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

An open-source family of large encoder-decoder foundation models for chemistry

Soares, E. A., Brazil, E. V., Shirasuna, V. Y., Zubarev, D. Y., Cerqueira, R., & Schmidt, K. (2025) · Communications Chemistry · doi:10.1038/s42004-025-01585-0 · Cited by 36

Soy Extract, Rich in Hydroxylated Isoflavones, Exhibits Antidiabetic Properties In Vitro and in Drosophila melanogaster In Vivo

Lüersen, K., Fischer, A., Bauer, I., Huebbe, P., Uekaji, Y., Chikamoto, K., Nakata, D., Hiramatsu, N., Terao, K., & Rimbach, G. H. (2023) · Nutrients · doi:10.3390/nu15061392 · Cited by 20

Predicting Outcomes of Atezolizumab and Bevacizumab Treatment in Patients with Hepatocellular Carcinoma

Han, J. W., & Jang, J. W. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241411799 · Cited by 22

Discovering the mechanism of action of drugs with a sparse explainable network

Real, K. S. D., & Rubio, Á. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104767 · Cited by 22

Exploration of phytochemical compounds against Marburg virus using QSAR, molecular dynamics, and free energy landscape

Rabaan, A. A., Halwani, M. A., Garout, M. A., Alotaibi, J., Alshehail, B. M., Alotaibi, N. E., Almuthree, S. A., Alshehri, A. A., Alshahrani, M. A., Othman, B. A., et al. (2023) · Molecular Diversity · doi:10.1007/s11030-023-10753-0 · Cited by 20

LibGen: Generating High Quality Spectral Libraries of Natural Products for EAD-, UVPD-, and HCD-High Resolution Mass Spectrometers

Kong, F., Keshet, U., Shen, T., Rodríguez, E. P., & Fiehn, O. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c02263 · Cited by 20

Predicting Molecular Docking of Per- and Polyfluoroalkyl Substances to Blood Protein Using Generative Artificial Intelligence Algorithm Diffdock

Fortela, D. L. B., Mikolajczyk, A. P., Carnes, M. R., Sharp, W. W., Revellame, E. D., Hernandez, R. A., Holmes, W. E., & Zappi, M. E. (2023) · BioTechniques · doi:10.2144/btn-2023-0070 · Cited by 23

Effects of Silybum marianum L. Seed Extracts on Multi Drug Resistant (MDR) Bacteria

El-Sapagh, S., Allam, N. G., El-Sayed, M. N. E.-D., El-Hefnawy, A. A., Korbecka-Glinka, G., & Shala, A. Y. (2023) · Molecules · doi:10.3390/molecules29010064 · Cited by 20

The Origin of Phthalates in Algae: Biosynthesis and Environmental Bioaccumulation

Pace, A., Vaglica, A., Maccotta, A., & Savoca, D. (2024) · Environments · doi:10.3390/environments11040078 · Cited by 20

Unlocking the dehalogenation potential of lead oxide (PbO) via its co-pyrolysis with polyvinyl chloride (PVC) and novel brominated flame retardants (NBFRs)

Kuttiyathil, M. S., Ali, L., Beg, M. B., Teoh, W. Y., & Altarawneh, M. (2024) · Case Studies in Chemical and Environmental Engineering · doi:10.1016/j.cscee.2024.100785 · Cited by 20

DrugMAP 2.0: molecular atlas and pharma-information of all drugs

Li, F., Mou, M., Xiao-yi, L., Xu, W., Yin, J., Zhang, Y., & Zhu, F. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae791 · Cited by 20

NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0

Shahid, S. B., Lacey, F. G., Wiedinmyer, C., Yokelson, R. J., & Barsanti, K. C. (2024) · Geoscientific model development · doi:10.5194/gmd-17-7679-2024 · Cited by 32

A review on machine learning-guided design of energy materials

Kim, S., Xu, J., Shang, W., Xu, Z., Lee, E., & Luo, T. (2024) · Progress in Energy · doi:10.1088/2516-1083/ad7220 · Cited by 19

Pred-O3, a web server to predict molecules, olfactory receptors and odor relationships

Ollitrault, G., Achebouche, R., Dreux, A., Murail, S., Audouze, K., Tromelin, A., & Taboureau, O. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae305 · Cited by 19

Knowledge-based in silico fragmentation and annotation of mass spectra for natural products with MassKG

Zhu, B., Li, Z., Jin, Z., Zhong, Y., Lv, T., Ge, Z., Li, H., Wang, T., Lin, Y., Liu, H., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.09.001 · Cited by 22

Identifying compound-protein interactions with knowledge graph embedding of perturbation transcriptomics

Ni, S., Kong, X., Zhang, Y., Chen, Z., Wang, Z., Fu, Z., Huo, R., Tong, X., Qu, N., Wu, X., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100655 · Cited by 19

Synergy between bacteria and fungi contributes to biodegradation and methane production of lignocellulosic anaerobic co-digestion exposing to surfactants

Tian, Y., Li, Y., Zhang, H., Huang, T., Tian, W., Wang, Z., & Qian, J. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.123579 · Cited by 19

Didemnins as marine-derived anticancer agents: mechanistic insights and clinical potential

Ali, M. A., Khan, A. U., Ali, A., Khaliq, M., Khan, N., Mujahid, S., Călina, D. C., Püsküllüoğlu, M., & Sharifi‐Rad, J. (2025) · Medical Oncology · doi:10.1007/s12032-024-02594-0 · Cited by 19

Normalized Protein–Ligand Distance Likelihood Score for End-to-End Blind Docking and Virtual Screening

Xia, S., Gu, Y., & Zhang, Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01014 · Cited by 22

Transformer graph variational autoencoder for generative molecular design

Nguyen, T., & Karolak, A. M. (2025) · Biophysical Journal · doi:10.1016/j.bpj.2025.01.022 · Cited by 23

Exploring the link between Di-2-ethylhexyl phthalate (DEHP) exposure and muscle mass: A systematic investigation utilizing NHANES data analysis, network toxicology and molecular docking approaches

Hao, J., Ran, B., Hu, S.-Y., Zhuang, Z., Zhang, J.-W., Xiong, M.-R., Wang, R., Zhuang, W., & Wang, M. (2025) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2025.118132 · Cited by 19

A Robust Machine Learning Framework Built Upon Molecular Representations Predicts CYP450 Inhibition: Toward Precision in Drug Repurposing

Ouzounis, S., Panagiotopoulos, V., Bafiti, V., Zoumpoulakis, P. G., Cavouras, D., Kalatzis, I. K., Matsoukas, M., & Κάτσιλα, Θ. (2023) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2023.0075 · Cited by 19

An overview of recent advances and challenges in predicting compound-protein interaction (CPI)

Li, Y., Fan, Z., Rao, J., Chen, Z., Chu, Q., Zheng, M., & Li, X. (2023) · Medical Review · doi:10.1515/mr-2023-0030 · Cited by 19

Antibacterial properties and urease suppression ability of Lactobacillus inhibit the development of infectious urinary stones caused by Proteus mirabilis

Szczerbiec, D., Bednarska, K., & Torzewska, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-51323-0 · Cited by 22

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

Amaya-Rodriguez, C. A., Carvajal-Zamorano, K., Bustos, D., Alegría‐Arcos, M., & Castillo, K. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1251061 · Cited by 21

Physics Constrained Multi-Objective Bayesian Optimization to Accelerate 3d Printing of Thermoplastics

Sattari, K., Wu, Y., Chen, Z., Mahjoubnia, A., Su, C., & Lin, J. (2023) · Additive manufacturing · doi:10.1016/j.addma.2024.104204 · Cited by 21

A refined picture of the native amine dehydrogenase family revealed by extensive biodiversity screening

Elisée, E., Ducrot, L., Méheust, R., Bastard, K., Fossey‐Jouenne, A., Grogan, G., Pelletier, É., Petit, J., Stam, M. R., de Berardinis, V., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49009-2 · Cited by 20

Computational Assessment of the Phytochemicals of Panax ginseng C.A. Meyer Against Dopamine Receptor D1 for Early Huntington’s Disease Prophylactics

Subin, J. A., & Shrestha, R. L. (2024) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-024-01426-2 · Cited by 19

Bu-Shen-Ning-Xin decoction ameliorates premature ovarian insufficiency by suppressing oxidative stress through rno_circRNA_012284/rno_miR-760–3p/HBEGF pathway

Xu, X., Wang, J., Jin, X., Ma, Q., Li, H., Zhou, Q. Y., & Chen, W.-J. (2024) · Phytomedicine · doi:10.1016/j.phymed.2024.155920 · Cited by 19

Identification of programmed cell death-related genes and diagnostic biomarkers in endometriosis using a machine learning and Mendelian randomization approach

Xie, Z.-W., He, Y., Feng, Y., & Wang, X. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1372221 · Cited by 21

Molecular insights into the antioxidative and anti-inflammatory effects of P-coumaric acid against bisphenol A-induced testicular injury: In vivo and in silico studies

Tekin, S., Şengül, E., Yıldırım, S., Aksu, E. H., Bolat, İ., Çınar, B., Shadidizaji, A., Çelebi, F., & Warda, M. (2024) · Reproductive Toxicology · doi:10.1016/j.reprotox.2024.108579 · Cited by 19

The pharmacokinetics and pharmacodynamics of ibogaine in opioid use disorder patients

Knuijver, T., ter Heine, R., Schellekens, A., Heydari, P., Lucas, L., Westra, S. W., Belgers, M., van Oosteren, T., Verkes, R. J., & Kramers, C. (2024) · Journal of Psychopharmacology · doi:10.1177/02698811241237873 · Cited by 22

Soil microbial communities and degradation of pesticides in greenhouse effluent through a woodchip bioreactor

Ewere, E. E., White, S., Mauleon, R., & Benkendorff, K. (2024) · Environmental Pollution · doi:10.1016/j.envpol.2024.124561 · Cited by 18

Can we Predict Drug Excretion into Saliva? A Systematic Review and Analysis of Physicochemical Properties

Nguyen, T. A., Chen, R. H., Hawkins, B. A., Hibbs, D. E., Kim, H. Y., Wheate, N., Groundwater, P. W., STOCKER, S. L., & Alffenaar, J. C. (2024) · Clinical Pharmacokinetics · doi:10.1007/s40262-024-01398-9 · Cited by 18

Overview and limitations of database in global traditional medicines: A narrative review

Li, X., Zhang, J., Shen, X.-J., Zhang, Y., & Guo, D. (2024) · Acta Pharmacologica Sinica · doi:10.1038/s41401-024-01353-1 · Cited by 19

ECBD: European chemical biology database

Škuta, C., Müller, T., Voršilák, M., Popr, M., Epp, T. A., Skopelitou, K., Rossella, F., Herzog, K., Stechmann, B., Gribbon, P. M., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae904 · Cited by 18

Organophosphorus Flame Retardants and Metabolic Disruption: An in Silico , in Vitro , and in Vivo Study Focusing on Adiponectin Receptors

Liu, Y., Ma, X., Le, Y., Feng, J., Xu, M., Wang, W., & Wang, C. (2024) · Environmental Health Perspectives · doi:10.1289/ehp14634 · Cited by 18

Evidence of the impacts of pharmaceuticals on aquatic animal behaviour (EIPAAB): a systematic map and open access database

Martin, J. M., Michelangeli, M., Bertram, M. G., Blanchfield, P. J., Brand, J. A., Brodin, T., Brooks, B. W., Červený, D., Fergusson, K. N., Lagisz, M., et al. (2025) · Environmental Evidence · doi:10.1186/s13750-025-00357-6 · Cited by 26

DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks

Wang, Y., Cheng, L., Zhang, Y., Cao, Y., & Alghazzawi, D. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf187 · Cited by 22

The Next Frontier: Unveiling Novel Approaches for Combating Multidrug-Resistant Bacteria

Mallari, P., Rostami, L. D., Alanko, I., Howaili, F., Ran, M., Bansal, K. K., Rosenholm, J. M., & Salo‐Ahen, O. M. H. (2025) · Pharmaceutical Research · doi:10.1007/s11095-025-03871-x · Cited by 19

Integrating AI into OLED material design: a comprehensive review of computational frameworks, challenges, and opportunities

Shi, Y., Sun, M., Shi, H., Liang, Z., Qiao, B., Zhao, S., Pu, X., & Song, D. (2025) · 中国科学通报：英文版 · doi:10.1016/j.scib.2025.07.007 · Cited by 20

Functional Additives in Automotive Polymer Matrices: Compatibility, Mechanisms, and Industry Challenges

Dobrotă, D., Bărbușiu, A.-M., Sava, G.-A., & Oleksik, V. S. (2025) · Polymers · doi:10.3390/polym17172328 · Cited by 21

ChemGraph as an agentic framework for computational chemistry workflows

Pham, T. D., Tanikanti, A., & Keçeli, M. (2026) · Communications Chemistry · doi:10.1038/s42004-025-01776-9 · Cited by 48

Synergistic Combination of Citrus Flavanones as Strong Antioxidant and COX-Inhibitor Agent

Smeriglio, A., Iraci, N., Denaro, M., Mandalari, G., Giofrè, S. V., & Trombetta, D. (2023) · Antioxidants · doi:10.3390/antiox12040972 · Cited by 18

Molecular Docking Integrated with Network Pharmacology Explores the Therapeutic Mechanism of Cannabis sativa against Type 2 Diabetes

Guzmán-Flores, J. M., Pérez-Vázquez, V., Martínez-Esquivias, F., Isiordia-Espinoza, M., & Víveros-Paredes, J. (2023) · Current Issues in Molecular Biology · doi:10.3390/cimb45090457 · Cited by 17

3D-printed prednisolone phosphate suppositories with tunable dose and rapid release for the treatment of inflammatory bowel disease

Kocabas, L. I., Ayyoubi, S., Tajqurishi, M., Quodbach, J., Vermonden, T., & Kok, R. J. (2023) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2023.123639 · Cited by 18

Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome

Malwe, A. S., & Sharma, V. K. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1254073 · Cited by 22

Exploring the combinatorial explosion of amine–acid reaction space via graph editing

Zhang, R., Mahjour, B. A., Outlaw, A., McGrath, A., Hopper, T., Kelley, B. P., Walters, W. P., & Cernak, T. (2024) · Communications Chemistry · doi:10.1038/s42004-024-01101-w · Cited by 20

Harnessing graph neural networks to craft fragrances based on consumer feedback

Rodrigues, B. C. L., Santana, V. V., Queiroz, L. P., Rebello, C. M., & Nogueira, I. B. R. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108674 · Cited by 20

Synergizing Chemical Structures and Bioassay Descriptions for Enhanced Molecular Property Prediction in Drug Discovery

Schuh, M. G., Boldini, D., & Sieber, S. A. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00765 · Cited by 26

Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies

Nash, W. J., Ngere, J. B., Najdekr, L., & Dunn, W. B. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.4c00966 · Cited by 18

Mining drug–target interactions from biomedical literature using chemical and gene descriptions-based ensemble transformer model

Aldahdooh, J., Tanoli, Z., & Tang, J. P. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae106 · Cited by 18

Deep learning in template-free de novo biosynthetic pathway design of natural products

Xie, X., Gui, L., Qiao, B., Wang, G., Huang, S., Zhao, Y., & Sun, S. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae495 · Cited by 21

RNA-seq analysis and in vivo experiments identified the protective effect of kaempferol on idiopathic pulmonary fibrosis by regulating the PPARG/TNC signaling pathway to reduce ECM deposition

Zhang, X., Xie, Y., Cai, Y., Huang, H.-T., Liang, H., Liao, G., Jiang, Y., Peng, X., Zhan, S., & Huang, X. (2024) · Food & Function · doi:10.1039/d4fo01474j · Cited by 18

Enhancing Industrial Hemp (Cannabis sativa) Leaf By-Products: Bioactive Compounds, Anti-Inflammatory Properties, and Potential Health Applications

Frusciante, L., Geminiani, M., Shabab, B., Olmastroni, T., Roncucci, N., Mastroeni, P., Salvini, L., Lamponi, S., Trezza, A., & Santucci, A. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26020548 · Cited by 20

FeatureDock for protein-ligand docking guided by physicochemical feature-based local environment learning using transformer

Xue, M., Liu, B., Cao, S., & Huang, X. (2025) · npj Drug Discovery. · doi:10.1038/s44386-025-00005-6 · Cited by 24

Occurrence and removal of 42 legacy and emerging per- and polyfluoroalkyl substances (PFAS) in drinking water treatment plants in South Korea

Kang, J.-K., Kim, M., & Oh, J. (2025) · Water Research X · doi:10.1016/j.wroa.2025.100329 · Cited by 19

Quantum Chemistry Dataset with Ground- and Excited-state Properties of 450 Kilo Molecules

Zhu, Y., Li, M., Xu, C., & Lan, Z. (2024) · Scientific Data · doi:10.1038/s41597-024-03788-x · Cited by 20

Identification of novel small-molecule inhibitors of SARS-CoV-2 by chemical genetics

Chan, C. C.-Y., Guo, Q., Chan, J. F., Tang, K., Cai, J., Chik, K. K., Huang, Y., Dai, M., Qin, B., Ong, C. P., et al. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.05.026 · Cited by 17

Enzymatic oxidation of polyethylene by Galleria mellonella intestinal cytochrome P450s

Son, J., Lee, S., Hwang, S., Jeong, J., Jang, S., Gong, J., Choi, J. Y., Je, Y. H., & Ryu, C. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.136264 · Cited by 17

Implications of trinodal inhibitions and drug repurposing in MAPK pathway: A putative remedy for breast cancer

Majumder, S., Lodh, E., & Chowdhury, T. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108255 · Cited by 17

UHPLC-MS/MS based comprehensive phenolic profiling, antimicrobial and antioxidant activities of Indian Rhodomyrtus tomentosa fruits

Priya, A. D., & Martin, A. (2025) · Scientific Reports · doi:10.1038/s41598-024-84800-7 · Cited by 17

A genome-scale metabolic model for the denitrifying bacterium Thauera sp. MZ1T accurately predicts degradation of pollutants and production of polymers

Tec-Campos, D., Tibocha‐Bonilla, J. D., Jiang, C., Passi, A., Thiruppathy, D., Zúñiga, C., Posadas, C., Zepeda, A., & Zengler, K. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012736 · Cited by 17

Positional embeddings and zero-shot learning using BERT for molecular-property prediction

Mswahili, M. E., Hwang, J., Rajapakse, J. C., Jo, K., & Jeong, Y.-S. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00959-9 · Cited by 21

Combined In Vitro and In Silico analysis of ferrocenylmethylaniline derivatives: Antibacterial potential, DFT calculations, and molecular dynamics insights

Bekkar, Y., Lanez, E., Lanez, T., Bourougaa, L., Adaika, A., Benine, A., & Saada, Z. (2025) · Journal of Organometallic Chemistry · doi:10.1016/j.jorganchem.2025.123618 · Cited by 17

CytoToxLCM: A Software to Predict Cytotoxicity of Emerging Contaminant Liquid Crystal Monomers

Wu, J., Bian, C., Yang, X., & Su, G. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c00027 · Cited by 17

Unlocking the application potential of AlphaFold3-like approaches in virtual screening

Shen, C., Zhang, X., Gu, S., Zhang, O., Wang, Q., Du, G., Zhao, Y., Jiang, L., Pan, P., Kang, Y., et al. (2025) · Chemical Science · doi:10.1039/d5sc06481c · Cited by 17

Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters

Malli, A., Vasyutyn, D., & Kim, J. R. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02428 · Cited by 19

Enantioselectivity effects of energy metabolism in honeybees (Apis mellifera) by triticonazole

Kong, Y., Si, M., Wang, P., Guo, H., Liu, X., & Zhao, M. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.162884 · Cited by 17

A novel perspective on the preventive treatment of hydrazine compound-induced liver injury: Isoniazid liver injury as an example

Li, X., Li, C., Li, C., Wu, C., Bai, Y., Zhao, X., Bai, Z., Zhang, X., Xiao, X., & Niu, M. (2023) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2023.116616 · Cited by 17

Contemporary bioinformatics and cheminformatics support for food peptidomics

Mińkiewicz, P., Iwaniak, A., & Darewicz, M. (2024) · Current Opinion in Food Science · doi:10.1016/j.cofs.2024.101125 · Cited by 19

EKGDR: An End-to-End Knowledge Graph-Based Method for Computational Drug Repurposing

Tayebi, J., & BabaAli, B. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01925 · Cited by 17

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition

Fluetsch, A., Trunzer, M., Gerebtzoff, G., & Rodríguez-Pérez, R. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00305 · Cited by 17

Unveiling the role of tRNA-derived small RNAs in MAPK signaling pathway: implications for cancer and beyond

Wang, Q., Huang, Q., Ying, X., Shen, J., & Duan, S. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1346852 · Cited by 17

Research on CO2 capture performance of DMEDA water-lean absorbents based on molecular dynamics

Chen, Z., Wang, T., Li, C., Fang, M., Chen, W., Hu, X., Zhang, W., Zhang, L., Fan, W., & Zeng, S. (2024) · Separation and Purification Technology · doi:10.1016/j.seppur.2024.128924 · Cited by 17

Study of photosensitizer dyes for high-performance dye-sensitized solar cells application: A computational investigation

Hasan, M. A., Rahman, I. M. M., Uddin, J., & Chowdhury, F. I. (2024) · Chemical Physics Impact · doi:10.1016/j.chphi.2024.100719 · Cited by 17

MPCD: A Multitask Graph Transformer for Molecular Property Prediction by Integrating Common and Domain Knowledge

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