QSAR Modeling: Where have you been? Where are you going to?

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Items 501–600 of 1,001

1. Global Analysis of Models for Predicting Human Absorption: QSAR, In Vitro , and Preclinical Models
   Edward Price, J. Cory Kalvass, David A. DeGoey, Balakrishna S. Hosmane, Stella Z. Doktor, Kelly E. Desino · 2021 · Journal of Medicinal Chemistry
2. Toward Application and Implementation of in Silico Tools and Workflows within Benign by Design Approaches
   Stefanie Lorenz, Ann-Kathrin Amsel, Neele Puhlmann, Marco Reich, Oliver Olsson, Klaus Kümmerer · 2021 · ACS Sustainable Chemistry & Engineering
3. Patent Toxicity
   Gianluca Biggi, Elisa Giuliani, Arianna Martinelli, Emilio Benfenati · 2021 · Research Policy
4. The OECD Principles for (Q)SAR Models in the Context of Knowledge Discovery in Databases (KDD)
   Gabriela Gómez-Jiménez, Karla Gonzalez-Ponce, Durbis J. Castillo‐Pazos, Abraham Madariaga‐Mazón, Joaquín Barroso‐Flores, Fernando Cortés‐Guzmán, Karina Martínez‐Mayorga · 2018 · Advances in protein chemistry and structural biology
5. Machine learning and molecular design of self-assembling -conjugated oligopeptides
   Bryce Thurston, Andrew L. Ferguson · 2018 · Molecular Simulation
6. Machine learning prediction of materials properties from chemical composition: Status and prospects
   Mohammed Alghadeer, Nufida D. Aisyah, Mahmoud Hezam, Saad M. Alqahtani, Ahmer A.B. Baloch, Fahhad H. Alharbi · 2024 · Chemical Physics Reviews
7. A comprehensive company database analysis of biological assay variability
   Christian Krämer, Göran Dahl, Christian Tyrchan, Johan Ulander · 2016 · Drug Discovery Today
8. Current State and Future Perspectives in QSAR Models to Predict Blood- Brain Barrier Penetration in Central Nervous System Drug R&D
   Juan Francisco Morales, Sebastián Scioli Montoto, Pietro L. Fagiolino, María Esperanza Ruiz · 2016 · Mini-Reviews in Medicinal Chemistry
9. QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities
   Marília N. N. Lima, Cleber Camilo Melo-Filho, Gustavo Capatti Cassiano, Bruno J. Neves, Vinícius M. Alves, Rodolpho C. Braga, Pedro Cravo, Eugene N. Muratov, Juliana Calit, Daniel Y. Bargieri et al. · 2018 · Frontiers in Pharmacology
10. Natural products as inhibitors of Leishmania major dihydroorotate dehydrogenase
    Lucas Apolinário Chibli, Thomas Jürgen Schmidt, Maria Cristina Nonato, Felipe A. Calil, Fernando B. Da Costa · 2018 · European Journal of Medicinal Chemistry
11. Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification
    Amit Kumar Halder · 2018 · SAR and QSAR in environmental research
12. Discovery of new potent hits against intracellular Trypanosoma cruzi by QSAR-based virtual screening
    Cleber Camilo Melo-Filho, Rodolpho C. Braga, Eugene N. Muratov, Caio Haddad Franco, Carolina Borsoi Moraes, Lucio Holanda Freitas-Junior, Carolina Horta Andrade · 2018 · European Journal of Medicinal Chemistry
13. In Silico Drug Design Methods for Drug Repurposing
    Bashir Akhlaq Akhoon, Harshita Tiwari, Amit Nargotra · 2019 · Elsevier eBooks
14. Methodology of aiQSAR: a group-specific approach to QSAR modelling
    Kristijan Vuković, Domenico Gadaleta, Emilio Benfenati · 2019 · Journal of Cheminformatics
15. Alignment-Free Method to Predict Enzyme Classes and Subclasses
    Riccardo Concu, M. Natália D. S. Cordeiro · 2019 · International Journal of Molecular Sciences
16. Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors
    Alexander M. Andrianov, Grigory I. Nikolaev, Nikita A. Shuldov, Ivan P. Bosko, Arseny I. Anischenko, Alexander V. Tuzikov · 2021 · Journal of Biomolecular Structure and Dynamics
17. Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals
    Arthur C. Silva, Joyce Villa Verde Bastos Borba, Vinícius M. Alves, Steven U.S. Hall, Nicholas Furnham, Nicole Kleinstreuer, Eugene N. Muratov, Alexander Tropsha, Carolina Horta Andrade · 2021 · Artificial Intelligence in the Life Sciences
18. Predicting In Vivo Compound Brain Penetration Using Multi-task Graph Neural Networks
    Seid Hamzic, Richard A. Lewis, Sandrine Desrayaud, Cihan Soylu, Michael Fortunato, Grégori Gerebtzoff, Raquel Rodríguez-Pérez · 2022 · Journal of Chemical Information and Modeling
19. Conformational Effects on the Passive Membrane Permeability of Synthetic Macrocycles
    Anna A. Rzepiela, Lauren A. Viarengo‐Baker, Victor Tatarskii, Roman V. Kombarov, Adrian Whitty · 2022 · Journal of Medicinal Chemistry
20. In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach
    Yi-Lung Ding, You-Chen Lyu, Max K. Leong · 2016 · Toxicology in Vitro
21. (Q)SAR Methods for Predicting Genotoxicity and Carcinogenicity: Scientific Rationale and Regulatory Frameworks
    Cecilia Bossa, Romualdo Benigni, Olga Tcheremenskaia, Chiara Laura Battistelli · 2018 · Methods in molecular biology
22. What place does molecular topology have in today’s drug discovery?
    Riccardo Zanni, María Gálvez-Llompart, Ramón García‐Domenech, Jorge Gálvez · 2020 · Expert Opinion on Drug Discovery
23. Medicare spending and mortality rates of the elderly.
    J Hadley · 1988 · PubMed
24. PySmash: Python package and individual executable program for representative substructure generation and application
    Ziyi Yang, Zhijiang Yang, Yue Zhao, Mingzhu Yin, Aiping Lü, Xiang Chen, Shao Liu, Tingjun Hou, Dongsheng Cao · 2021 · Briefings in Bioinformatics
25. Anesthetic drug discovery with computer-aided drug design and machine learning
    Xianggen Liu, Zhe Xue, Mingmin Luo, Bowen Ke, Jiancheng Lv · 2024 · Anesthesiology and Perioperative Science
26. Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning
    Muhammad Zafar Irshad Khan, Jia-Nan Ren, Cheng Cao, Hong-Yu-Xiang Ye, Hao Wang, Ya-Min Guo, Jinrong Yang, Jian‐Zhong Chen · 2024 · Frontiers in Pharmacology
27. ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction
    Dong Wang, Jieyu Jin, Guqin Shi, Jingxiao Bao, Zheng Wang, Shimeng Li, Peichen Pan, Dan Li, Yu Kang, Tingjun Hou · 2025 · Journal of Cheminformatics
28. Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies
    Bilal Ahmad, Khmaies Ouahada, Habib Hamam · 2026 · Computational and Structural Biotechnology Journal
29. Cytotoxicity of cardiotonic steroids in sensitive and multidrug-resistant leukemia cells and the link with Na+/K+-ATPase
    Maen Zeino, Ruth Brenk, Lisa Gruber, Martin Zehl, Ernst Urban, Brigitte Kopp, Thomas Efferth · 2015 · The Journal of Steroid Biochemistry and Molecular Biology
30. Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening
    Stanisław Jastrzȩbski, Maciej Szymczak, Agnieszka Pocha, Stefan Mordalski, Jacek Tabor, Andrzej Jacek Bojarski, Sabina Podlewska · 2020 · Journal of Chemical Information and Modeling
31. Overview of Antifungal Drugs against Paracoccidioidomycosis: How Do We Start, Where Are We, and Where Are We Going?
    Lívia do Carmo Silva, Amanda Alves de Oliveira, Dienny Rodrigues de Souza, Katheryne Lohany Barros Barbosa, Kleber Santiago Freitas e Silva, Marcos Antonio Batista de Carvalho Júnior, Olivia Basso Rocha, Raisa Melo Lima, Thaynara Gonzaga Santos, Célia Maria de Almeida Soares et al. · 2020 · Journal of Fungi
32. Green Toxicology: Connecting Green Chemistry and Modern Toxicology
    Johanna Krebs, Maureen McKeague · 2020 · Chemical Research in Toxicology
33. Evaluation of the OECD QSAR toolbox automatic workflow for the prediction of the acute toxicity of organic chemicals to fathead minnow
    Enrico Mombelli, Pascal Pandard · 2021 · Regulatory Toxicology and Pharmacology
34. Predicting zebrafish (Danio rerio) embryo developmental toxicity through a non-conformational QSAR approach
    Laura M. Saavedra, Pablo R. Duchowicz · 2021 · The Science of The Total Environment
35. Quantitative in vitro to in vivo extrapolation for developmental toxicity potency of valproic acid analogues
    Xiaoqing Chang, Jessica A. Palmer, Annie Lumen, Un Jung Lee, Patricia C. Ceger, Kamel Mansouri, Catherine S. Sprankle, Elizabeth L.R. Donley, Shannon M. Bell, Thomas B. Knudsen et al. · 2022 · Birth Defects Research
36. Protein Function Analysis through Machine Learning
    Chris Avery, John Patterson, Tyler J. Grear, Theodore D. Frater, Donald J. Jacobs · 2022 · Biomolecules
37. MetMaxStruct: A Tversky-Similarity-Based Strategy for Analysing the (Sub)Structural Similarities of Drugs and Endogenous Metabolites
    Steve O’Hagan, Douglas Bruce Kell · 2016 · Frontiers in Pharmacology
38. In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases
    Carolina Horta Andrade, Bruno J. Neves, Cleber Camilo Melo-Filho, Juliana Rodrigues, Diego Cabral Silva, Rodolpho C. Braga, Pedro Cravo · 2018 · Current Medicinal Chemistry
39. Combining traditional 2D and modern physical organic-derived descriptors to predict enhanced enantioselectivity for the key aza-Michael conjugate addition in the synthesis of Prevymis™ (letermovir)
    Toni T. Metsänen, Katrina W. Lexa, Celine B. Santiago, Cheol Keun Chung, Yingju Xu, Zhijian Liu, Guy R. Humphrey, Rebecca T. Ruck, Edward C. Sherer, Matthew S. Sigman · 2018 · Chemical Science
40. Analysis and Comparison of Vector Space and Metric Space Representations in QSAR Modeling
    Samina Kausar, André O. Falcão · 2019 · Molecules
41. QSAR studies of the antioxidant activity of anthocyanins
    Pablo R. Duchowicz, Nicolas Alejandro Szewczuk, Alicia Beatriz Pomilio · 2019 · Journal of Food Science and Technology
42. Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning
    Yuwei Miao, Hehuan Ma, Junzhou Huang · 2023 · Chemical Research in Toxicology
43. Creating New Antifoulants Using the Tools and Tactics of Medicinal Chemistry
    Patrick Louis Cahill, Lindon W. K. Moodie, Cora Hertzer, Emiliano Pinori, Henrik Pavia, Claire Hellio, Margaret Anne Brimble, Johan Svenson · 2024 · Accounts of Chemical Research
44. Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches
    Amit Kumar Halder, Sk. Abdul Amin, Tarun Jha, Shovanlal Gayen · 2017 · SAR and QSAR in environmental research
45. QSAR modeling of dihydrofolate reductase inhibitors as a therapeutic target for multiresistant bacteria
    Jovana B. Veselinović, Vukica Đorđević, Milena Bogdanović, Ivana Morić, Aleksandar M. Veselinović · 2017 · Structural Chemistry
46. A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data
    Francois Berenger, Yoshihiro Yamanishi · 2018 · Journal of Chemical Information and Modeling
47. Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets
    Benoît Baillif, Joerg Wichard, Oscar Méndez‐Lucio, David Rouquié · 2020 · Frontiers in Chemistry
48. Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils
    Manuela Sabatino, Marco Fabiani, Mijat Božović, Stefanıa Garzoli, Lorenzo Antonini, Maria Elena Marcocci, Anna Teresa Palamara, Giovanna De Chiara, Rino Ragno · 2020 · Molecules
49. Extending the identification of structural features responsible for anti-SARS-CoV activity of peptide-type compounds using QSAR modelling
    Vijay Hariram Masand, Vesna Rastija, Meghshyam K. Patil, Ajaykumar Gandhi, A. Chapolikar · 2020 · SAR and QSAR in environmental research
50. Molecular docking and QSAR theoretical model for prediction of phthalazinone derivatives as new class of potent dengue virus inhibitors
    Samuel Ndaghiya Adawara, Gideon Adamu Shallangwa, Paul Andrew Mamza, Ibrahim Abdulkadir · 2020 · Beni-Suef University Journal of Basic and Applied Sciences
51. Prenylated (iso)flavonoids as antifungal agents against the food spoiler Zygosaccharomyces parabailii
    Sylvia Kalli, Carla Araya‐Cloutier, John W Chapman, Jan-Willem Sanders, Jean‐Paul Vincken · 2021 · Food Control
52. Electrochemoinformatics as an Emerging Scientific Field for Designing Materials and Electrochemical Energy Storage and Conversion Devices—An Application in Battery Science and Technology
    Igor I. Baskin, Yair Ein‐Eli · 2022 · Advanced Energy Materials
53. Identification of promising inhibitory heterocyclic compounds against acetylcholinesterase using QSAR, ADMET, biological activity, and molecular docking
    Hai Duc Nguyen, Min‐Sun Kim · 2023 · Computational Biology and Chemistry
54. AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease
    Alexander M. Andrianov, Mikita A. Shuldau, Konstantin V. Furs, Artsemi M. Yushkevich, Alexander V. Tuzikov · 2023 · International Journal of Molecular Sciences
55. QSAR in natural non-peptidic food-related compounds: Current status and future perspective
    Yi Chen Zhao, Yuting Xia, Yuandong Yu, Guizhao Liang · 2023 · Trends in Food Science & Technology
56. Activity and Property Landscape Modeling is at the Interface of Chemoinformatics and Medicinal Chemistry
    José L. Medina‐Franco, Gabriel Navarrete‐Vázquez, Oscar Méndez‐Lucio · 2015 · Future Medicinal Chemistry
57. Combining machine learning models of in vitro and in vivo bioassays improves rat carcinogenicity prediction
    Davy Guan, Kevin Fan, Ian Spence, Slade T. Matthews · 2018 · Regulatory Toxicology and Pharmacology
58. Synthesis, bioactivity and mode of action of 5A5B6C tricyclic spirolactones as novel antiviral lead compounds
    Yu‐Jie Zhu, Qifan Wu, Zhijin Fan, Jingqian Huo, Jinlin Zhang, Bin Zhao, Lai Chen, Xiaolin Qian, Dejun Ma, Dawei Wang · 2018 · Pest Management Science
59. Integrating the Structure–Activity Relationship Matrix Method with Molecular Grid Maps and Activity Landscape Models for Medicinal Chemistry Applications
    Atsushi Yoshimori, Toru Tanoue, Jürgen Bajorath · 2019 · ACS Omega
60. QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction
    Isidro Cortés‐Ciriano, Ctibor Škuta, Andreas Bender, Daniel Svozil · 2020 · Journal of Cheminformatics
61. A Review on AI-Based Data-Driven Models for Optimization of Nanocarriers as Drug Delivery Systems
    Riddhi Kantesaria, Himanshu Sekhar Panda · 2026 · ACS Biomaterials Science & Engineering
62. Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2
    Matthew P. Baumgartner, David A. Price Evans · 2017 · Journal of Computer-Aided Molecular Design
63. Computational modeling approaches to quantitative structure–binding kinetics relationships in drug discovery
    Pier Giuseppe De Benedetti, Francesca Fanelli · 2018 · Drug Discovery Today
64. Guiding Conventional Protein–Ligand Docking Software with Convolutional Neural Networks
    Huaipan Jiang, Mengran Fan, Jian Wang, Anup Sarma, Shruti Mohanty, Nikolay V. Dokholyan, Mehrdad Mahdavi, Mahmut Kandemir · 2020 · Journal of Chemical Information and Modeling
65. Ensemble machine learning to evaluate the in vivo acute oral toxicity and in vitro human acetylcholinesterase inhibitory activity of organophosphates
    Liangliang Wang, Junjie Ding, Peichang Shi, Li Fu, Pan Li, Jia-Hao Tian, Dongsheng Cao, Hui Jiang, Xiaoqin Ding · 2021 · Archives of Toxicology
66. In Silico Prediction of Skin Permeability Using a Two-QSAR Approach
    Yu-Wen Wu, Giang Huong Ta, Yi-Chieh Lung, Ching‐Feng Weng, Max K. Leong · 2022 · Pharmaceutics
67. Target Specific Inhibition of Protein Tyrosine Kinase in Conjunction With Cancer and SARS-COV-2 by Olive Nutraceuticals
    Arabinda Ghosh, Nobendu Mukerjee, Bhavdeep Sharma, Anushree Pant, Yugal Kishore Mohanta, Rahul D. Jawarkar, Ravindra L. Bakal, Ermias Mergia Terefe, Gaber El‐Saber Batiha, Gomaa Mostafa‐Hedeab et al. · 2022 · Frontiers in Pharmacology
68. Computational Methods in Cooperation with Experimental Approaches to Design Protein Tyrosine Phosphatase 1B Inhibitors in Type 2 Diabetes Drug Design: A Review of the Achievements of This Century
    Mara Ibeth Campos-Almazán, Alicia Hernández‐Campos, Rafael Castillo, Erick Sierra‐Campos, Mónica Andrea Valdez-Solana, Claudia I. Avitia-Domínguez, Alfredo Téllez‐Valencia · 2022 · Pharmaceuticals
69. Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
    Ge Wang, Yuhao Bai, Jiarui Cui, Zirui Zong, Yuan Gao, Zhen Zheng · 2022 · Molecules
70. Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods
    Jianing Fan, Shaohua Shi, Hong Xiang, Li Fu, Yanjing Duan, Dongsheng Cao, Hongwei Lu · 2024 · Journal of Chemical Information and Modeling
71. Representing molecular and materials data for unsupervised machine learning
    Ellen T. Swann, Baichuan Sun, Deidre M. Cleland, Amanda Susan Barnard · 2018 · Molecular Simulation
72. N-doped carbon dots/g-C3N4 photoexcitation simultaneously enhanced the microbial degradation of nitrate and oxytetracycline in a low C/N ratio aquaculture wastewater
    Pin Peng, Lixiang Chen, Xinyu Yan, Rui Bai, Bob Adyari, Xudong Zhou, Feng Zhao · 2024 · Chemical Engineering Journal
73. POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles
    Josef Kehrein, Alex Bunker, Robert Luxenhofer · 2024 · Molecular Pharmaceutics
74. Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties
    Srivathsan Badrinarayanan, Chakradhar Guntuboina, Parisa Mollaei, Amir Barati Farimani · 2024 · Journal of Chemical Information and Modeling
75. Structure-Based Approach for the Prediction of Mu-opioid Binding Affinity of Unclassified Designer Fentanyl-Like Molecules
    Giuseppe Floresta, Antonio Rescifina, Vincenzo Abbate · 2019 · International Journal of Molecular Sciences
76. Integrative Multi-Kinase Approach for the Identification of Potent Antiplasmodial Hits
    Marilia N. N. Lima, Gustavo Capatti Cassiano, Kaira C. P. Tomaz, Arthur C. Silva, Bruna Katiele de Paula Sousa, Leticia T. Ferreira, Tatyana Almeida Tavella, Juliana Calit, Daniel Y. Bargieri, Bruno J. Neves et al. · 2019 · Frontiers in Chemistry
77. Thyroid-Disrupting Effects of 6:2 and 8:2 Polyfluoroalkyl Phosphate Diester (diPAPs) at Environmentally Relevant Concentrations from Integrated In Silico and In Vivo Studies
    Pengyu Chen, Jing Chun Yang, Geng Chen, Shujun Yi, Menglin Liu, Lingyan Y. Zhu · 2020 · Environmental Science & Technology Letters
78. HCVpred : A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors
    Aijaz Ahmad Malik, Chuleeporn Phanus‐umporn, Nalini Schaduangrat, Watshara Shoombuatong, Chartchalerm Isarankura‐Na‐Ayudhya, Chanin Nantasenamat · 2020 · Journal of Computational Chemistry
79. Construction of pioneering quantitative structure activity relationship screening models for abuse potential of designer drugs using index of ideality of correlation in monte carlo optimization
    Ashwani Kumar, Prof Parvin Kumar · 2020 · Archives of Toxicology
80. COMPARE Analysis, a Bioinformatic Approach to Accelerate Drug Repurposing against Covid-19 and Other Emerging Epidemics
    Imad Naasani · 2020 · SLAS DISCOVERY
81. Application of Machine Learning and Reaction Optimization for the Iterative Improvement of Enantioselectivity of Cinchona-Derived Phase Transfer Catalysts
    Katrina W. Lexa, Kevin M. Belyk, Jeremy J. Henle, Bangping Xiang, Robert P. Sheridan, Scott E. Denmark, Rebecca T. Ruck, Edward C. Sherer · 2021 · Organic Process Research & Development
82. Natural Polyether Ionophores and Their Pharmacological Profile
    Valery M. Dembitsky · 2022 · Marine Drugs
83. QSPR Modelling of the Solubility of Drug and Drug‐like Compounds in Supercritical Carbon Dioxide
    Imane Euldji, Cherif Si‐Moussa, Mabrouk Hamadache, Othmane Benkortbi · 2022 · Molecular Informatics
84. Graph Neural Networks for the Prediction of Molecular Structure–Property Relationships
    Jan G. Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, Artur M. Schweidtmann · 2023 · Royal Society of Chemistry eBooks
85. Computational study involving identification of endocrine disrupting potential of herbicides: Its implication in TDS and cancer progression in CRPC patients
    Md. Irshad Ahmad, Afia Usman, Masood Ahmad · 2017 · Chemosphere
86. QSAR study of dipeptidyl peptidase-4 inhibitors based on the Monte Carlo method
    Dusan T. Sokolovic, Jasmina Ranković, Veroljub Stanković, Rade Stefanović, Sladjan Karaleić, Branimir B. Mekić, Vesko Milenković, Jadranka Kocić, Aleksandar M. Veselinović · 2017 · Medicinal Chemistry Research
87. Assessment of predictivity of volatile organic compounds carcinogenicity and mutagenicity by freeware in silico models
    Lilia R. Guerra, Alessandra Mendonça Teles de Souza, Juliana Alves Côrtes, Viviane de Oliveira Freitas Lione, Helena Carla Castro, Gutemberg Gomes Alves · 2017 · Regulatory Toxicology and Pharmacology
88. Use of Simplified Molecular Input Line Entry System and Molecular Graph Based Descriptors in Prediction and Design of Pancreatic Lipase Inhibitors
    Ashwani Kumar, Shilpi Chauhan · 2018 · Future Medicinal Chemistry
89. An integrated quantitative structure and mechanism of action-activity relationship model of human serum albumin binding
    Angela Serra, Serli Önlü, Pietro Coretto, Dario Greco · 2019 · Journal of Cheminformatics
90. Multi-task learning with a natural metric for quantitative structure activity relationship learning
    Noureddin Sadawi, Iván Olier, Joaquin Vanschoren, Jan N. van Rijn, Jérémy Besnard, Richard Bickerton, Crina Groşan, Larisa Soldatova, Ross D. King · 2019 · Journal of Cheminformatics
91. Hierarchical Quantitative Structure–Activity Relationship Modeling Approach for Integrating Binary, Multiclass, and Regression Models of Acute Oral Systemic Toxicity
    Xinhao Li, Nicole Kleinstreuer, Denis Fourches · 2020 · Chemical Research in Toxicology
92. Cheminformatics Analysis and Modeling with MacrolactoneDB
    Phyo Phyo Kyaw Zin, Gavin J. Williams, Sean Ekins · 2020 · Scientific Reports
93. Semi-supervised regression trees with application to QSAR modelling
    Jurica Levatić, Michelangelo Ceci, Tomaž Stepišnik, Sašo Džeroski, Dragi Kocev · 2020 · Expert Systems with Applications
94. Predicting molecular activity on nuclear receptors by multitask neural networks
    Cecile Valsecchi, Magda Collarile, Francesca Grisoni, Roberto Todeschini, Davide Ballabio, Viviana Consonni · 2020 · Journal of Chemometrics
95. Discovery of Synergistic and Antagonistic Drug Combinations against SARS-CoV-2 In Vitro
    Tesia Bobrowski, Lu Chen, Richard T. Eastman, Zina Itkin, Paul Shinn, Catherine Chen, Hui Guo, Wei Zheng, Sam Michael, Anton Simeonov et al. · 2020 · bioRxiv (Cold Spring Harbor Laboratory)
96. Explaining molecular properties with natural language
    Heta A. Gandhi, Andrew D. White · 2022 · ChemRxiv
97. Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures
    Vijay Hariram Masand, Sami A. Al‐Hussain, Abdullah Yahya Abdullah Alzahrani, Aamal A. Al‐Mutairi, Rania A. Hussien, Abdul Samad, Magdi E. A. Zaki · 2024 · ACS Omega
98. Application of artificial intelligence in bioprospecting for natural products for biopharmaceutical purposes
    Victor Uchenna Chigozie, Chidozie Godwin Ugochukwu, Kingsley Otubo Igboji, Festus Basden Okoye · 2025 · BMC Artificial Intelligence
99. Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase
    C. David Andersson, J. Mikael Hillgren, Cecilia Lindgren, Weixing Qian, Christine Akfur, Lotta Berg, Fredrik Ekström, Anna Linusson · 2014 · Journal of Computer-Aided Molecular Design
100. Graph Memory Networks for Molecular Activity Prediction
     Trang Pham, Truyen Tran, Svetha Venkatesh · 2018 · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition