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Journals Journal of Chemical Informatio...

Journal of Chemical Information and Modeling

https://read.qxmd.com/read/38536765/autobench-v1-0-benchmarking-automation-for-electronic-structure-calculations
#1
JOURNAL ARTICLE
Rodrigo A Cormanich, Gabriel D da Silva
This work reports on new software for automatic conformer energy benchmarking calculations for flexible molecules. The software workflow consists of four parts: conformational search, preoptimization, optimization, and frequency calculations at a higher level and last calculations using several theoretical levels. The software was written to be user-friendly and versatile to be used by nonexperts in computational chemistry. Any theoretical levels available in either Gaussian 16 or ORCA 5 may be applied in the benchmarking study...
March 27, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38533705/hsqc-spectra-simulation-and-matching-for-molecular-identification
#2
JOURNAL ARTICLE
Martin Priessner, Richard J Lewis, Magnus J Johansson, Jonathan M Goodman, Jon Paul Janet, Anna Tomberg
In the pursuit of improved compound identification and database search tasks, this study explores heteronuclear single quantum coherence (HSQC) spectra simulation and matching methodologies. HSQC spectra serve as unique molecular fingerprints, enabling a valuable balance of data collection time and information richness. We conducted a comprehensive evaluation of the following four HSQC simulation techniques: ACD/Labs (ACD), MestReNova (MNova), Gaussian NMR calculations (DFT), and a graph-based neural network (ML)...
March 27, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38533570/binding-sites-of-bicarbonate-in-phosphoenolpyruvate-carboxylase
#3
JOURNAL ARTICLE
Nicolas Chéron
Phosphoenolpyruvate carboxylase (PEPC) is used in plant metabolism for fruit maturation or seed development as well as in the C4 and crassulacean acid metabolism (CAM) mechanisms in photosynthesis, where it is used for the capture of hydrated CO2 (bicarbonate). To find the yet unknown binding site of bicarbonate in this enzyme, we have first identified putative binding sites with nonequilibrium molecular dynamics simulations and then ranked these sites with alchemical free energy calculations with corrections of computational artifacts...
March 27, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38532612/sequential-contrastive-and-deep-learning-models-to-identify-selective-butyrylcholinesterase-inhibitors
#4
JOURNAL ARTICLE
Mustafa Kemal Ozalp, Patricia A Vignaux, Ana C Puhl, Thomas R Lane, Fabio Urbina, Sean Ekins
Butyrylcholinesterase (BChE) is a target of interest in late-stage Alzheimer's Disease (AD) where selective BChE inhibitors (BIs) may offer symptomatic treatment without the harsh side effects of acetylcholinesterase (AChE) inhibitors. In this study, we explore multiple machine learning strategies to identify BIs in silico , optimizing for precision over all other metrics. We compare state-of-the-art supervised contrastive learning (CL) with deep learning (DL) and Random Forest (RF) machine learning, across single and sequential modeling configurations, to identify the best models for BChE selectivity...
March 26, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38530291/deepka-web-server-high-throughput-protein-p-k-a-prediction
#5
JOURNAL ARTICLE
Zhitao Cai, Hao Peng, Shuo Sun, Jiahao He, Fangfang Luo, Yandong Huang
DeepKa is a deep-learning-based protein p K a predictor proposed in our previous work. In this study, a web server was developed that enables online protein p K a prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how p K a 's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent...
March 26, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38529913/evaluation-of-open-source-large-language-models-for-metal-organic-frameworks-research
#6
JOURNAL ARTICLE
Xuefeng Bai, Yabo Xie, Xin Zhang, Honggui Han, Jian-Rong Li
Along with the development of machine learning, deep learning, and large language models (LLMs) such as GPT-4 (GPT: Generative Pre-Trained Transformer), artificial intelligence (AI) tools have been playing an increasingly important role in chemical and material research to facilitate the material screening and design. Despite the exciting progress of GPT-4 based AI research assistance, open-source LLMs have not gained much attention from the scientific community. This work primarily focused on metal-organic frameworks (MOFs) as a subdomain of chemistry and evaluated six top-rated open-source LLMs with a comprehensive set of tasks including MOFs knowledge, basic chemistry knowledge, in-depth chemistry knowledge, knowledge extraction, database reading, predicting material property, experiment design, computational scripts generation, guiding experiment, data analysis, and paper polishing, which covers the basic units of MOFs research...
March 26, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38529877/from-nmr-to-ai-designing-a-novel-chemical-representation-to-enhance-machine-learning-predictions-of-physicochemical-properties
#7
JOURNAL ARTICLE
Arkadiusz Leniak, Wojciech Pietruś, Rafał Kurczab
A novel approach to the utilization of nuclear magnetic resonance (NMR) spectroscopy data in the prediction of logD through machine learning algorithms is shown. In the analysis, a data set of 754 chemical compounds, organized into 30 clusters, was evaluated using advanced machine learning models, such as Support Vector Regression (SVR), Gradient Boosting, and AdaBoost, and comprehensive validation and testing methods were employed, including 10-fold cross-validation, bootstrapping, and leave-one-out. The study revealed the superior performance of the Bucket Integration method for dimensionality reduction, consistently yielding the lowest root mean square error (RMSE) across all data sets and normalization schemes...
March 26, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38528706/correlation-of-the-molecular-cross-sectional-area-of-organic-monofunctional-compounds-with-topological-descriptors
#8
JOURNAL ARTICLE
Didier Desmecht, Vincent Dubois
The molecular cross-sectional area (σ) has proved to be an interesting molecular measure not only in the field of adsorption phenomena on solids but also in biochemistry, physiology, or surfactant chemistry. The existing methods to estimate the cross-sectional areas are either not readily applicable or can only be applied to a limited number of compounds. The aim of this work was to describe a method, as general as possible, quick and easy to perform. To that end, the molecular cross-sectional areas were correlated with topological indices...
March 25, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38528664/hysteresis-elimination-for-an-anisotropic-liquid-crystal-model-via-molecule-design-and-replica-exchange-optimization
#9
JOURNAL ARTICLE
Akie Kowaguchi, Paul E Brumby, Kenji Yasuoka
The phenomenon of hysteresis in simulations, in which a system's current state is correlated to previous states and inhibits the transition to a more stable phase, may often lead to misleading results in physical chemistry. In this study, in addition to the replica exchange method (REM), a novel approach was taken by combining an evolution strategy based on the evolutionary principles of nature to predict phase transitions for the Hess-Su liquid-crystal model. In this model, an anisotropy term is added to the simple 6-12 Lennard-Jones model to intuitively reproduce the behavior of liquid crystals...
March 25, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38526504/prointerval-validation-of-protein-protein-interfaces-through-learned-interface-representations
#10
JOURNAL ARTICLE
Damla Ovek, Ozlem Keskin, Attila Gursoy
Proteins are vital components of the biological world and serve a multitude of functions. They interact with other molecules through their interfaces and participate in crucial cellular processes. Disruption of these interactions can have negative effects on organisms, highlighting the importance of studying protein-protein interfaces for developing targeted therapies for diseases. Therefore, the development of a reliable method for investigating protein-protein interactions is of paramount importance. In this work, we present an approach for validating protein-protein interfaces using learned interface representations...
March 25, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38523272/empowering-graph-neural-networks-with-block-based-dual-adaptive-deep-adjustment-for-drug-resistance-related-ncrna-discovery
#11
JOURNAL ARTICLE
Yi Zhang, Xuanzhao Li
Drug resistance to chemotherapeutic agents remains a formidable challenge in cancer treatment, significantly impacting treatment efficacy. Extensive research has exposed the intimate involvement of noncoding RNAs (ncRNAs) in conferring resistance to cancer drugs. Understanding the intricate associations between ncRNAs and drug resistance is of pivotal importance in advancing clinical interventions and expediting drug development. However, traditional biological experimental methods are hampered by limitations, such as labor intensiveness, time consumption, and constraints in scalability...
March 24, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38523267/learning-association-characteristics-by-dynamic-hypergraph-and-gated-convolution-enhanced-pairwise-attributes-for-prediction-of-disease-related-lncrnas
#12
JOURNAL ARTICLE
Ping Xuan, Siyuan Lu, Hui Cui, Shuai Wang, Toshiya Nakaguchi, Tiangang Zhang
As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models...
March 24, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38523266/calcium-driven-in-silico-inactivation-of-a-human-olfactory-receptor
#13
JOURNAL ARTICLE
Lorenza Pirona, Federico Ballabio, Mercedes Alfonso-Prieto, Riccardo Capelli
Conformational changes as well as molecular determinants related to the activation and inactivation of olfactory receptors are still poorly understood due to the intrinsic difficulties in the structural determination of this GPCR family. Here, we perform, for the first time, the in silico inactivation of human olfactory receptor OR51E2, highlighting the possible role of calcium in this receptor state transition. Using molecular dynamics simulations, we show that a divalent ion in the ion binding site, coordinated by two acidic residues at positions 2...
March 24, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38523265/predicting-the-activity-of-unidentified-chemicals-in-complementary-bioassays-from-the-hrms-data-to-pinpoint-potential-endocrine-disruptors
#14
JOURNAL ARTICLE
Ida Rahu, Meelis Kull, Anneli Kruve
The majority of chemicals detected via nontarget liquid chromatography high-resolution mass spectrometry (HRMS) in environmental samples remain unidentified, challenging the capability of existing machine learning models to pinpoint potential endocrine disruptors (EDs). Here, we predict the activity of unidentified chemicals across 12 bioassays related to EDs within the Tox21 10K dataset. Single- and multi-output models, utilizing various machine learning algorithms and molecular fingerprint features as an input, were trained for this purpose...
March 24, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38520328/discovery-of-covalent-lead-compounds-targeting-3cl-protease-with-a-lateral-interactions-spiking-neural-network
#15
JOURNAL ARTICLE
Zhihao Gu, Yong Yan, Hanwen Liu, Di Wu, Hequan Yao, Kejiang Lin, Xuanyi Li
Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale because covalent docking requires determination of the covalent reaction type of the compound. Here, we propose to use deep learning of a lateral interactions spiking neural network to construct a covalent lead compound screening model to quickly screen covalent lead compounds...
March 23, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38517012/molecular-insights-into-the-variability-in-infection-and-immune-evasion-capabilities-of-sars-cov-2-variants-a-sequence-and-structural-investigation-of-the-rbd-domain
#16
JOURNAL ARTICLE
Tian Hua Wang, Hai Ping Shao, Bing Qiang Zhao, Hong Lin Zhai
As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continuously emerge, an increasing number of mutations are accumulating in the Spike protein receptor-binding domain (RBD) region. Through sequence analysis of various Variants of Concern (VOC), we identified that they predominantly fall within the ο lineage although recent variants introduce any novel mutations in the RBD. Molecular dynamics simulations were employed to compute the binding free energy of these variants with human Angiotensin-converting enzyme 2 (ACE2)...
March 22, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38516950/lgga-mpp-local-geometry-guided-graph-attention-for-molecular-property-prediction
#17
JOURNAL ARTICLE
Lei Song, Huimin Zhu, Kaili Wang, Min Li
Molecular property prediction is a fundamental task of drug discovery. With the rapid development of deep learning, computational approaches for predicting molecular properties are experiencing increasing popularity. However, these existing methods often ignore the 3D information on molecules, which is critical in molecular representation learning. In the past few years, several self-supervised learning (SSL) approaches have been proposed to exploit the geometric information by using pre-training on 3D molecular graphs and fine-tuning on 2D molecular graphs...
March 22, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38514966/cfssynergy-combining-feature-based-and-similarity-based-methods-for-drug-synergy-prediction
#18
JOURNAL ARTICLE
Fatemeh Rafiei, Hojjat Zeraati, Karim Abbasi, Parvin Razzaghi, Jahan B Ghasemi, Mahboubeh Parsaeian, Ali Masoudi-Nejad
Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor...
March 21, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38511939/mechanism-of-agt-mediated-repair-of-dg-dc-cross-links-in-the-drug-resistance-to-chloroethylnitrosoureas-molecular-docking-md-simulation-and-oniom-qm-mm-investigation
#19
JOURNAL ARTICLE
Jiaojiao Wang, Ting Ren, Guohui Sun, Na Zhang, Lijiao Zhao, Rugang Zhong
Chloroethylnitrosoureas (CENUs) are important chemotherapies applied in the treatment of cancer. They exert anticancer activity by inducing DNA interstrand cross-links (ICLs) via the formation of two O 6 -alkylguanine intermediates, O 6 -chloroethylguanine ( O 6 -ClEtG) and N 1, O 6 -ethanoguanine ( N 1, O 6 -EtG). However, O 6 -alkylguanine-DNA alkyltransferase (AGT), a DNA-repair enzyme, can restore the O 6 -alkylguanine damages and thereby obstruct the formation of ICLs (dG-dC cross-link). In this study, the inhibitory mechanism of ICL formation was investigated to elucidate the drug resistance of CENUs mediated by AGT in detail...
March 21, 2024: Journal of Chemical Information and Modeling
https://read.qxmd.com/read/38506664/elvim-exploring-biomolecular-energy-landscapes-through-multidimensional-visualization
#20
JOURNAL ARTICLE
Rafael Giordano Viegas, Ingrid B S Martins, Murilo Nogueira Sanches, Antonio B Oliveira Junior, Juliana B de Camargo, Fernando V Paulovich, Vitor B P Leite
Molecular dynamics (MD) simulations provide a powerful means of exploring the dynamic behavior of biomolecular systems at the atomic level. However, analyzing the vast data sets generated by MD simulations poses significant challenges. This article discusses the energy landscape visualization method (ELViM), a multidimensional reduction technique inspired by the energy landscape theory. ELViM transcends one-dimensional representations, offering a comprehensive analysis of the effective conformational phase space without the need for predefined reaction coordinates...
March 20, 2024: Journal of Chemical Information and Modeling
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