment of top performers across six adsorption, separation, and electronic-structure tasks within 400 evaluations. The authors position it as an interpretable inverse-design approach requiring only a natural-language request.

AI–materials Generative designScientific agents
Abstract brief CC0 Preprint
#09 arXiv v2 / preprint

Preprint—not peer reviewed

Unsupervised Thermodynamics of Molecular Diffusion Models: Action-Operator Semantics and Auditable Free-Energy Readout

Wenjie Xi

The action-operator framework represents a fixed molecular environment and an alchemical perturbation through learned diffusion fields. For a ligand-pocket perturbation with negligible phase-space overlap, the authors report a free-energy estimate within approximately one thermal energy unit of a stable multistate reference. This provides a defined thermodynamic reading for diffusion-model representations, while the reported recovery remains partial.

AI–biochemistryAI–chemistry Atomistic modelingGenerative design
Abstract brief CC0 Preprint
#10 arXiv v1 / preprint

Preprint—not peer reviewed

ReactionAtlas: Ab origine exploration of chemical reaction networks with machine learning

Stefan Gugler, Max Eissler, Khaled Kahouli, Klaus-Robert Müller

ReactionAtlas starts from seed molecules, uses a generative model to propose reactions, and screens candidate transition states with a density-functional-theory-trained machine learned force field. Beginning with eight pre-biotic seeds, the authors report roughly 47,000 reactions among roughly 12,000 compounds. This offers a route to build reaction networks from chemistry generated during the search rather than from supplied reaction rules.

AI–chemistry Generative designNeural potentialsReaction modeling
Abstract brief CC0 Preprint