PubChem 2023 update

Gene Ontology: tool for the unification of biology

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206

Database resources of the National Center for Biotechnology Information: update

Wheeler, D. L. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh073 · Cited by 10,695

SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules

Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436

UniProt: the universal protein knowledgebase in 2021

The UniProt Consortium, Bateman, A., Martin, M.-J., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., Alpi, E., Bowler-Barnett, E. H., Britto, R., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1100 · Cited by 6,372

Database resources of the National Center for Biotechnology Information

Agarwala, R., Barrett, T., Beck, J., Benson, D., Bollin, C., Bolton, E. E., Bourexis, D., Brister, J., Bryant, S. H., Canese, K., et al. (2017) · Nucleic Acids Res. · doi:10.1093/nar/gkx1095 · Cited by 6,277

PubChem Substance and Compound databases

Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436

PubChem in 2021: new data content and improved web interfaces

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa971 · Cited by 3,953

The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium, Carbon, S., Douglass, E., Good, B. M., Unni, D. R., Harris, N. L., Mungall, C. J., Basu, S., Chisholm, R. L., Dodson, R. J., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1113 · Cited by 3,555

PubChem 2019 update: improved access to chemical data

Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B. A., Thiessen, P. A., Yu, B., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1033 · Cited by 3,249

NCBI Taxonomy: a comprehensive update on curation, resources and tools

Schoch, C. L., Ciufo, S., Domrachev, M., Hotton, C. L., Kannan, S., Khovanskaya, R., Leipe, D., Mcveigh, R., O’Neill, K., Robbertse, B., et al. (2020) · Database · doi:10.1093/database/baaa062 · Cited by 2,635

Choice of the Right Supporting Electrolyte in Electrochemical Reductions: A Principal Component Analysis

Mast, F., Hielscher, M. M., Wirtanen, T., Erichsen, M., Gauß, J., Diezemann, G., & Waldvogel, S. R. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c00910 · Cited by 16

Prediction of retention data of phenolic compounds by quantitative structure retention relationship models under reverse-phase liquid chromatography

Vinci, R. L., Arena, K., Rigano, F., Cacciola, F., Dugo, P., & Mondello, L. (2024) · Journal of Chromatography A · doi:10.1016/j.chroma.2024.465146 · Cited by 17

Unveiling promising phytocompounds from Moringa oleifera as dual inhibitors of EGFR(T790M/C797S) and VEGFR-2 in non-small cell lung cancer through in silico screening, ADMET, dynamics simulation, and DFT analysis

Munna, M. M. R., Tusar, M. T. T., Shanta, S. S., Ahmed, M. H., & Ali, M. S. (2024) · Journal of Genetic Engineering and Biotechnology · doi:10.1016/j.jgeb.2024.100406 · Cited by 16

Interactions between volatile air pollutants and atmospheric water production – Effects of chemical properties, mechanisms, and transfer processes

Kaplan, A., Gozlan, I., Kira, O., & Avisar, D. (2024) · Chemosphere · doi:10.1016/j.chemosphere.2024.143399 · Cited by 16

NPGPT: natural product-like compound generation with GPT-based chemical language models

Sakano, K., Furui, K., & Ohue, M. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06860-w · Cited by 16

The efficient prediction of inflammatory osteolysis caused by polylactic acid through network toxicology and molecular docking strategy

Cui, Z., Tian, H., & Zhang, S. (2025) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2025.117876 · Cited by 16

Automatic extraction of FAIR data from publications using LLMPreprint

Patiny, L., & Godin, G. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-05v1b-v2 · Cited by 16

Comprehensive lipidomics and flavoromics analysis reveals the formation mechanism of the unique flavor in quail egg yolks during thermal treatment

Zhou, Z., Jiang, H., Sun, H., Huang, X.-H., Cui, S., Zhou, D., & Qin, L. (2024) · Food Production Processing and Nutrition · doi:10.1186/s43014-024-00234-8 · Cited by 15

Genetic mapping of serum metabolome to chronic diseases among Han Chinese

Cheng, C., Xu, F., Pan, X., Wang, C., Fan, J., Yang, Y., Liu, Y., Sun, L., Liu, X., Xu, Y., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2024.100743 · Cited by 17

Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence

Wu, X., Guo, B., Chang, X., Yang, Y., Liu, Q., Liu, J., Yang, Y., Zhang, K., Ma, Y., & Fu, S. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1427626 · Cited by 15

MicrobeRX: a tool for enzymatic-reaction-based metabolite prediction in the gut microbiome

Ruiz-Moreno, A. J., del Castillo-Izquierdo, Á., Tamargo-Rubio, I., & Fu, J. (2025) · Microbiome · doi:10.1186/s40168-025-02070-5 · Cited by 17

Deep Learning for Bidirectional Translation between Molecular Structures and Vibrational Spectra

Hu, T., Zou, Z., Li, B., Zhu, T., Gu, S., Jiang, J., Luo, Y., & Hu, W. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c05010 · Cited by 15

Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions

Abdelwahab, A. A., Elattar, M., & Fawzi, S. A. (2025) · BioMedical Engineering OnLine · doi:10.1186/s12938-025-01412-6 · Cited by 20

Predictions of the Biological Effects of Several Acyclic Monoterpenes as Chemical Constituents of Essential Oils Extracted from Plants

Dascălu, D., Isvoran, A., & Ianovici, N. (2023) · Molecules · doi:10.3390/molecules28124640 · Cited by 15

Phytochemical analysis for ten Peruvian Mentheae (Lamiaceae) by liquid chromatography associated with high resolution mass spectrometry

Serrano, C. A., Villena, G. K., Rodríguez, E. F. R., Calsino, B., Huaman, M. A. L., & Ccana-Ccapatinta, G. V. (2023) · Scientific Reports · doi:10.1038/s41598-023-37830-6 · Cited by 15

System biology analysis to develop diagnostic biomarkers, monitoring pathological indexes, and novel therapeutic approaches for immune targeting based on maggot bioactive compounds and polyphenolic cocktails in mice with gastric cancer

Kaviani, E., Hajibabaie, F., Abedpoor, N., Safavi, K., Ahmadi, Z., & Karimy, A. (2023) · Environmental Research · doi:10.1016/j.envres.2023.117168 · Cited by 15

Discovery of a novel cardiac-specific myosin modulator using artificial intelligence-based virtual screening

Parijat, P., Attili, S., Hoare, Z., Shattock, M. J., Kenyon, V. A., & Kampourakis, T. (2023) · Nature Communications · doi:10.1038/s41467-023-43538-y · Cited by 15

A Solitary Stalled 80S Ribosome Prevents mRNA Recruitment to Stress Granules

Fedorovskiy, A. G., Burakov, A. V., Terenin, I. M., Bykov, D. A., Lashkevich, K. A., Попенко, В. И., Makarova, N. E., Sorokin, I. I., Sukhinina, A. P., Prassolov, V., et al. (2023) · Biochemistry (Moscow) · doi:10.1134/s000629792311010x · Cited by 13

Predicting the solubility of gases in imidazolium-based ionic liquids with SAFT-VR Mie EoS by a novel approach based on COSMO

Beraldo, C. S., Liang, X., & Follegatti–Romero, L. A. (2023) · Chemical Engineering Science · doi:10.1016/j.ces.2023.119610 · Cited by 15

Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis

Hecker, M., Frahm, N., & Zettl, U. K. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics16010003 · Cited by 15

Improved QSAR models for PARP-1 inhibition using data balancing, interpretable machine learning, and matched molecular pair analysis

Gomatam, A., Hirlekar, B. U., Singh, K. D., Murty, U. S., & Dixit, V. A. (2024) · Molecular Diversity · doi:10.1007/s11030-024-10809-9 · Cited by 15

Co-Assembly of Cancer Drugs with Cyclo-HH Peptides: Insights from Simulations and Experiments

Vlachou, A., Kumar, V. B., Tiwari, O. S., Rencus‐Lazar, S., Chen, Y., Ozguney, B., Gazit, E., & Tamamis, P. (2024) · ACS Applied Bio Materials · doi:10.1021/acsabm.3c01304 · Cited by 15

Estimation of the mass density of biological matter from refractive index measurements

Möckel, C., Beck, T., Kaliman, S., Abuhattum, S., Kim, K., Kolb, J., Wehner, D., Zaburdaev, V., & Guck, J. R. (2024) · Biophysical Reports · doi:10.1016/j.bpr.2024.100156 · Cited by 15

Chemical Characterization of the Essential Oil Compositions of Mentha spicata and M. longifolia ssp. cyprica from the Mediterranean Basin and Multivariate Statistical Analyses

İsfendiyaroğlu, H., Hanoğlu, A., Hanoğlu, D. Y., Alkaş, F. B., Başer, K. H. C., & Yavuz, D. Ö. (2024) · Molecules · doi:10.3390/molecules29091970 · Cited by 15

Microbiome-based precision nutrition: Prebiotics, probiotics and postbiotics

Odriozola, A., González, A., Odriozola, I., Álvarez‐Herms, J., & Corbi, F. (2024) · Advances in genetics · doi:10.1016/bs.adgen.2024.04.001 · Cited by 18

In silico and in vitro studies for the identification of small molecular inhibitors from Euphorbia hirta Linn for rheumatoid arthritis: targeting TNF-α-mediated inflammation

Velmurugan, Y., Natarajan, S. R., Chakkarapani, N., Jayaraman, S., Hemamalini, M., & Venkatachalam Rajakannan, B. (2024) · Molecular Diversity · doi:10.1007/s11030-024-10900-1 · Cited by 15

Assisted Active Learning for Model-Based Method Development in Liquid Chromatography

Bosten, E., Pardon, M., Chen, K., Koppen, V., Herck, G. V., Hellings, M., & Cabooter, D. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c02700 · Cited by 15

Revisiting the Application of Machine Learning Approaches in Predicting Aqueous Solubility

Zheng, T., Mitchell, J. B. O., & Dobson, S. (2024) · ACS Omega · doi:10.1021/acsomega.4c06163 · Cited by 15

Synergistic enhancement: Exploring the potential of piperine in cancer therapeutics through chemosensitization and combination therapies

Manickasamy, M. K., Kumar, A., BharathwajChetty, B., Alqahtani, M. S., Abbas, M., Alqahtani, A., Unnikrishnan, J., Bishayee, A., Sethi, G., & Kunnumakkara, A. B. (2024) · Life Sciences · doi:10.1016/j.lfs.2024.122943 · Cited by 15

PungentDB: Bridging traditional Chinese medicine of medicine food homology and modern food flavor chemistry

Zhao, C. X., Wang, Z., Cao, Y., Shi, X., Xu, B., Li, X., Li, J., Zhang, Y., & Qiao, Y. (2024) · Food Chemistry X · doi:10.1016/j.fochx.2024.101742 · Cited by 15

Research Progresses and Applications of Knowledge Graph Embedding Technique in Chemistry

Wang, C., Yang, Y., Song, J., & Nan, X. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00791 · Cited by 17

Predicting Collision-Induced-Dissociation Tandem Mass Spectra (CID-MS/MS) Using Ab Initio Molecular Dynamics

Lee, J., Tantillo, D. J., Wang, L., & Fiehn, O. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00760 · Cited by 15

PreTKcat: A pre-trained representation learning and machine learning framework for predicting enzyme turnover number

Cai, Y., Zhang, W., Dou, Z., Wang, C., Yu, W., & Wang, L. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108327 · Cited by 15

MLC-DTA: Drug-target affinity prediction based on multi-level contrastive learning and equivariant graph neural networks

Zheng, M., Sun, G., & Fan, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130052 · Cited by 15

Growth vs Diversity: A Time-Evolution Analysis of the Chemical Space

Pérez, K. L., López‐López, E., Soulage, F., Félix, E., Medina‐Franco, J. L., & Miranda‐Quintana, R. A. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00347 · Cited by 16

Deep Learning for Green Chemistry: An AI-Enabled Pathway for Biodegradability Prediction and Organic Material Discovery

Gbadago, D. Q., Hwang, G., Lee, K., & Hwang, S. (2024) · Korean Journal of Chemical Engineering · doi:10.1007/s11814-024-00202-5 · Cited by 18

Extracellular domain of TREM2 possess two distinct ligand recognition sites: Insights from machine-learning guided docking and all-atoms molecular dynamics simulations

Mishra, S., Swain, P. S., Pati, S., & Dehury, B. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e41414 · Cited by 14

TopMT-GAN: a 3D topology-driven generative model for efficient and diverse structure-based ligand design

Wang, S., Lin, T., Peng, T., Xing, E., Chen, S., Kara, L. B., & Cheng, X. (2025) · Chemical Science · doi:10.1039/d4sc05211k · Cited by 14

Automated Retrosynthesis Planning of Macromolecules Using Large Language Models and Knowledge Graphs

Ma, Q., Zhou, Y., & Li, J. (2025) · Macromolecular Rapid Communications · doi:10.1002/marc.202500065 · Cited by 17

Integrating network pharmacology, molecular docking, and molecular dynamics to explore the antidiabetic mechanism of Physalis angulata L.

Fitrianingsih, S. P., Kurniati, N. F., Fakih, T. M., & Adnyana, I. K. (2025) · Pharmacia · doi:10.3897/pharmacia.72.e149156 · Cited by 14

Safety evaluation of irinotecan: a real-world disproportionality analysis using FAERS and JADER databases during the time period 2004-2024

Lou, S., Chen, H., Cui, Z., Zhang, X., Zhu, C., Zhou, L., Ou, Y., & Zou, F. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1516449 · Cited by 14

Network pharmacology and experimental validation to reveal the target of matrine against PRRSV

Zhao, Y., Ling, X., Zhang, H., Sun, P., Sun, Y., Yin, W. H., Fan, K., Yang, H., Zhong, J., Zhang, Z., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106371 · Cited by 15

In-silico approaches for identification of compounds inhibiting SARS-CoV-2 3CL protease

Zeyaullah, M., Khan, N., Muzammil, K., AlShahrani, A. M., Khan, M. S. A. A., Alam, M. S., Ahmad, R., & Khan, W. H. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284301 · Cited by 14

Functioning of a Fluorescein pH-Probe in Aqueous Media: Impact of Temperature and Viscosity

Surzhikova, D. P., Sukovatyi, L. A., Немцева, Е. В., Esimbekova, E. N., & Slyusareva, E. A. (2023) · Micromachines · doi:10.3390/mi14071442 · Cited by 14

Exploring the inhibition mechanism of interleukin-1-beta in gouty arthritis by polygonum cuspidatum using network pharmacology and molecular docking: A review

Ge, X., Zhang, Y., Fang, R.-L., Zhao, J., & Huang, J. (2023) · Medicine · doi:10.1097/md.0000000000034396 · Cited by 14

Screening eco-friendliness tire antioxidants alternatives: functional 2,2,4-trimethyl-1,2-dihydroquinoline derivatives design and toxicity evaluation

Wang, Y., Fu, R., Sun, P., Li, X., & Zhao, W. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-28836-8 · Cited by 14

Guiding maps of solvents for lithium-sulfur batteries via a computational data-driven approach

Atwi, R., & Rajput, N. N. (2023) · Patterns · doi:10.1016/j.patter.2023.100799 · Cited by 14

Kalium 3.0 is a comprehensive depository of natural, artificial, and labeled polypeptides acting on potassium channels

Krylov, N. A., Tabakmakher, V. М., Yureva, D. A., Vassilevski, A. A., & Kuzmenkov, A. I. (2023) · Protein Science · doi:10.1002/pro.4776 · Cited by 14

A Murder Mystery Gamification Session to Consolidate Analytical Biochemical Techniques Learning

Hill, A. M., & Harmer, N. J. (2023) · Journal of Chemical Education · doi:10.1021/acs.jchemed.3c00279 · Cited by 14

Quantitative imaging of doxorubicin diffusion and cellular uptake in biomimetic gels with human liver tumor cells

Degerstedt, O., O’Callaghan, P., Clavero, A. L., Gråsjö, J., Eriksson, O., Sjögren, E., Hansson, P., Heindryckx, F., Kreuger, J., & Lennernäs, H. (2023) · Drug Delivery and Translational Research · doi:10.1007/s13346-023-01445-1 · Cited by 14

Identification of an antivirulence agent targeting the master regulator of virulence genes in Staphylococcus aureus

Arya, R., Kim, T., Youn, J. W., Bae, T., & Kim, K. K. (2023) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2023.1268044 · Cited by 13

Exploring the potential mechanisms of Tongmai Jiangtang capsules in treating diabetic nephropathy through multi-dimensional data

Liu, Y., Cui, X., Zhang, X., Xie, Z., Wang, W., Xi, J., & Xie, Y. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1172226 · Cited by 16

Non-target screening of surface water samples to identify exposome-related pollutants: a case study from Luxembourg

Aurich, D., Diderich, P., Helmus, R., & Schymanski, E. (2023) · Environmental Sciences Europe · doi:10.1186/s12302-023-00805-5 · Cited by 16

Extensive preclinical evaluation of combined mangiferin and glycyrrhizic acid for restricting synovial neovascularization in rheumatoid arthritis

Mao, X., Yan, X., Li, C., Liu, Y., Zhang, Y., & Lin, N. (2023) · Chinese Medicine · doi:10.1186/s13020-023-00863-0 · Cited by 14

RediscMol: Benchmarking Molecular Generation Models in Biological Properties

Weng, G., Zhao, H., Nie, D., Zhang, H., Liu, L., Hou, T., & Kang, Y. (2024) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c02051 · Cited by 13

CrossFuse-XGBoost: accurate prediction of the maximum recommended daily dose through multi-feature fusion, cross-validation screening and extreme gradient boosting

Li, Q., He, Y., & Pan, J. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad511 · Cited by 14

Unlocking the anti-aging ingredients of Sacha inchi husk through ultrasound-assisted extraction: Response surface methodology and comprehensive analytical approach

Poomanee, W., Thavanapong, T., Yaowiwat, N., Chaichit, S., Sainakham, M., Kiattisin, K., & Chaiyana, W. (2024) · Journal of Agriculture and Food Research · doi:10.1016/j.jafr.2024.101016 · Cited by 14

Database of 4 Million Medicinal Chemistry-Relevant Ring Systems

Ertl, P. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01812 · Cited by 13

Can Small Molecules Provide Clues on Disease Progression in Cerebrospinal Fluid from Mild Cognitive Impairment and Alzheimer’s Disease Patients?

Andújar, B. T., Mary, A., Venegas, C., Cheng, T., Zaslavsky, L. Y., Bolton, E., Heneka, M. T., & Schymanski, E. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.3c10490 · Cited by 13

Enhancing Multi-species Liver Microsomal Stability Prediction through Artificial Intelligence

Long, T.-Z., Jiang, D., Shi, S., Deng, Y., Wang, W., & Cao, D. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00159 · Cited by 14

Artificial intelligence-open science symbiosis in chemoinformatics

Miljković, F. N., & Medina‐Franco, J. L. (2024) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2024.100096 · Cited by 18

Computational exploration of SLC14A1 genetic variants through structure modeling, protein-ligand docking, and molecular dynamics simulation

Sultana, T., Mou, S. I., Chatterjee, D., Faruk, M. O., & Hosen, M. I. (2024) · Biochemistry and Biophysics Reports · doi:10.1016/j.bbrep.2024.101703 · Cited by 16

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

Abedpoor, N., Taghian, F., Dehkordi, K. J., & Safavi, K. (2024) · Cancer Cell International · doi:10.1186/s12935-024-03328-y · Cited by 14

Effect of continuous microwave processing on enzymes and quality attributes of bael beverage

Dhar, R., & Chakraborty, S. (2024) · Food Chemistry · doi:10.1016/j.foodchem.2024.139621 · Cited by 14

Safer and greener chemicals for the aquatic ecosystem: Chemometric modeling of the prolonged and chronic aquatic toxicity of chemicals on Oryzias latipes

Kumar, A., Ojha, P. K., & Roy, K. (2024) · Aquatic Toxicology · doi:10.1016/j.aquatox.2024.106985 · Cited by 14

In-depth analysis of γ-CuI as an HTM for perovskite solar cells: A comprehensive DFT study of structural, elastic, mechanical, charge density, and optoelectronic properties

Naimi, S., Laalioui, S., Salmani, E. M., Alaoui, K. B., & Hamid Ez-zahraouy, A. (2024) · Solar Energy · doi:10.1016/j.solener.2024.112680 · Cited by 14

Chemogenomics for NR1 nuclear hormone receptors

Isigkeit, L., Schallmayer, E., Busch, R., Brunello, L., Menge, A., Elson, L., Müller, S., Knapp, S., Stolz, A., Marschner, J. A., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49493-6 · Cited by 14

Bioinformatics tools for the study of bioactive peptides from vegetal sources: evolution and future perspectives

Longarela, N. R., Paredes-Ramos, M., & Vilariño, J. M. L. (2024) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2024.2367571 · Cited by 14

Quantum-Informed Molecular Representation Learning Enhancing ADMET Property Prediction

Kim, J., Chang, W., Ji, H., & Joung, I. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00772 · Cited by 17

Exploring Plant-Based Compounds as Alternatives for Targeting Enterococcus faecalis in Endodontic Therapy: A Molecular Docking Approach

Boreak, N. M., Mahde, R. Z. A., Otayn, W. A. K., Alamer, A. Y., Alrajhi, T., Jafri, S., Sharwani, A., Swaidi, E., Abozoah, S., & Mowkly, A. A. M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25147727 · Cited by 14

Enhanced catalytic performance and pH stability of Streptomyces Laccase Y230R and its degradation of malachite green

Bian, L., Zhang, S., Chang, T., Zhang, J., Zhu, X., & Zhang, C. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.134108 · Cited by 14

Natural Products Dereplication: Databases and Analytical Methods

Pérez‐Victoria, I. (2024) · Fortschritte der Chemie Organischer Naturstoffe/Fortschritte der Chemie organischer Naturstoffe/Progress in the chemistry of organic natural products · doi:10.1007/978-3-031-59567-7_1 · Cited by 14

Chemical Exposomics in Human Plasma by Lipid Removal and Large-Volume Injection Gas Chromatography–High-Resolution Mass Spectrometry

Xie, H., Sdougkou, K., Bonnefille, B., Papazian, S., Bergdahl, I. A., Rantakokko, P., & Martin, J. W. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.4c05942 · Cited by 16

Smartphone based TLC approach versus conventional densitometric measurement for the simultaneous determination of donepezil and memantine, content uniformity testing along with greenness and whiteness assessment

Moaaz, E. M., Abdel‐Moety, E. M., Rezk, M. R., & Fayed, A. S. (2024) · Sustainable Chemistry and Pharmacy · doi:10.1016/j.scp.2024.101789 · Cited by 14

Navigating a 1E+60 Chemical Space of Peptide/Peptoid Oligomers

Orsi, M., & Reymond, J. (2024) · Molecular Informatics · doi:10.1002/minf.202400186 · Cited by 18

Latin American Natural Product Database (LANaPDB): An Update

Gómez‐García, A., Jiménez, D. A. A., Zamora, W. J., Barazorda-Ccahuana, H. L., Chávez‐Fumagalli, M. Á., Valli, M., Andricopulo, A. D., Bolzani, V. D. S., Olmedo, D. A., Solís, P. N., et al. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01560 · Cited by 20

Biological activities of astaxanthin in the treatment of neurodegenerative diseases

Lotfi, A., Abroodi, Z., & Khazaei, M. (2024) · Neurodegenerative Disease Management · doi:10.1080/17582024.2024.2433932 · Cited by 14

A quantitative analysis of knowledge-learning preferences in large language models in molecular science

Liu, P., Tao, J., & Ren, Z. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-024-00977-6 · Cited by 17

Prediction of ADMET profile and anti-inflammatory potential of chamuangone

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