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

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Items 901–1,000 of 1,001

1. An Inverse QSAR Method Based on a Two-Layered Model and Integer Programming
   Yu Shi, Jianshen Zhu, Naveed Ahmed Azam, Kazuya Haraguchi, Liang Zhao, Hiroshi Nagamochi, Tatsuya Akutsu · 2021 · International Journal of Molecular Sciences
2. ERpred: a web server for the prediction of subtype-specific estrogen receptor antagonists
   Nalini Schaduangrat, Aijaz Ahmad Malik, Chanin Nantasenamat · 2021 · PeerJ
3. Towards Property Profiling: SYNTHESIS and SAR Probing of New Tetracyclic Diazaphenothiazine Analogues
   Anna Empel, Andrzej Bąk, Violetta Kozik, Małgorzata T Latocha, Alois Čížek, Josef Jampílek, Kinga Suwinska, Aleksander Sochanik, Andrzej Zięba · 2021 · International Journal of Molecular Sciences
4. Development of a QSAR model to predict comedogenic potential of some cosmetic ingredients
   Sebla Oztan Akturk, Gulcin Tugcu, Hande Si̇pahi̇ · 2021 · Computational Toxicology
5. Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency
   Kazue Chinen, Timothy F. Malloy · 2022 · International Journal of Environmental Research and Public Health
6. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents
   Amal Alqahtani · 2022 · Evidence-based Complementary and Alternative Medicine · Retracted
7. Mixtures, metabolites, ionic liquids: a new measure to evaluate similarity between complex chemical systems
   Andrea Mauri, Davide Ballabio, Roberto Todeschini, Viviana Consonni · 2016 · Journal of Cheminformatics
8. Modernization of Enoxaparin Molecular Weight Determination Using Homogeneous Standards
   Katelyn Arnold, Stephen J. Capuzzi, Yongmei Xu, Eugene N. Muratov, Kevin Carrick, Anita Y. Szajek, Alexander Tropsha, Jian Liu · 2017 · Pharmaceuticals
9. Advantages of Relative versus Absolute Data for the Development of Quantitative Structure–Activity Relationship Classification Models
   Irene Luque Ruiz, Miguel Ángel Gómez‐Nieto · 2017 · Journal of Chemical Information and Modeling
10. Proteochemometric Modeling for Drug Repositioning
    Nalini Schaduangrat, Nuttapat Anuwongcharoen, Chuleeporn Phanus‐umporn, Nagaya Sriwanichpoom, Jarl E. S. Wikberg, Chanin Nantasenamat · 2019 · Elsevier eBooks
11. QSAR studies on benzothiophene derivatives as Plasmodium falciparum N‐myristoyltransferase inhibitors: Molecular insights into affinity and selectivity
    Mariana Lopes Garcia, Andrew Albert de Oliveira, Renata Vieira Bueno, Victor H. R. Nogueira, Guilherme E. de Souza, Rafael V. C. Guido · 2020 · Drug Development Research
12. Clinical pharmacokinetic study of latrepirdine via in silico sublingual administration
    Joana Santos, Luísa Correia Lopes Lobato, Nuno Vale · 2021 · In Silico Pharmacology
13. Improving QSAR Modeling for Predictive Toxicology using Publicly Aggregated Semantic Graph Data and Graph Neural Networks
    Joseph D. Romano, Yun Hao, Jason H. Moore · 2021 · Pacific Symposium on Biocomputing (PSB)
14. Greedy and Linear Ensembles of Machine Learning Methods Outperform Single Approaches for QSPR Regression Problems
    William Kew, John B. O. Mitchell · 2015 · Molecular Informatics
15. CADD medicine: design is the potion that can cure my disease
    Eric S. Manas, Darren V. S. Green · 2017 · Journal of Computer-Aided Molecular Design
16. Ecotoxicity Databases for QSAR Modeling
    Shinjita Ghosh, Supratik Kar, Jerzy Leszczyński · 2020 · Methods in pharmacology and toxicology
17. Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries
    Dmitry S. Druzhilovskiy, Leonid A. Stolbov, Полина Игоревна Савосина, Pavel V. Pogodin, Dmitry A. Filimonov, Alexander V. Veselovsky, Karen M. Stefanisko, Nadya I. Tarasova, Marc C. Nicklaus, Vladimir Poroikov · 2020 · Supercomputing Frontiers and Innovations
18. Biological pathways of per- and polyfluoroalkyl substances (PFAS): A critical review of cellular and molecular toxicity mechanisms
    McKenna Dunmyer, Jack Welchert, Christopher I. Olivares, Vasiliki Karanikola · 2026 · Journal of Hazardous Materials
19. Chemical space and molecular descriptors for QSAR studies
    Viviana Consonni, Davide Ballabio, Roberto Todeschini · 2023 · Elsevier eBooks
20. On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 1—From Data Collection to Model Construction: Understanding of the Methods and Their Effects
    Cindy Trinh, Youssef Tbatou, Silvia Lasala, Olivier Herbinet B, Dimitrios Meimaroglou · 2023 · Processes
21. Machine Learning Prediction of On/Off Target-driven Clinical Adverse Events
    Albert Cao, Luchen Zhang, Yingzi Bu, Duxin Sun · 2024 · Pharmaceutical Research
22. Seasonal variability and risk evaluation of emerging organic contaminants in European river: Linking in silico and in vitro approaches to prioritize hazardous EOCs
    Göran I.V. Klobučar, Ana Selak, Draženka Stipaničev, Siniša Repec, Matej Vucić, Krunoslav Bojanić, Mario Šiljeg, Sandra Hudina, Dalma Martinović‐Weigelt · 2025 · Environmental Research
23. In Silico High-Performance Liquid Chromatography Method Development via Machine Learning
    Alberto Marchetto, Monica Tirapelle, Luca Mazzei, Eva Sørensen, Maximilian O. Besenhard · 2025 · Analytical Chemistry
24. How to rank and discriminate artificial neural networks? Case study: prediction of anticancer activity of 17-picolyl and 17-picolinylidene androstane derivatives
    Strahinja Z. Kovačević, Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Evgenija A. Djurendić, Jovana J. Ajduković, Slobodan B. Gadžurić, Milan Vraneš · 2015 · Journal of the Iranian Chemical Society
25. Prediction and interpretation of the lipophilicity of small peptides
    Alessia Visconti, Giuseppe Ermondi, Giulia Caron, Roberto Esposito · 2015 · Journal of Computer-Aided Molecular Design
26. Quantitative structure–activity relationships study of potent pyridinone scaffold derivatives as HIV-1 integrase inhibitors with therapeutic applications
    Abolfazl Barzegar, Hossein Hamidi Shishavan · 2017 · Journal of Theoretical and Computational Chemistry
27. Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach
    S. Bhuvaneshwari, Kavitha Sankaranarayanan · 2019 · SAR and QSAR in environmental research
28. VISAR: an interactive tool for dissecting chemical features learned by deep neural network QSAR models
    Qingyang Ding, Siyu Hou, Songpeng Zu, Yonghui Zhang, Shao Li · 2020 · Bioinformatics
29. Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study
    Vijay Hariram Masand, Siddhartha Akasapu, Ajaykumar Gandhi, Vesna Rastija, Meghshyam K. Patil · 2020 · Chemometrics and Intelligent Laboratory Systems
30. Computer-assisted discovery of compounds with insecticidal activity against Musca domestica and Mythimna separata
    Gabriela Cristina Soares Rodrigues, Mayara dos Santos Maia, Andreza Barbosa Silva Cavalcanti, Renata Priscila Costa Barros, Luciana Scotti, Carlos L. Céspedes, Eugene N. Muratov, Marcus Tullius Scotti · 2020 · Food and Chemical Toxicology
31. Developing a variation of 3D‐QSAR / MD method in drug design
    Hamed Haghshenas, Bita Kaviani, Monireh Firouzeh, Hossein Tavakol · 2021 · Journal of Computational Chemistry
32. Comparisons of Molecular Structure Generation Methods Based on Fragment Assemblies and Genetic Graphs
    Philippe Gantzer, Benoît Creton, Carlos Nieto‐Draghi · 2021 · Journal of Chemical Information and Modeling
33. Monte Carlo Optimization Method Based QSAR Modeling of Placental Barrier Permeability
    Predrag Vukomanović, Milan Stefanović, Jelena Milošević, Aleksandra Petrić, Milan S. Trenkić, Lazar Andrejević, Milan Lazarević, Danka Sokolović, Aleksandar M. Veselinović · 2024 · Pharmaceutical Research
34. FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology
    Pieter B. Burger, Xiaohu Hu, Ilya A. Balabin, Morné Muller, Megan Stanley, Fourie Joubert, Thomas M. Kaiser · 2024 · Journal of Chemical Information and Modeling
35. Improving the prediction of organism-level toxicity through integration of chemical, protein target and cytotoxicity qHTS data
    Chad H. G. Allen, Alexios Koutsoukas, Isidro Cortés‐Ciriano, Daniel S Murrell, Thérèse E. Malliavin, Robert Charles Glen, Andreas Bender · 2016 · Toxicology Research
36. An integrated approach with new strategies for QSAR models and lead optimization
    Hui-Hui Hsu, Yen-Chao Hsu, Li-Jen Chang, Jinn‐Moon Yang · 2017 · BMC Genomics
37. Novel 4-acetamide-2-alkylthio-N-acetanilides resembling nimesulide: Synthesis, cell viability evaluation and in silico studies
    Mafalda Catarro, João L. Serrano, Eunice Cavalheiro, Susana S. Ramos, Adriana O. Santos, Samuel Silvestre, Paulo Almeida · 2017 · Bioorganic & Medicinal Chemistry
38. QSAR study and rustic ligand-based virtual screening in a search for aminooxadiazole derivatives as PIM1 inhibitors
    Adnane Aouidate, Adib Ghaleb, Mounir Ghamali, Samir Chtita, Abdellah Ousaa, M’barek Choukrad, Abdelouahid Sbai, Mohammed Bouachrıne, Tahar Lakhlifi · 2018 · Chemistry Central Journal
39. Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method
    James Oreluk, Zhenyuan Liu, Arun Hegde, Wenyu Li, Andrew Packard, Michael Frenklach, Dmitry Yu. Zubarev · 2018 · Scientific Reports
40. MIA-QSAR based model for bioactivity prediction of flavonoid derivatives as acetylcholinesterase inhibitors
    Panchaksaram Muthukumaran, Rajiniraja Muniyan · 2018 · Journal of Theoretical Biology
41. Cheminformatics approach to exploring and modeling trait-associated metabolite profiles
    Jeremy R. Ash, Mélaine A. Kuenemann, Daniel Miller Rotroff, Alison A. Motsinger‐Reif, Denis Fourches · 2019 · Journal of Cheminformatics
42. A Random Forest Model to Predict the Activity of a Large Set of Soluble Epoxide Hydrolase Inhibitors Solely Based on a Set of Simple Fragmental Descriptors
    Jamal Shamsara · 2019 · Combinatorial Chemistry & High Throughput Screening
43. Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt
    Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen · 2019 · Angewandte Chemie
44. Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction
    Tan Xian, Yang Yu, Kaiwen Duan, Jingbo Zhang, Pingping Sun, Hui Sun · 2020 · Current Topics in Medicinal Chemistry
45. On the intrinsic reactivity of highly potent trypanocidal cruzain inhibitors
    Vinícius Bonatto, Pedro Henrique Jataí Batista, Lorenzo Cianni, Daniela De Vita, Daniel Gedder Silva, Rodrigo Cedron, Daiane Y. Tezuka, Sérgio de Albuquerque, Carolina Borsoi Moraes, Caio Haddad Franco et al. · 2020 · RSC Medicinal Chemistry
46. Balanced QSAR analysis to identify the structural requirements of ABBV-075 (Mivebresib) analogues as bromodomain and extraterminal domain (BET) family bromodomain inhibitor
    Vijay Hariram Masand, Meghshyam K. Patil, Nahed N. E. El-Sayed, Magdi E. A. Zaki, Zainab M. Almarhoon, Sami A. Al‐Hussain · 2020 · Journal of Molecular Structure
47. How doppelgänger effects in biomedical data confound machine learning
    Li Rong Wang, Limsoon Wong, Wilson Wen Bin Goh · 2021 · Drug Discovery Today
48. Selection of Promising Novel Fragment Sized S. aureus SrtA Noncovalent Inhibitors Based on QSAR and Docking Modeling Studies
    Dmitry Alexandrovich Shulga, Konstantin V. Kudryavtsev · 2021 · Molecules
49. Interpreting the Physicochemical Meaning of a Molecular Descriptor Which Is Predictive of Amorphous Solid Dispersion Formation in Polyvinylpyrrolidone Vinyl Acetate
    Kevin DeBoyace, Mustafa Bookwala, Ira S. Buckner, Deliang Zhou, Peter L. D. Wildfong · 2021 · Molecular Pharmaceutics
50. A Multi-Objective Genetic Algorithm for Outlier Removal
    Oren E. Nahum, Abraham Yosipof, Hanoch Senderowitz · 2015 · Journal of Chemical Information and Modeling
51. QSPR modeling of optical rotation of amino acids using specific quantum chemical descriptors
    Каріна Капуста, Natalia Sizochenko, Sedat Karabulut, Sergiy I. Okovytyy, E. O. Voronkov, Jerzy Leszczyński · 2018 · Journal of Molecular Modeling
52. Out‐compute drug side effects: Focus on cytochrome P450 2D6 modeling
    Charleen G. Don, Martin Smieško · 2018 · Wiley Interdisciplinary Reviews Computational Molecular Science
53. QSAR Implementation for HIC Retention Time Prediction of mAbs Using Fab Structure: A Comparison between Structural Representations
    Micael Karlberg, João Victor de Souza, Lanyu Fan, Arathi Kizhedath, Agnieszka K. Bronowska, Jarka Glassey · 2020 · International Journal of Molecular Sciences
54. Chemoinformatics: From Chemical Art to Chemistry in Silico
    Jarosław Polański · 2018 · Elsevier eBooks
55. The system of self-consistent models for vapour pressure
    Alla P. Toropova, Andrey Andreevich Toropov, Alessandra Roncaglioni, Emilio Benfenati · 2022 · Chemical Physics Letters
56. Explainable artificial intelligence in the design of selective carbonic anhydrase I‐II inhibitors via molecular fingerprinting
    Kevser Kübra Kırboğa, Mesut Işık · 2024 · Journal of Computational Chemistry
57. QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning
    Zhijiang Yang, Tengxin Huang, Li Pan, Jingjing Wang, Liangliang Wang, Junjie Ding, Junhua Xiao · 2024 · Journal of Cheminformatics
58. Chemical hazard assessment toward safer electrolytes for lithium-ion batteries
    Branden Schwaebe, Haoyang He, Christopher Glaubensklee, Oladele A. Ogunseitan, Julie M. Schoenung · 2024 · Integrated Environmental Assessment and Management
59. Applications of Computational Tools in the Prediction of Toxicity
    Bhavna Mahajan, Priyanka Gawarkar‐Patil, Pratibha R. Adnaik, Nikita N. Bagade, Priyanka Gondhale-Karpe, Sonali Manwatkar, Sojwal Patil · 2024 · Springer eBooks
60. Toxicity of Bromo-DragonFLY as a New Psychoactive Substance: Application of In Silico Methods for the Prediction of Key Toxicological Parameters Important to Clinical and Forensic Toxicology
    Maciej Noga, Kamil Jurowski · 2024 · Chemical Research in Toxicology
61. Ecotoxicological risk assessment of active pharmaceutical ingredients (APIs) against different aquatic species leveraging intelligent consensus prediction and i-QSTTR modeling
    Pabitra Narayan Samanta, P.K. Bhattacharyya, Abhisek Samal, Ankur Kumar, Arnab Bhattacharjee, Probir Kumar Ojha · 2024 · Journal of Hazardous Materials
62. Decoding cyanide toxicity: Integrating Quantitative Structure-Toxicity Relationships (QSTR) with species sensitivity distributions and q-RASTR modeling
    Kabiruddin Khan, Ramin Abdullayev, Gopala Krishna Jillella, Varun Gopalakrishnan Nair, Mahmoud Bousily, Supratik Kar, Agnieszka Gajewicz · 2025 · Ecotoxicology and Environmental Safety
63. Active Learning FEP: Impact on Performance of AL Protocol and Chemical Diversity
    Richard Lonsdale, Jack Glancy, Leen N. Kalash, David Marcus, Ian D. Wall · 2025 · Journal of Chemical Theory and Computation
64. Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design
    Muhammad Arslan Masood, Samuel Kaski A, Tianyu Cui · 2025 · Journal of Cheminformatics
65. Improved QSAR methods for predicting drug properties utilizing topological indices and machine learning models
    Muhammad Shoaib Sardar, Muhammad Shahid Iqbal, Muhammad Mudassar Hassan, Changjiang Bu, Sharafat Hussain · 2025 · The European Physical Journal E
66. Machine Learning Prediction of Protein Adsorption on Drug-delivering Nanoparticles: A Literature Survey and Need for Future Development
    Koushiki Basu, Venkata S. Chelagamsetty, Veronica A. Ruiz-Avila, Tonglei Li · 2025 · Pharmaceutical Research
67. Ligand-Based Virtual Screening Using Tailored Ensembles: A Prioritization Tool for Dual A 2A Adenosine Receptor Antagonists / Monoamine Oxidase B Inhibitors
    Aliuska Morales Helguera, Yunierkis Pérez‐Castillo, M. Natália D. S. Cordeiro, Eduardo Tejera, César Paz‐y‐Miño, Aminael Sánchez‐Rodríguez, Marta Teijeira, Evys Ancede‐Gallardo, Fernando Cagide, Fernanda Borges et al. · 2016 · Current Pharmaceutical Design
68. Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation
    Mengmeng Huang, Yan Wei, Jun Wang, Yu Zhang · 2016 · Scientific Reports
69. Improving confidence in (Q)SAR predictions under Canada’s Chemicals Management Plan – a chemical space approach
    Sunil A. Kulkarni, Emilio Benfenati, Tara S. Barton‐Maclaren · 2016 · SAR and QSAR in environmental research
70. Cyclin dependent kinase 4 inhibitory activity of Thieno[2,3-d] pyrimidin-4-ylhydrazones – Multiple QSAR and docking studies
    V. Divya, V.L. Pushpa, K. B. Manoj · 2019 · Journal of Molecular Structure
71. chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models
    Jeremy R. Ash, Jacqueline M. Hughes‐Oliver · 2018 · Journal of Cheminformatics
72. Microbiological Aspects of Unique, Rare, and Unusual Fatty Acids Derived from Natural Amides and Their Pharmacological Profile
    Valery M. Dembitsky · 2022 · Microbiology Research
73. Structural characterization of functional peptides by extending the hybrid orbital theory
    Jun Zhong, Jiaoyan Ren · 2022 · eFood
74. Discovery and Design of Radiopharmaceuticals by In silico Methods
    David Alan Winkler, Maryam Salahinejad, Fereshteh Shiri · 2022 · Current Radiopharmaceuticals
75. Evaluating molecular fingerprint-based models of drug side effects against a statistical control
    Berk A. Alpay, Mark M. Gosink, Derek Aguiar · 2022 · Drug Discovery Today
76. Review and Development of Best Practices for Toxicity Tests with Dreissenid Mussels
    Diane L. Waller, Sherri Pucherelli, Matthew T. Barbour, Samantha Tank, Matthew Meulemans, Jeremy K. Wise, Angelique D. Dahlberg, David C. Aldridge, Renata Claudi, W. Gregory Cope et al. · 2023 · Environmental Toxicology and Chemistry
77. PepScaf: Harnessing Machine Learning with In Vitro Selection toward De Novo Macrocyclic Peptides against IL-17C/IL-17RE Interaction
    Silong Zhai, Yahong Tan, Chengyun Zhang, Christopher J. Hipolito, Lulu Song, Cheng Zhu, You‐Ming Zhang, Hongliang Duan, Yizhen Yin · 2023 · Journal of Medicinal Chemistry
78. Quinoxalines against Leishmania amazonensis: SAR study, proposition of a new derivative, QSAR prediction, synthesis, and biological evaluation
    Anna Carolina Silva De Jesus Passaes, Juliana A. Dantas, Fernanda Landim Lopes, Diego Pereira Sangi, Magaly Girão Albuquerque, Celso Vataru Nakamura, Julliane Diniz Yoneda · 2023 · Scientific Reports
79. Rational drug design applied to myeloperoxidase inhibition
    Pierre Van Antwerpen, Karim Zouaoui Boudjeltia · 2015 · Free Radical Research
80. Exploiting mAb structure characteristics for a directed QbD implementation in early process development
    Micael Karlberg, Moritz von Stosch, Jarka Glassey · 2018 · Critical Reviews in Biotechnology
81. An Overview of Antiretroviral Agents for Treating HIV Infection in Paediatric Population
    Rita Melo, Agostinho Lemos, António J. Preto, Beatriz Bueschbell, Pedro Matos‐Filipe, Carlos A.V. Barreto, José Guilherme de Almeida, Rúben D. M. Silva, João D. G. Correia, Irina Sousa Moreira · 2018 · Current Medicinal Chemistry
82. Elucidating the aryl hydrocarbon receptor antagonism from a chemical-structural perspective
    Elizabeth Goya‐Jorge, T.Q. Doan, Marie‐Louise Scippo, Marc Müller, Rosa María Giner, Stephen J. Barigye, Rafael Gozalbes · 2020 · SAR and QSAR in environmental research
83. Setting New Routes for Antifungal Drug Discovery Against Pathogenic Fungi
    Kleber Santiago Freitas e Silva, Lívia do Carmo Silva, Relber Aguiar Gonçales, Bruno J. Neves, Célia Maria de Almeida Soares, Maristela Pereira · 2020 · Current Pharmaceutical Design
84. Quantitative structure–toxicity relationships in translational toxicology
    Svetoslav H. Slavov, Richard D. Beger · 2020 · Current Opinion in Toxicology
85. Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives
    Jennifer Lagoutte‐Renosi, Florentin Allemand, Christophe Ramseyer, Semen O. Yesylevskyy, Siamak Davani · 2021 · Drug Discovery Today
86. Computer-Assisted Discovery of Alkaloids with Schistosomicidal Activity
    Renata Priscila Barros de Menezes, Jéssika de Oliveira Viana, Eugene N. Muratov, Luciana Scotti, Marcus Tullius Scotti · 2022 · Current Issues in Molecular Biology
87. Semi-automated harmonization and selection of chemical data for risk and impact assessment
    Nicolò Aurisano, Peter Fantke · 2022 · Chemosphere
88. Convergent QSAR studies on a series of NK3receptor antagonists for schizophrenia treatment
    Marina Candido Primi, Vinícius Gonçalves Maltarollo, Juliana Gallottini Magalhães, Matheus Malta de Sá, Carlota de Oliveira Rangel‐Yagui, Gustavo Henrique Goulart Trossini · 2015 · Journal of Enzyme Inhibition and Medicinal Chemistry
89. BCL::Mol2D—a robust atom environment descriptor for QSAR modeling and lead optimization
    Oanh Vu, Jeffrey Mendenhall, Doaa Altarawy, Jens Meiler · 2019 · Journal of Computer-Aided Molecular Design
90. Molecular Design of Environment-Friendly PAE Derivatives Based on 3D-QSAR Assisted with a Comprehensive Evaluation Method Combining Toxicity and Estrogen Activities
    Qing X. Li, Youli Qiu, Yu Li · 2020 · Water Air & Soil Pollution
91. Virtual Screening and Lead Discovery
    Nisha Kumari Singh, Nigam Jyoti Maiti, Manshi Mishra, Shantanu Raj, Gourav Rakshit, Rahul Ghosh, Sharanya Roy · 2024 · Wiley eBooks
92. Machine learning and traditional QSAR modeling methods: a case study of known PXR activators
    William M. Neal, Pankaj Pandey, Shabana Iqrar Khan, Ikhlas Ahmed Khan, Amar Gopal Chittiboyina · 2023 · Journal of Biomolecular Structure and Dynamics
93. Machine learning models for forecasting microplastic dynamics in China’s coastal waters
    Jing Li, Zhoujia Jiang, Ling Shu, Xiangyu Li, Chuanxi Wang, Haibo Zhang · 2025 · Journal of Hazardous Materials
94. Advances in AI for predicting pharmacological properties of natural medicines
    Tianyu Xu, Yuemiao Xu, Jinger Zhang, Yuchen Zhou, Huiying Feng, Aiqin Zhang, Yuhua Zhang · 2025 · Life Sciences
95. Alternative Toxicity Testing: Analyses on Skin Sensitization, ToxCast Phases I and II, and Carcinogenicity Provide Indications on How to Model Mechanisms Linked to Adverse Outcome Pathways
    Romualdo Benigni, Chiara Laura Battistelli, Cecilia Bossa, Alessandro Giuliani, Olga Tcheremenskaia · 2015 · Journal of Environmental Science and Health Part C
96. Off-Pocket Activity Cliffs: A Puzzling Facet of Molecular Recognition
    Tigran M. Abramyan, Yi An, Dmitri Kireev · 2019 · Journal of Chemical Information and Modeling
97. Environmental Toxicity (Q)SARs for Polymers as an Emerging Class of Materials in Regulatory Frameworks, with a Focus on Challenges and Possibilities Regarding Cationic Polymers
    Hans Sanderson, Kabiruddin Khan, Anna Magdalene Brun Hansen, Kristin A. Connors, Monica W. Lam, Kunal Roy, Scott E. Belanger · 2020 · Methods in pharmacology and toxicology
98. A QSAR modeling approach for predicting myeloid antimicrobial peptides with high sequence similarity
    Faiza Hanif Waghu, Ulka Gawde, Anish Gomatam, Evans C. Coutinho, Susan Idicula‐Thomas · 2020 · Chemical Biology & Drug Design
99. Suggestion of active 3-chymotrypsin like protease (3CL Pro ) inhibitors as potential anti-SARS-CoV-2 agents using predictive QSAR model based on the combination of ALASSO with an ANN model
    Zeinab Mozafari, Mansour Arab Chamjangali, ‎M‎ohammad Arashi, Nasser Goudarzi · 2021 · SAR and QSAR in environmental research
100. Skin sensitization quantitative QSAR models based on mechanistic structural alerts
     Chayawan Chayawan, Gianluca Selvestrel, Diego Baderna, Cosimo Toma, Ana Yisel Caballero Alfonso, A. Gamba, Emilio Benfenati · 2022 · Toxicology