-
Moldina: a fast and accurate search algorithm for simultaneous docking of multiple ligands J. Cheminfom. (IF 7.1) Pub Date : 2025-04-28 Radek Halfar, Jiří Damborský, Sérgio M. Marques, Jan Martinovič
Protein-ligand docking is a computational method routinely used in many structural biology applications. It usually involves one receptor and one ligand. The docking of multiple ligands, however, can be important in several situations, such as the study of synergistic effects, substrate and product inhibition, or competitive binding. This can be a challenging and computationally demanding process.
-
Photoinduced ferroelectric phase transition triggering photocatalytic water splitting npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-25 Jun Wen, Zhi-rui Luo, Lin-can Fang, Wen-xian Chen, Gui-lin Zhuang
-
Strain and ligand effects in the 1-D limit: reactivity of steps npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-25 Onyinyechukwu Goodness Njoku, Paige Fronczak, Kara Smeltz, Ian T. McCrum
-
A machine learning model with minimize feature parameters for multi-type hydrogen evolution catalyst prediction npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-24 Chao Wang, Bing Wang, Changhao Wang, Aojian Li, Zhipeng Chang, Ruzhi Wang
-
ELEQTRONeX: A GPU-accelerated exascale framework for non-equilibrium quantum transport in nanomaterials npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-24 Saurabh S. Sawant, François Léonard, Zhi Yao, Andrew Nonaka
-
Temperature-Dependent Coarse-Grained Model for Simulations of Intrinsically Disordered Protein LCST and UCST Liquid-Liquid Phase Separations. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-25 Yingmin Jiang,Tâp Ha-Duong
Many intrinsically disordered proteins (IDPs) can undergo a liquid-liquid phase separation (LLPS) in water, depending on solution conditions (temperature, pH, and ionic strength). There are two types of LLPS that are controlled by temperature: those occurring above a lower critical solution temperature (LCST) and those occurring below an upper critical solution temperature (UCST). IDP coarse-grained
-
Impact of Native Environment in Multiheme-Cytochrome Chains of the MtrCAB Complex. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-25 Sasthi C Mandal,Ronit Sarangi,Atanu Acharya
MtrCAB protein complex plays a crucial role in exporting electrons through the outer membrane (OM) to external acceptors. This complex consists of three proteins and contains 20 hemes. Optimal protein-protein interactions and, consequently, heme-heme interactions facilitate efficient electron transfer through the conduit of hemes. The cytochrome MtrA remains mostly inside porin MtrB, and the MtrB barrel
-
Boosting Drug-Disease Association Prediction for Drug Repositioning via Dual-Feature Extraction and Cross-Dual-Domain Decoding. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-25 Enqiang Zhu,Xiang Li,Chanjuan Liu,Nikhil R Pal
The extraction of biomedical data has significant academic and practical value in contemporary biomedical sciences. In recent years, drug repositioning, a cost-effective strategy for drug development by discovering new indications for approved drugs, has gained increasing attention. However, many existing drug repositioning methods focus on mining information from adjacent nodes in biomedical networks
-
Algorithm for Efficient Superposition and Clustering of Molecular Assemblies Using the Branch-and-Bound Method. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-25 Yuki Yamamoto
The root-mean-square deviation (RMSD) is one of the most common metrics for comparing the similarity of three-dimensional chemical structures. The chemical structure similarity plays an important role in data chemistry because it is closely related to chemical reactivity, physical properties, and bioactivity. Despite the wide applicability of the RMSD, the simultaneous determination of atom mapping
-
-
Insights into oxygen diffusion in rare earth disilicate environmental barrier coatings npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-24 Shiqiang Hao, Richard P. Oleksak, Ömer N. Doğan, Michael C. Gao
-
Uncertainty quantification for neural network potential foundation models npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-24 Jenna A. Bilbrey, Jesun S. Firoz, Mal-Soon Lee, Sutanay Choudhury
-
Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-24 Xiumin Liu, Xinyue Zhang, Ye Sheng, Zihe Zhang, Pan Xiong, Xuehai Ju, Junwu Zhu, Caichao Ye
-
Visualising lead optimisation series using reduced graphs J. Cheminfom. (IF 7.1) Pub Date : 2025-04-24 Jessica Stacey, Baptiste Canault, Stephen D. Pickett, Valerie J. Gillet
The typical way in which lead optimisation (LO) series are represented in the medicinal chemistry literature is as Markush structures and associated R-group tables. The Markush structure shows a central core or molecular scaffold that is common to the series with R groups that indicate the points of variability that have been explored in the series. The associated R-group table shows the substituent
-
High-throughput screening data generation, scoring and FAIRification: a case study on nanomaterials J. Cheminfom. (IF 7.1) Pub Date : 2025-04-23 Gergana Tancheva, Vesa Hongisto, Konrad Patyra, Luchesar Iliev, Nikolay Kochev, Penny Nymark, Pekka Kohonen, Nina Jeliazkova, Roland Grafström
In vitro-based high-throughput screening (HTS) technology is applicable to hazard-based ranking and grouping of diverse agents, including nanomaterials (NMs). We present a standardized HTS-derived human cell-based testing protocol which combines the analysis of five assays into a broad toxic mode-of-action-based hazard value, termed Tox5-score. The overall protocol includes automated data FAIRification
-
Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design J. Cheminfom. (IF 7.1) Pub Date : 2025-04-23 Muhammad Arslan Masood, Samuel Kaski, Tianyu Cui
In drug discovery, prioritizing compounds for experimental testing is a critical task that can be optimized through active learning by strategically selecting informative molecules. Active learning typically trains models on labeled examples alone, while unlabeled data is only used for acquisition. This fully supervised approach neglects valuable information present in unlabeled molecular data, impairing
-
Modeling Enzyme Reaction and Mutation by Direct Machine Learning/Molecular Mechanics Simulations. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-24 Xinhu Sha,Zhuo Chen,Daiqian Xie,Yanzi Zhou
Accurately modeling enzyme reactions through direct machine learning/molecular mechanics simulations remains challenging in describing the electrostatic coupling between the QM and MM subsystems. In this work, we proposed a reweighting ME (mechanic embedding) REANN (recursively embedded atom neural network) method that trains the potential and point charges of the QM subsystem in vacuo. The charge
-
Stability of the Long-Range Corrected Exchange-Correlation Functional and the Proca Procedural Functional in Time-Dependent Density-Functional Theory. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-24 Jared R Williams,Carsten A Ullrich
Excitonic effects in the optical absorption spectra of solids can be described with time-dependent density-functional theory (TDDFT) in the linear-response regime, using a simple class of approximate, long-range corrected (LRC) exchange-correlation functionals. It was recently demonstrated that the LRC approximation can also be employed in real-time TDDFT to describe exciton dynamics. Here, we investigate
-
Revisiting Machine Learning Potentials for Silicate Glasses: The Missing Role of Dispersion Interactions. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-24 Alfonso Pedone,Marco Bertani,Matilde Benassi
Machine learning interatomic potentials (MLIPs) offer a promising alternative to traditional force fields and ab initio methods for simulating complex materials such as oxide glasses. In this work, we present the first evaluation of the pretrained MACE (Multi-ACE) model [D.P. Kovács et al., J. Chem. Phys. 159(2023), 044118] for silicate glasses, using sodium silicates as a test case. We compare its
-
ANI-1xBB: An ANI-Based Reactive Potential for Small Organic Molecules. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-24 Shuhao Zhang,Roman Zubatyuk,Yinuo Yang,Adrian Roitberg,Olexandr Isayev
Reactive potentials serve as essential tools for investigating chemical reactions with moderate computational costs. However, traditional reactive potentials often depend on fixed, semiempirical parameters, which limits their accuracy and transferability. Overcoming these limitations can significantly expand the applicability of reactive potentials, enabling the simulation of a broader range of reactions
-
Combined In Vitro and In Silico Workflow to Deliver Robust, Transparent, and Contextually Rigorous Models of Bioactivity. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-24 Nathaniel Charest,Gabriel Sinclair,Stephanie A Eytcheson,Daniel T Chang,Todd M Martin,Charles N Lowe,Katie Paul Friedman,Antony J Williams
New approach methodologies (NAMs) are an increasing priority in the field of toxicology to fill data gaps and reduce time and resources in chemical safety assessment. We describe an NAMs workflow that integrates an in vitro high-throughput bioassay with an in silico computational model. In defining this workflow, we propose, as a crucial step of in silico development, the identification of explicit
-
Aliphatic Polyester Recognition and Reactivity at the Active Cleft of a Fungal Cutinase. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-24 Pietro Vidossich,Madushanka Manathunga,Andreas W Götz,Kenneth M Merz,Marco De Vivo
Protein engineering of cutinases is a promising strategy for the biocatalytic degradation of non-natural polyesters. We report a mechanistic study addressing the hydrolysis of the aliphatic polyester poly(butylene succinate, or PBS) by the fungal Apergillus oryzae cutinase enzyme. Through atomistic molecular dynamics simulations and advanced alchemical transformations, we reveal how three units of
-
Effects of Point Mutations on the Thermal Stability of the NBD1 Domain of hCFTR. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-24 Lior Lublin,Hanoch Senderowitz
Cystic fibrosis (CF) is an autosomal recessive genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) chloride channel. The first nucleotide-binding domain (NBD1) of the CFTR is considered to be a hotspot for CF-causing mutations, and some of these mutations compromise the domain's thermal stability as well as its interactions with other domains. The mechanisms
-
Beyond ST‐246: Unveiling Potential Inhibitors Targeting VP37 Protein in Silico From Herb and Marine Databases J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-24 Runhua Zhang, Xin Zhang, Shulin Zhao, Quan Zou, Yijie Ding, Xiaoyi Guo, Hongjie Wu
-
Analytical First Derivatives of the SCF Energy for the Conductor‐Like Polarizable Continuum Model With Non‐Static Radii J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-24 Lukas Wittmann, Miquel Garcia‐Ratés, Christoph Riplinger
-
The Role of Gold in Modifying the Structural Stabilities, Superhalogen Properties, and Double Aromaticity of Cyclic Carbon Clusters: Insights From AuC20− and AuC20 J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-24 Sheng‐Jie Lu, Guo‐Jin Cao, Zhao‐Ou Gao
-
KNDM: A Knowledge Graph Transformer and Node Category Sensitive Contrastive Learning Model for Drug and Microbe Association Prediction. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-23 Dongliang Chen,Tiangang Zhang,Hui Cui,Jing Gu,Ping Xuan
It has been proven that the microbiome in human bodies can promote or inhibit the treatment effects of the drugs by affecting their toxicities and activities. Therefore, identifying drug-related microbes helps in understanding how drugs exert their functions under the influence of these microbes. Most recent methods for drug-related microbe prediction are developed based on graph learning. However
-
Hierarchy-boosted funnel learning for identifying semiconductors with ultralow lattice thermal conductivity npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-22 Mengfan Wu, Shenshen Yan, Jie Ren
-
GESim: ultrafast graph-based molecular similarity calculation via von Neumann graph entropy J. Cheminfom. (IF 7.1) Pub Date : 2025-04-22 Hiroaki Shiokawa, Shoichi Ishida, Kei Terayama
Representing molecules as graphs is a natural approach for capturing their structural information, with atoms depicted as nodes and bonds as edges. Although graph-based similarity calculation approaches, such as the graph edit distance, have been proposed for calculating molecular similarity, these approaches are nondeterministic polynomial (NP)-hard and thus computationally infeasible for routine
-
Learning motif features and topological structure of molecules for metabolic pathway prediction J. Cheminfom. (IF 7.1) Pub Date : 2025-04-21 Jianguo Hu, Yiqing Zhang, Jinxin Xie, Zhen Yuan, Zhangxiang Yin, Shanshan Shi, Honglin Li, Shiliang Li
Metabolites serve as crucial biomarkers for assessing disease progression and understanding underlying pathogenic mechanisms. However, when the metabolic pathway category of metabolites is unknown, researchers face challenges in conducting metabolomic analyses. Due to the complexity of wet laboratory experimentation for pathway identification, there is a growing demand for predictive methods. Various
-
Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset J. Cheminfom. (IF 7.1) Pub Date : 2025-04-21 Nadin Ulrich, Karsten Voigt, Anton Kudria, Alexander Böhme, Ralf-Uwe Ebert
Water solubility is a relevant physico-chemcial property in environmental chemistry, toxicology, and drug design. Although the water solubility is besides the octanol–water partition coefficient, melting point, and boiling point a property with a large amount of available experimental data, there are still more compounds in the chemical universe for which information on their water solubility is lacking
-
Activity cliff-aware reinforcement learning for de novo drug design J. Cheminfom. (IF 7.1) Pub Date : 2025-04-21 Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang
The integration of artificial intelligence (AI) in drug discovery offers promising opportunities to streamline and enhance the traditional drug development process. One core challenge in de novo molecular design is modeling complex structure-activity relationships (SAR), such as activity cliffs, where minor molecular changes yield significant shifts in biological activity. In response to the limitations
-
Spatial and Sequential Topological Analysis of Molecular Dynamics Simulations of IgG1 Fc Domains. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-22 Melinda Kleczynski,Christina Bergonzo,Anthony J Kearsley
Monoclonal antibodies are utilized in a wide range of biomedical applications. The NIST monoclonal antibody is a resource for developing analysis methods for monoclonal antibody based biopharmaceutical platforms. Techniques from topological data analysis quantify structural features such as loops and tunnels which are not easily measured by classical data analysis methods. In this paper, we introduce
-
Allosteric Regulation of Enzymatic Catalysis through Mechanical Force. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-22 Zeyu Zhang,Yangyang Zhang,Weitong Ren,Weiwei Zhang,Wenfei Li,Wei Wang
Mechanical force has been increasingly recognized to play crucial roles in regulating various cellular processes, which has inspired wide interest in elucidating the biophysical mechanism underlying these mechanobiological processes. In this work, we investigate the mechanical regulation of enzyme catalysis by developing a residue-resolved computational model capable of describing the full catalytic
-
Unitary Block-Correlated Coupled Cluster Ansatz Based on the Generalized Valence Bond Wave Function for Quantum Simulation. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-22 Jiaqi Hu,Qingchun Wang,Shuhua Li
Strongly correlated (SC) systems present significant challenges for classical quantum chemistry methods. Quantum computing, particularly the variational quantum eigensolver (VQE), offers a promising framework to address these challenges by inherently supporting exponentially large configuration spaces. However, its application to SC systems remains limited due to the single-reference nature of the
-
Ensemble Adaptive Sampling Scheme: Identifying an Optimal Sampling Strategy via Policy Ranking. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-22 Hassan Nadeem,Diwakar Shukla
Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamic behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of the phase space. In this work, we present a framework for identifying the optimal sampling policy through metric-driven ranking. Our approach
-
Unraveling Disease-Associated PIWI-Interacting RNAs with a Contrastive Learning Methods. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-22 Xiaowen Hu,Hao Sun,Linchao Shan,Chenxi Ma,Hanming Quan,Yuanpeng Zhang,Jiaxuan Zhang,Ziyu Fan,Yongjun Tang,Lei Deng
PIWI-interacting RNAs (piRNAs) are a class of small, non-coding RNAs predominantly expressed in the germ cells of animals and play a crucial role in maintaining genomic integrity, mediating transposon suppression, and ensuring gene stability. Beyond their functions in reproductive cells, piRNAs also play roles in various human diseases, including cancer, suggesting their potential as significant biomarkers
-
Rational Computational Workflow for Structure-Guided Discovery of a Novel USP7 Inhibitor. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-22 Mitul Srivastava,Deepika Kumari,Sushanta Majumder,Nitu Singh,Rajani Mathur,Tushar Kanti Maiti,Ajay Kumar,Shailendra Asthana
Rationally applied, structurally guided computational methods hold the promise of identifying potent and distinct chemotypes while enabling the precise targeting of structural determinants. Here, we implemented a computational workflow combining insights from cocrystal poses and monitoring the dynamical structural determinants from our previous studies for the identification of potential candidates
-
SeqMG-RPI: A Sequence-Based Framework Integrating Multi-Scale RNA Features and Protein Graphs for RNA-Protein Interaction Prediction. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-22 Teng Ma,Mingjian Jiang,Shunpeng Pang,Zhi Zhang,Huaibin Hang,Wei Zhou,Yuanyuan Zhang
RNA-protein interaction (RPI) plays a crucial role in cell biology, and accurate prediction of RPI is essential to understand molecular mechanisms and advance disease research. Some existing RPI prediction methods typically rely on a single feature and there is significant room for improvement. In this paper, we propose a novel sequence-based RPI prediction method, called SeqMG-RPI. For RNA, SeqMG-RPI
-
Computational Strategies for Broad Spectrum Venom Phospholipase A2 Inhibitors. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-22 David A Poole,Laura-Oana Albulescu,Jeroen Kool,Nicholas R Casewell,Daan P Geerke
Snakebite envenoming is a persistent cause of mortality and morbidity worldwide due to the logistical challenges and costs of current antibody-based treatments. Their persistence motivates a broad interest in the discovery of inhibitors against multispecies venom phospholipase A2 (PLA2), which are underway as an alternative or supplemental treatment to improve health outcomes. Here, we present new
-
Assessment of Free Energies From Electrostatic Embedding Density Functional Tight Binding‐Based/Molecular Mechanics in Periodic Boundary Conditions J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-22 Simone Bonfrate, Woojin Park, Dulce Trejo‐Zamora, Nicolas Ferré, Cheol Ho Choi, Miquel Huix‐Rotllant
-
NeuroPred-AIMP: Multimodal Deep Learning for Neuropeptide Prediction via Protein Language Modeling and Temporal Convolutional Networks. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-21 Jinjin Li,Shuwen Xiong,Hua Shi,Feifei Cui,Zilong Zhang,Leyi Wei
Neuropeptides are key signaling molecules that regulate fundamental physiological processes ranging from metabolism to cognitive function. However, accurate identification is a huge challenge due to sequence heterogeneity, obscured functional motifs and limited experimentally validated data. Accurate identification of neuropeptides is critical for advancing neurological disease therapeutics and peptide-based
-
Role of Residues Undergoing Hereditary Spastic Paraplegias Mutations: Insights from Simulating the Spiral to Ring Transition in Katanin. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-21 Maria S Kelly,Riccardo Capelli,Ruxandra I Dima,Paolo Carloni
Several dozen mutations in the M87 isoform of the spastin enzyme have been associated with mobility impairment in hereditary spastic paraplegias. Some of them impact the structural determinants of two functional conformations of the protein: spiral and ring. Here we investigate the possible patterns between these disease-related residues in spastin and aligned regions in the closely related protein
-
Methyl•••Methyl Interactions in Proteins: Insights from Structural and Computational Studies. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-21 Juhi Dutta,Akshay Kumar Sahu,Subhrakant Jena,Himansu S Biswal
The low affinity of nonpolar groups for water gave birth to one of the significant supramolecular forces known as hydrophobic interaction as early as 1937. While the precise origins of this phenomenon remain debated, the significant role of London dispersion forces in stabilizing nonpolar complexes is well-established. This article presents a comprehensive investigation of the nature and strength of
-
-
Design of BCC/FCC dual-solid solution refractory high-entropy alloys through CALPHAD, machine learning and experimental methods npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-20 Longjun He, Chaoyue Wang, Mina Zhang, Jinghao Li, Tianlun Chen, Xianglin Zhou
-
AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-20 Siqin Cao,Feliks Nüske,Bojun Liu,Micheline B Soley,Xuhui Huang
Elucidating collective variables (CVs) for biomolecular dynamics is crucial for understanding numerous biological processes. By leveraging the tensor-train data structure, a multilinear version of the AMUSE (Algorithm for Multiple Unknown Signals) algorithm for Koopman approximation (AMUSEt) was recently developed to identify CVs for biomolecular dynamics. To find slow CVs, AMUSEt transforms input
-
Extraction of Double Photoionization Amplitudes from Full-Scattered Wave Functions. J. Chem. Theory Comput. (IF 5.7) Pub Date : 2025-04-20 Alexander A Sadamune,Robert R Lucchese,C William McCurdy,Frank L Yip
Although cross sections for double photoionization (DPI) are much smaller than single photoionization cross sections, DPI by a single photon is a sensitive means of probing correlated electron dynamics. We extend a rigorous method for computing double ionization amplitudes in both time-independent and time-dependent computational formalisms by eliminating the requirement that the one-electron testing
-
Data-Driven Insights into Porphyrin Geometry: Interpretable AI for Non-Planarity and Aromaticity Analyses. J. Chem. Inf. Model. (IF 5.6) Pub Date : 2025-04-20 Shachar Fite,Zeev Gross
Porphyrins are involved in numerous and very different chemical and biological processes, due to the sensitivity of their application-relevant properties to subtle structural changes. Applying modern machine learning methodology is very appealing for discovering structure-activity relationships that can be used for design of tailor-made porphyrins for specific purposes. For achieving this goal, a high-quality
-
The Effects of Conformational Sampling and QM Region Size in QM/MM Simulations: An Adaptive QM/MM Study With Model Systems J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-19 Holden Paz, Silvan Beck, Richmond Lee, Junming Ho, Haibo Yu
-
Computational Study of Complexation in LiH:nNH3 (n = 1–4) Clusters: An Interplay Among Hydrogen, Dihydrogen, and Lithium Bonds J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-19 Krishna, Lalit Kumar Saini, Mukesh Pandey
-
P-type dopability in Half-Heusler thermoelectric semiconductors npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-19 Lirong Hu, Shen Han, Tiejun Zhu, Tianqi Deng, Chenguang Fu
-
On the Use of PDB X‐Ray Crystal Structures as Force Field Target and Validation Data for Pyranose Ring Puckering J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-19 Olgun Guvench, Andrew L. Straffin
-
Giant Dipole Moments: Remarkable Effects Mono‐, Di‐, and Tri‐ Hydrated 5,6‐Diaminobenzene‐1,2,3,4‐Tetracarbonnitrile J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-19 Katherine Stanley, R. Houston Givhan, Justin M. Turney, Henry F. Schaefer
-
Collisional Dynamics of Newly Detected Protonated Dicyanoacetylene (NC4NH+$$ {\mathrm{NC}}_4{\mathrm{NH}}^{+} $$) With He at Low Interstellar Temperatures J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-19 Pooja Chahal, T. J. Dhilip Kumar
-
IMPACT‐4CCS: Integrated Modeling and Prediction Using Ab Initio and Trained Potentials for Collision Cross Sections J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-19 Carson Farmer, Hector Medina
-
Atomic Neural Network for Calculation of Solvation Free Energies in Organic Solvents J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-19 Sergei F. Vyboishchikov
-
Substrates (Acyl‐CoA and Diacylglycerol) Entry and Products (CoA and Triacylglycerol) Egress Pathways in DGAT1 J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-19 Hwayoung Lee, Wonpil Im
-
Through-Space/Through-Bond Energy Decomposition Analysis Clarifies the Mechanism of Transition Mutation in DNA Containing O6-Methylguanine Lesion J. Comput. Chem. (IF 3.4) Pub Date : 2025-04-18 Mariia V. Ivonina, Yuuichi Orimoto, Yuriko Aoki
-
Machine learning enabled accurate prediction of structural and magnetic properties of cobalt ferrite npj Comput. Mater. (IF 9.4) Pub Date : 2025-04-17 Ying Fang, Suraj Mullurkara, Keith M. Taddei, Paul R. Ohodnicki, Guofeng Wang