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Does the Traditional Band Picture Describe the Electronic Structure of Doped Conjugated Polymers? TD-DFT and Natural Transition Orbital Study of Doped P3HT J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-28 Eric C. Wu, Benjamin J. Schwartz
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Data Driven Discovery of MOFs for Hydrogen Gas Adsorption J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-27 Samrendra K. Singh, Abhishek T. Sose, Fangxi Wang, Karteek K. Bejagam, Sanket A. Deshmukh
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Conformational Switch of the 250s Loop Enables the Efficient Transglycosylation in GH Family 77 J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-28 Zhiyong Guo, Lei Wang, Deming Rao, Weiqiong Liu, Miaomiao Xue, Qisheng Fu, Mengwei Lu, Lingqia Su, Sheng Chen, Binju Wang, Jing Wu
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Viscosity Prediction of High-Concentration Antibody Solutions with Atomistic Simulations J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-27 Tobias M. Prass, Patrick Garidel, Michaela Blech, Lars V. Schäfer
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Computed Protein–Protein Enthalpy Signatures as a Tool for Identifying Conformation Sampling Problems J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-27 Süleyman Selim Çınaroğlu, Philip C. Biggin
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Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-25 Yingying Ma, Minjie Li, Yongkun Mu, Gang Wang, Wencong Lu
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Reliable and accurate prediction of basic pK\(_a\) values in nitrogen compounds: the pK\(_a\) shift in supramolecular systems as a case study J. Cheminfom. (IF 8.6) Pub Date : 2023-09-28 Jackson J. Alcázar, Alessandra C. Misad Saide, Paola R. Campodónico
This article presents a quantitative structure–activity relationship (QSAR) approach for predicting the acid dissociation constant (pK $$_a$$ ) of nitrogenous compounds, including those within supramolecular complexes based on cucurbiturils. The model combines low-cost quantum mechanical calculations with QSAR methodology and linear regressions to achieve accurate predictions for a broad range of nitrogen-containing
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Applications and Advances in Machine Learning Force Fields J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-26 Shiru Wu, Xiaowei Yang, Xun Zhao, Zhipu Li, Min Lu, Xiaoji Xie, Jiaxu Yan
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Translocation Processes of Pt(II)-Based Drugs through Human Breast Cancer Cell Membrane: In Silico Experiments J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-26 Eduardo R. Almeida, Priscila V. Z. Capriles Goliatt, Hélio F. Dos Santos, Fabien Picaud
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Augmented Reality for Enhanced Visualization of MOF Adsorbents J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-26 Lawson T. Glasby, Rama Oktavian, Kewei Zhu, Joan L. Cordiner, Jason C. Cole, Peyman Z. Moghadam
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Discrepancies and error evaluation metrics for machine learning interatomic potentials npj Comput. Mater. (IF 9.7) Pub Date : 2023-09-26 Yunsheng Liu, Xingfeng He, Yifei Mo
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Reducing the Cost of Neural Network Potential Generation for Reactive Molecular Systems J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-25 Krystof Brezina, Hubert Beck, Ondrej Marsalek
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Realizing unipolar and bipolar intrinsic skyrmions in MXenes from high-fidelity first-principles calculations npj Comput. Mater. (IF 9.7) Pub Date : 2023-09-25 Arnab Kabiraj, Santanu Mahapatra
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A molecule perturbation software library and its application to study the effects of molecular design constraints J. Cheminfom. (IF 8.6) Pub Date : 2023-09-26 Alan Kerstjens, Hans De Winter
Computational molecular design can yield chemically unreasonable compounds when performed carelessly. A popular strategy to mitigate this risk is mimicking reference chemistry. This is commonly achieved by restricting the way in which molecules are constructed or modified. While it is well established that such an approach helps in designing chemically appealing molecules, concerns about these restrictions
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The stability of oxygen-centered radicals and its response to hydrogen bonding interactions J. Comput. Chem. (IF 3.0) Pub Date : 2023-09-25 Vasilii Korotenko, Hendrik Zipse
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DOCK 6: Incorporating hierarchical traversal through precomputed ligand conformations to enable large-scale docking J. Comput. Chem. (IF 3.0) Pub Date : 2023-09-25 Trent E. Balius, Y. Stanley Tan, Mayukh Chakrabarti
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Mechanisms of CH4 activation over oxygen-preadsorbed transition metals by ReaxFF and AIMD simulations J. Comput. Chem. (IF 3.0) Pub Date : 2023-09-25 Jie Wang, Gui-Chang Wang
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Toward accurate modeling of structure and energetics of bulk hexagonal boron nitride J. Comput. Chem. (IF 3.0) Pub Date : 2023-09-22 Michal Novotný, Matúš Dubecký, František Karlický
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Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-25 Sina Stocker, Hyunwook Jung, Gábor Csányi, C. Franklin Goldsmith, Karsten Reuter, Johannes T. Margraf
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Mapping Molecular Hamiltonians into Hamiltonians of Modular cQED Processors J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-21 Ningyi Lyu, Alessandro Miano, Ioannis Tsioutsios, Rodrigo G. Cortiñas, Kenneth Jung, Yuchen Wang, Zixuan Hu, Eitan Geva, Sabre Kais, Victor S. Batista
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Koopmans’ Theorem-Compliant Long-Range Corrected (KTLC) Density Functional Mediated by Black-Box Optimization and Data-Driven Prediction for Organic Molecules J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-20 Kei Terayama, Yamato Osaki, Takehiro Fujita, Ryo Tamura, Masanobu Naito, Koji Tsuda, Toru Matsui, Masato Sumita
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Probabilistic generative transformer language models for generative design of molecules J. Cheminfom. (IF 8.6) Pub Date : 2023-09-25 Lai Wei, Nihang Fu, Yuqi Song, Qian Wang, Jianjun Hu
Self-supervised neural language models have recently found wide applications in the generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult
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PLPCA: Persistent Laplacian-Enhanced PCA for Microarray Data Analysis J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-22 Sean Cottrell, Rui Wang, Guo-Wei Wei
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A Discrete-Variable Local Diabatic Representation of Conical Intersection Dynamics J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-22 Bing Gu
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SOiCISCF: Combining SOiCI and iCISCF for Variational Treatment of Spin–Orbit Coupling J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-20 Yang Guo, Ning Zhang, Wenjian Liu
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AdsorbML: a leap in efficiency for adsorption energy calculations using generalizable machine learning potentials npj Comput. Mater. (IF 9.7) Pub Date : 2023-09-22 Janice Lan, Aini Palizhati, Muhammed Shuaibi, Brandon M. Wood, Brook Wander, Abhishek Das, Matt Uyttendaele, C. Lawrence Zitnick, Zachary W. Ulissi
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Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks npj Comput. Mater. (IF 9.7) Pub Date : 2023-09-21 Yigitcan Comlek, Thang Duc Pham, Randall Q. Snurr, Wei Chen
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Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis J. Cheminfom. (IF 8.6) Pub Date : 2023-09-23 Ximin Hu, Derek Mar, Nozomi Suzuki, Bowei Zhang, Katherine T. Peter, David A. C. Beck, Edward P. Kolodziej
Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction
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Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors J. Cheminfom. (IF 8.6) Pub Date : 2023-09-23 Prasannavenkatesh Durai, Sue Jung Lee, Jae Wook Lee, Cheol-Ho Pan, Keunwan Park
Machine learning-based chemical screening has made substantial progress in recent years. However, these predictions often have low accuracy and high uncertainty when identifying new active chemical scaffolds. Hence, a high proportion of retrieved compounds are not structurally novel. In this study, we proposed a strategy to address this issue by iteratively optimizing an evolutionary chemical binding
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HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-22 Ali Hashemi, Sana Bougueroua, Marie-Pierre Gaigeot, Evgeny A. Pidko
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DASH: Dynamic Attention-Based Substructure Hierarchy for Partial Charge Assignment J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-22 Marc T. Lehner, Paul Katzberger, Niels Maeder, Carl C.G. Schiebroek, Jakob Teetz, Gregory A. Landrum, Sereina Riniker
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miPEPPred-FRL: A Novel Method for Predicting Plant MiRNA-Encoded Peptides Using Adaptive Feature Representation Learning J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-21 Haibin Li, Jun Meng, Zhaowei Wang, Youwei Tang, Shihao Xia, Yu Wang, Zhaojing Qin, Yushi Luan
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ReactionDataExtractor 2.0: A Deep Learning Approach for Data Extraction from Chemical Reaction Schemes J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-20 Damian M. Wilary, Jacqueline M. Cole
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SILVR: Guided Diffusion for Molecule Generation J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-19 Nicholas T. Runcie, Antonia S.J.S. Mey
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Large sliding regulation in van der waals layered nonlinear optical ternary chalcogenides npj Comput. Mater. (IF 9.7) Pub Date : 2023-09-22 Qingchen Wu, Lei Kang, Jian Wu, Zheshuai Lin
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Sequence-Dependent Orientational Coupling and Electrostatic Attraction in Cation-Mediated DNA–DNA Interactions J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-20 Weiwei He, Xiangyun Qiu, Serdal Kirmizialtin
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Computing Free Energies of Fold-Switching Proteins Using MELD x MD J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-19 Sridip Parui, Emiliano Brini, Ken A. Dill
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Machine Learning-Guided Adaptive Parametrization for Coupling Terms in a Mixed United-Atom/Coarse-Grained Model for Diphenylalanine Self-Assembly in Aqueous Ionic Liquids J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-19 Yang Ge, Xueping Wang, Qiang Zhu, Yuqin Yang, Hao Dong, Jing Ma
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Toward Universal Substituent Constants: Relating QTAIM Functional Group Descriptors to Substituent Effect Proxies J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-20 Kevin M. Lefrancois-Gagnon, Robert C. Mawhinney
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RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-19 Yingchao Yan, Yang Zhao, Huifeng Yao, Jie Feng, Li Liang, Weijie Han, Xiaohe Xu, Chengtao Pu, Chengdong Zang, Lingfeng Chen, Yuanyuan Li, Haichun Liu, Tao Lu, Yadong Chen, Yanmin Zhang
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MIST-CF: Chemical Formula Inference from Tandem Mass Spectra J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-19 Samuel Goldman, Jiayi Xin, Joules Provenzano, Connor W. Coley
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Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists J. Cheminfom. (IF 8.6) Pub Date : 2023-09-19 Christopher Secker, Konstantin Fackeldey, Marcus Weber, Sourav Ray, Christoph Gorgulla, Christof Schütte
Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the $$\mu$$ -opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding
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Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding J. Cheminfom. (IF 8.6) Pub Date : 2023-09-19 Thomas E. Hadfield, Jack Scantlebury, Charlotte M. Deane
Many recently proposed structure-based virtual screening models appear to be able to accurately distinguish high affinity binders from non-binders. However, several recent studies have shown that they often do so by exploiting ligand-specific biases in the dataset, rather than identifying favourable intermolecular interactions in the input protein-ligand complex. In this work we propose a novel approach
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Integrating synthetic accessibility with AI-based generative drug design J. Cheminfom. (IF 8.6) Pub Date : 2023-09-19 Maud Parrot, Hamza Tajmouati, Vinicius Barros Ribeiro da Silva, Brian Ross Atwood, Robin Fourcade, Yann Gaston-Mathé, Nicolas Do Huu, Quentin Perron
Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been
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School of cheminformatics in Latin America J. Cheminfom. (IF 8.6) Pub Date : 2023-09-19 Karla Gonzalez-Ponce, Carolina Horta Andrade, Fiona Hunter, Johannes Kirchmair, Karina Martinez-Mayorga, José L. Medina-Franco, Matthias Rarey, Alexander Tropsha, Alexandre Varnek, Barbara Zdrazil
We report the major highlights of the School of Cheminformatics in Latin America, Mexico City, November 24–25, 2022. Six lectures, one workshop, and one roundtable with four editors were presented during an online public event with speakers from academia, big pharma, and public research institutions. One thousand one hundred eighty-one students and academics from seventy-nine countries registered for
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Extended study on atomic featurization in graph neural networks for molecular property prediction J. Cheminfom. (IF 8.6) Pub Date : 2023-09-19 Agnieszka Wojtuch, Tomasz Danel, Sabina Podlewska, Łukasz Maziarka
Graph neural networks have recently become a standard method for analyzing chemical compounds. In the field of molecular property prediction, the emphasis is now on designing new model architectures, and the importance of atom featurization is oftentimes belittled. When contrasting two graph neural networks, the use of different representations possibly leads to incorrect attribution of the results
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Machine Learning-Based Prediction of Activation Energies for Chemical Reactions on Metal Surfaces J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-18 Daniel J. Hutton, Kari E. Cordes, Carine Michel, Florian Göltl
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Expanding Bioactive Fragment Space with the Generated Database GDB-13s J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-18 Ye Buehler, Jean-Louis Reymond
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Self-consistent field method for open-shell systems within the density-matrix functional theory J. Comput. Chem. (IF 3.0) Pub Date : 2023-09-13 Marinela Irimia, Jian Wang
The unrestricted Hartree-Fock method is extended to correlation calculation within the density-matrix functional theory. The method is derived from an entropic cumulant functional for the correlation energy. The eigenvalue equations for the spin-orbitals are modified by the orbital occupation numbers. The Euler equation for the occupation numbers results in the Fermi-Dirac distribution, which is very
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BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-17 Kong Meng, Chenyu Huang, Yaxin Wang, Yunjiang Zhang, Shuyuan Li, Zhaolin Fang, Huimin Wang, Shihao Wei, Shaorui Sun
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Growth Reduction of Similarity-Transformed Electronic Hamiltonians in Qubit Space J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-16 Robert A. Lang, Aadithya Ganeshram, Artur F. Izmaylov
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Coarse-Grained Modeling Using Neural Networks Trained on Structural Data J. Chem. Theory Comput. (IF 5.5) Pub Date : 2023-09-15 Mikhail Ivanov, Maksim Posysoev, Alexander P. Lyubartsev
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Telescoping-Solvation-Box Protocol-Based Umbrella Sampling Method for Potential Mean Force Estimation J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-16 Diship Srivastava, Niladri Patra
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Data-Driven Prediction of Configurational Stability of Molecule-Adsorbed Heterogeneous Catalysts J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-15 Juhwan Noh, Hyunju Chang
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Correcting charge distribution in reduced Li-molecule pair for computational screening of battery solvents J. Comput. Chem. (IF 3.0) Pub Date : 2023-09-15 M. A. Orekhov
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Explainable Graph Neural Networks with Data Augmentation for Predicting pKa of C–H Acids J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-14 Hongle An, Xuyang Liu, Wensheng Cai, Xueguang Shao
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Editorial: Harnessing the Power of Large Language Model-Based Chatbots for Scientific Discovery J. Chem. Inf. Model. (IF 5.6) Pub Date : 2023-09-11 Kenneth M. Merz, Guo-Wei Wei, Feng Zhu
With its ability to comprehend vast amounts of information, process complex data, and generate insights that were previously difficult to attain, ChatGPT and various other Chatbots are anticipated to have great potential for revolutionizing scientific discovery. First, ChatGPT can streamline the scientific discovery process by quickly sifting through the vastness of the scientific literature and identifying
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rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation J. Cheminfom. (IF 8.6) Pub Date : 2023-09-15 Gerard Baquer, Lluc Sementé, Pere Ràfols, Lucía Martín-Saiz, Christoph Bookmeyer, José A. Fernández, Xavier Correig, María García-Altares
Matrix-Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) spatially resolves the chemical composition of tissues. Lipids are of particular interest, as they influence important biological processes in health and disease. However, the identification of lipids in MALDI-MSI remains a challenge due to the lack of chromatographic separation or untargeted tandem mass spectrometry
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Tuning ultrafast time-evolution of photo-induced charge-transfer states: A real-time electronic dynamics study in substituted indenotetracene derivatives J. Comput. Chem. (IF 3.0) Pub Date : 2023-09-14 Luigi Crisci, Federico Coppola, Alessio Petrone, Nadia Rega
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The verification of delta SCF and Slater's transition state theory for the calculation of core ionization energy J. Comput. Chem. (IF 3.0) Pub Date : 2023-09-14 Kimihiko Hirao, Takahito Nakajima, Bun Chan, Ho-Jin Lee