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

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Items 401–500 of 1,001

1. The Application of the Combination of Monte Carlo Optimization Method based QSAR Modeling and Molecular Docking in Drug Design and Development
   Maja Živković, Marko Zlatanović, Nevena Zlatanović, Mlađjan Golubović, Aleksandar M. Veselinović · 2020 · Mini-Reviews in Medicinal Chemistry
2. Combined 3D-QSAR and Molecular Docking Analysis of Thienopyrimidine Derivatives as Staphylococcus aureus Inhibitors
   Mebarka Ouassaf, Salah Belaıdı, Saida Khamoulı, Houmam Belaıdı, Samir Chtita · 2021 · Acta chimica slovenica
3. Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds
   Oky Hermansyah, Alhadi Bustamam, Arry Yanuar · 2021 · Computational Biology and Chemistry
4. Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?
   Kai-Yue Ji, Chong Liu, Zhaoqian Liu, Yafeng Deng, Tingjun Hou, Dongsheng Cao · 2023 · Briefings in Bioinformatics
5. Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology
   Tomasz Puzyn, Nina Jeliazkova, Haralambos Sarimveis, Richard Marchese Robinson, Vladimir Lobaskin, Robert Ralló, A.-N. Richarz, Agnieszka Gajewicz, Manthos G. Papadopulos, Janna Hastings et al. · 2017 · Food and Chemical Toxicology
6. 100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design
   Andrew L. Ferguson, Rama Ranganathan · 2021 · ACS Macro Letters
7. Small Data Can Play a Big Role in Chemical Discovery
   Hadas Shalit Peleg, Anat Milo · 2023 · Angewandte Chemie International Edition
8. Computational Chemistry as Applied in Environmental Research: Opportunities and Challenges
   Christian Sandoval‐Pauker, Sheng Yin, Alexandria Castillo, Neidy Ocuane, Diego Puerto-Diaz, D. Villagrán · 2023 · ACS ES&T Engineering
9. The pursuit of accurate predictive models of the bioactivity of small molecules
   Karina Martínez‐Mayorga, José Guadalupe Rosas-Jiménez, Karla Gonzalez-Ponce, Edgar López‐López, Antonio Neme, José L. Medina‐Franco · 2024 · Chemical Science
10. Uncertainty quantification for molecular property predictions with graph neural architecture search
    Shengli Jiang, Shiyi Qin, Reid C. Van Lehn, Prasanna Balaprakash, Victor M. Zavala · 2024 · Digital Discovery
11. QSAR studies in the discovery of novel type-II diabetic therapies
    Areej Abuhammad, Mutasem O. Taha · 2015 · Expert Opinion on Drug Discovery
12. Statistical models are able to predict ionic liquid viscosity across a wide range of chemical functionalities and experimental conditions
    Wesley Beckner, Coco M. Mao, Jim Pfaendtner · 2018 · Molecular Systems Design & Engineering
13. Metoprolol and metoprolol acid degradation in UV/H2O2 treated wastewaters: An integrated screening approach for the identification of hazardous transformation products
    Adrián Jaén-Gil, Gianluigi Buttiglieri, Aleix Benito, Rafael Gonzalez‐Olmos, ‪Damià Barceló, Sara Rodríguez‐Mozaz · 2019 · Journal of Hazardous Materials
14. Drug Repurposing to Identify Nilotinib as a Potential SARS-CoV-2 Main Protease Inhibitor: Insights from a Computational and In Vitro Study
    Souvik Banerjee, Shalini Yadav, Sourav Banerjee, Sayo Olawale Fakayode, Jyothi Parvathareddy, Walter Reichard, Surekha Surendranathan, Foyez Mahmud, Ryan Whatcott, Joshua Thammathong et al. · 2021 · Journal of Chemical Information and Modeling
15. Transformational machine learning: Learning how to learn from many related scientific problems
    Iván Olier, Oghenejokpeme I. Orhobor, Tirtharaj Dash, Andy M. Davis, Larisa Soldatova, Joaquin Vanschoren, Ross D. King · 2021 · Proceedings of the National Academy of Sciences
16. QSAR-based virtual screening of traditional Chinese medicine for the identification of mitotic kinesin Eg5 inhibitors
    Damilola Samuel Bodun, Damilola Alex Omoboyowa, Olaposi Idowu Omotuyi, Ezekiel Abiola Olugbogi, Toheeb Adewale Balogun, Chiamaka J. Ezeh, Emmanuel Sunday Omirin · 2023 · Computational Biology and Chemistry
17. Novel triazolothiadiazines act as potent anticancer agents in liver cancer cells through Akt and ASK-1 proteins
    Sevim Peri Aytaç, İrem Durmaz, Douglas R. Houston, Rengül Çetin-Atalay, Birsen Tozkoparan · 2016 · Bioorganic & Medicinal Chemistry
18. Quantitative structure–activity relationship analysis and virtual screening studies for identifying HDAC2 inhibitors from known HDAC bioactive chemical libraries
    Hai Pham‐The, Gerardo M. Casañola‐Martín, Karel Diéguez‐Santana, N. Nguyen-Hai, N.T. Ngoc, L. Vu-Duc, Huong Le‐Thi‐Thu · 2017 · SAR and QSAR in environmental research
19. Predictive in silico models for aquatic toxicity of cosmetic and personal care additive mixtures
    Yuting Yang, Hong‐Gang Ni · 2023 · Water Research
20. Filtered circular fingerprints improve either prediction or runtime performance while retaining interpretability
    Martin Gütlein, Stefan Krämer · 2016 · Journal of Cheminformatics
21. A Systematic Review of Computational Drug Discovery, Development, and Repurposing for Ebola Virus Disease Treatment
    James Schuler, Matthew L. Hudson, Diane G. Schwartz, Ram Samudrala · 2017 · Molecules
22. Assessment and Reproducibility of Quantitative Structure–Activity Relationship Models by the Nonexpert
    Mukesh L. Patel, Martyn L. Chilton, Andrea Sartini, Laura Gibson, Chris Barber, Liz Covey-Crump, Katarzyna R. Przybylak, M Cronin, Judith Clare Madden · 2018 · Journal of Chemical Information and Modeling
23. Development of an allosteric inhibitor class blocking RNA elongation by the respiratory syncytial virus polymerase complex
    Robert M. Cox, Märt Toots, Jeong-Joong Yoon, Julien Sourimant, Barbara I. Ludeke, Rachel Fearns, Élyse Bourque, Joseph M. Patti, Edward Wolfgang Lee, John H. Vernachio et al. · 2018 · Journal of Biological Chemistry
24. Reverse chemical ecology in a moth: machine learning on odorant receptors identifies new behaviorally active agonists
    Gabriela Caballero-Vidal, Cédric Bouysset, Jérémy Gévar, Hayat Mbouzid, Céline Nara, Julie Delaroche, Jérôme Golebiowski, Nicolas Montagné, Sébastien Fiorucci, Emmanuelle Jacquin‐Joly · 2021 · Cellular and Molecular Life Sciences
25. Diversity and Chemical Library Networks of Large Data Sets
    Timothy B. Dunn, Gustavo de Miranda Seabra, Taewon David Kim, K. Eurídice Juárez‐Mercado, Chenglong Li, José L. Medina‐Franco, Ramón Alain Miranda‐Quintana · 2021 · Journal of Chemical Information and Modeling
26. Deep Neural Networks for QSAR
    Yuting Xu · 2021 · Methods in molecular biology
27. Pharmacophore modeling and its applications
    Rashmi Tyagi, Amisha Singh, Kamal Kumar Chaudhary, Manoj Kumar Yadav · 2021 · Elsevier eBooks
28. Rethinking the applicability domain analysis in QSAR models
    José R. Mora, Edgar Márquez, Noel Pérez, Ernesto Contreras‐Torres, Yunierkis Pérez‐Castillo, Guillermı́n Agüero-Chapin, Félix Martínez-Rios, Yovani Marrero‐Ponce, Stephen J. Barigye · 2024 · Journal of Computer-Aided Molecular Design
29. Advancing efficiency in deep-blue OLEDs: Exploring a machine learning–driven multiresonance TADF molecular design
    Hyung Suk Kim, Hyung Jin Cheon, Sang Hoon Lee, Junho Kim, Seunghyup Yoo, Yun‐Hi Kim, Chihaya Adachi · 2025 · Science Advances
30. Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches
    Tanya Liyaqat, Tanvir Ahmad, Chandni Saxena · 2025 · Archives of Computational Methods in Engineering
31. Dempster-Shafer theory for combining in silico evidence and estimating uncertainty in chemical risk assessment
    James F. Rathman, Chihae Yang, Haojin Zhou · 2018 · Computational Toxicology
32. Gains from no real PAINS: Where ‘Fair Trial Strategy’ stands in the development of multi-target ligands
    Jianbo Sun, Hui Zhong, Kun Wang, Na Li, Li Chen · 2021 · Acta Pharmaceutica Sinica B
33. X-ray Crystallography-Guided Design, Antitumor Efficacy, and QSAR Analysis of Metabolically Stable Cyclopenta-Pyrimidinyl Dihydroquinoxalinone as a Potent Tubulin Polymerization Inhibitor
    Souvik Banerjee, Foyez Mahmud, Shanshan Deng, Lingling Ma, Mi‐Kyung Yun, Sayo Olawale Fakayode, Kinsie E. Arnst, Lei Yang, Hao Chen, Zhongzhi Wu et al. · 2021 · Journal of Medicinal Chemistry
34. In Silico Prediction of Steroids and Triterpenoids as Potential Regulators of Lipid Metabolism
    Valery M. Dembitsky · 2021 · Marine Drugs
35. Batched Bayesian Optimization for Drug Design in Noisy Environments
    Hugo Bellamy, Abbi Abdel‐Rehim, Oghenejokpeme I. Orhobor, Ross D. King · 2022 · Journal of Chemical Information and Modeling
36. Structure–response relationship in electrospray ionization-mass spectrometry of sartans by artificial neural networks
    Jelena B. Golubović, Claudia Birkemeyer, Ana D. Protić, Biljana M. Otašević, Mira L. Zečević · 2016 · Journal of Chromatography A
37. In silico identification of milk antihypertensive di- and tripeptides involved in angiotensin I–converting enzyme inhibitory activity
    Vladimir Vukić, Dajana Vukić, Spasenija Milanović, Mirela Iličić, Katarina Kanurić, Mark S. Johnson · 2017 · Nutrition Research
38. The Convergence of Artificial Intelligence and Chemistry for Improved Drug Discovery
    Clive P. Green, Ola Engkvist, Garry Pairaudeau · 2018 · Future Medicinal Chemistry
39. One Drug for Multiple Targets: A Computational Perspective
    Oscar Méndez‐Lucio, J. Jesús Naveja, Hugo Vite‐Caritino, Fernando Daniel Prieto-Martínez, José L. Medina‐Franco · 2017 · Journal of the Mexican Chemical Society
40. A Concise Review on the Significance of QSAR in Drug Design
    Hiteshi Tandon, Tanmoy Chakraborty, Vandana Suhag · 2019 · Chemical and Biomolecular Engineering
41. Predicting Chemical Properties using Self-Attention Multi-task Learning based on SMILES Representation
    Sangrak Lim, Yong Oh Lee · 2021 · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition
42. Comprehensive Study on Molecular Supervised Learning with Graph Neural Networks
    Doyeong Hwang, Soojung Yang, Yongchan Kwon, Kyung Hoon Lee, Grace Lee, Hanseok Jo, Seyeol Yoon, Seongok Ryu · 2020 · Journal of Chemical Information and Modeling
43. Application of Quantitative Structure–Property Relationship Predictive Models to Water Treatment: A Critical Review
    Dion Awfa, Mohamed Ateia, David Mendoza, Chihiro Yoshimura · 2021 · ACS ES&T Water
44. Representative feature selection of molecular descriptors in QSAR modeling
    Jingshan Li, Dehan Luo, Tengteng Wen, Qi Liu, Zhuofeng Mo · 2021 · Journal of Molecular Structure
45. Molecular Descriptors
    Andrea Mauri, Viviana Consonni, Roberto Todeschini · 2016 · Springer eBooks
46. Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches
    Jelica Vučićević, Katarina M. Nikolic, John B. O. Mitchell · 2019 · Current Medicinal Chemistry
47. Application of machine learning in chemical engineering: outlook and perspectives
    Ashraf Al Sharah, Hamza Abu Owida, Feras Alnaimat, Suhaila Farhan Ahmad Abuowaida · 2023 · IAES International Journal of Artificial Intelligence
48. Aminopyrimidine derivatives as protein kinases inhibitors. Molecular design, synthesis, and biologic activity
    Elena V. Koroleva, Zh. I. Ignatovich, Yu. V. Sinyutich, К. Н. Гусак · 2016 · Russian Journal of Organic Chemistry
49. QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines
    Vijay Hariram Masand, Nahed N. E. El-Sayed, Devidas T. Mahajan, Andrew G. Mercader, Ahmed Mahmoud Alafeefy, Indira G. Shibi · 2016 · Journal of Molecular Structure
50. Design, Synthesis, Structure–Activity Relationship Studies, and Three-Dimensional Quantitative Structure–Activity Relationship (3D-QSAR) Modeling of a Series of O-Biphenyl Carbamates as Dual Modulators of Dopamine D3 Receptor and Fatty Acid Amide Hydrolase
    Alessio De Simone, Debora Russo, G.F. Ruda, Alessandra Micoli, Mariarosaria Ferraro, Rita Maria Concetta Di Martino, Giuliana Ottonello, Maria Summa, Andrea Armirotti, Tiziano Bandiera et al. · 2017 · Journal of Medicinal Chemistry
51. In-silico prediction of sweetness using structure-activity relationship models
    Anukrati Goel, Kishore Gajula, Rakesh Kumar Gupta, Beena Rai · 2018 · Food Chemistry
52. Challenges with multi-objective QSAR in drug discovery
    George Lambrinidis, Anna Tsantili‐Kakoulidou · 2018 · Expert Opinion on Drug Discovery
53. Ecotoxicological Modeling, Ranking and Prioritization of Pharmaceuticals Using QSTR and i‐QSTTR Approaches: Application of 2D and Fragment Based Descriptors
    Kabiruddin Khan, Supratik Kar, Hans Sanderson, Kunal Roy, Jerzy Leszczyński · 2018 · Molecular Informatics
54. Selecting Adsorbents to Separate Diverse Near-Azeotropic Chemicals
    Farhad Gharagheizi, Dai Tang, David S. Sholl · 2020 · The Journal of Physical Chemistry C
55. Integrated approaches to testing and assessment as a tool for the hazard assessment and risk characterization of cosmetic preservatives
    Andrezza Di Pietro Micali Canavez, Gabriela de Oliveira Prado Corrêa, Vera Lúcia Borges Isaac, Desirée Cigaran Schuck, Márcio Lorencini · 2021 · Journal of Applied Toxicology
56. The History and Development of Quantitative Structure-Activity Relationships (QSARs)
    John C. Dearden · 2016 · IGI Global eBooks
57. Systematic Artifacts in Support Vector Regression-Based Compound Potency Prediction Revealed by Statistical and Activity Landscape Analysis
    Jenny Balfer, Jürgen Bajorath · 2015 · PLoS ONE
58. In silico Prediction of Aqueous Solubility: a Comparative Study of Local and Global Predictive Models
    Oleg A. Raevsky, Daniel E. Polianczyk, Veniamin Y. Grigorev, Olga E. Raevskaja, John C. Dearden · 2015 · Molecular Informatics
59. PlayMolecule Glimpse: Understanding Protein–Ligand Property Predictions with Interpretable Neural Networks
    Alejandro Varela‐Rial, Iain Maryanow, Maciej Majewski, Stefan Doerr, Nikolai Schapin, José Jiménez-Luna, Gianni De Fabritiis · 2022 · Journal of Chemical Information and Modeling
60. AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening
    Boyang Ni, Haoying Wang, Huda Khalaf, Vincent Blay, Douglas R. Houston · 2024 · Journal of Chemical Information and Modeling
61. QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool
    Helle W. van den Maagdenberg, Martin Šícho, David Alencar Araripe, Sohvi Luukkonen, Linde Schoenmaker, Michiel Jespers, Olivier J. M. Béquignon, Marina Gorostiola González, Remco L. van den Broek, Andrius Bernatavicius et al. · 2024 · Journal of Cheminformatics
62. Sustainable Practices in Medicinal Chemistry Part 2: Green by Design
    Ignacio Aliagas, Raphaëlle Berger, Kristin Goldberg, Rachel T. Nishimura, John Reilly, Paul Richardson, Daniel Richter, Edward C. Sherer, Brian A. Sparling, Marian C. Bryan · 2017 · Journal of Medicinal Chemistry
63. Towards the Revival of Interpretable QSAR Models
    Watshara Shoombuatong, Philip Prathipati, Wiwat Owasirikul, Apilak Worachartcheewan, Saw Simeon, Nuttapat Anuwongcharoen, Jarl E. S. Wikberg, Chanin Nantasenamat · 2017 · Challenges and advances in computational chemistry and physics
64. Identification of Novel Aurora Kinase A (AURKA) Inhibitors via Hierarchical Ligand-Based Virtual Screening
    Yue Kong, Andreas Bender, Aixia Yan · 2017 · Journal of Chemical Information and Modeling
65. Prediction of Effective Drug Combinations by an Improved Naïve Bayesian Algorithm
    Li-Yue Bai, Hao Dai, Qin Xu, Muhammad Junaid, Shaoliang Peng, Xiaolei Zhu, Yi Xiong, Dong‐Qing Wei · 2018 · International Journal of Molecular Sciences
66. Diverse Classes of HDAC8 Inhibitors: In Search of Molecular Fingerprints that Regulate Activity
    Sk. Abdul Amin, Nilanjan Adhikari, Tarun Jha · 2018 · Future Medicinal Chemistry
67. Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors
    Vijay Hariram Masand, Nahed N. E. El-Sayed, Mukesh U. Bambole, Vaijant R. Patil, Sumer D. Thakur · 2018 · Journal of Molecular Structure
68. Quantitative-Structure-Activity-Relationship (QSAR) models for the reaction rate and temperature of nitrogenous organic compounds in supercritical water oxidation (SCWO)
    Zhiwen Cheng, Bowen Yang, Qincheng Chen, Xiaoping Gao, Yujia Tan, Tao Yuan, Zhemin Shen · 2018 · Chemical Engineering Journal
69. 4D- quantitative structure–activity relationship modeling: making a comeback
    Denis Fourches, Jeremy R. Ash · 2019 · Expert Opinion on Drug Discovery
70. Applicability Domains Enhance Application of PPARγ Agonist Classifiers Trained by Drug-like Compounds to Environmental Chemicals
    Zhongyu Wang, Jingwen Chen, Huixiao Hong · 2020 · Chemical Research in Toxicology
71. BNNmix: A new approach for predicting the mixture toxicity of multiple components based on the back-propagation neural network
    Zejun Wang, Shu‐Shen Liu, Li Feng, Ya-Qian Xu · 2020 · The Science of The Total Environment
72. Theoretical Insights into the Anti-SARS-CoV‑2 Activity of Chloroquine and Its Analogs and In Silico Screening of Main Protease Inhibitors
    A. S. Achutha, V.L. Pushpa, Suchitra Surendran · 2020 · Journal of Proteome Research
73. High-Throughput Chemical Screening and Structure-Based Models to Predict hERG Inhibition
    Shagun Krishna, Alexandre Borrel, Ruili Huang, Jinghua Zhao, Menghang Xia, Nicole Kleinstreuer · 2022 · Biology
74. k‐Nearest neighbors optimization‐based outlier removal
    Abraham Yosipof, Hanoch Senderowitz · 2014 · Journal of Computational Chemistry
75. Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening
    Shardul Paricharak, Oscar Méndez‐Lucio, Aakash Chavan Ravindranath, Andreas Bender, Adriaan P. IJzerman, Gerard J. P. van Westen · 2016 · Briefings in Bioinformatics
76. Going All In: A Strategic Investment in In Silico Toxicology
    Jakub Kostal, Adelina M. Voutchkova‐Kostal · 2020 · Chemical Research in Toxicology
77. MolPredictX: Online Biological Activity Predictions by Machine Learning Models
    Marcus Tullius Scotti, Chonny Herrera‐Acevedo, Renata Priscila Barros de Menezes, Holli‐Joi Martin, Eugene N. Muratov, Ávilla Ítalo de Souza Silva, Emmanuella Faustino Albuquerque, Lucas Ferreira Calado, Ericsson David Coy-Barrera, Luciana Scotti · 2022 · Molecular Informatics
78. A Systems Toxicology Approach for the Prediction of Kidney Toxicity and Its Mechanisms In Vitro
    Susanne Ramm, Petar V. Todorov, Vidya Chandrasekaran, Anders B. Dohlman, Maria Beatriz Monteiro, Mira Pavkovic, Jeremy L. Muhlich, Harish Shankaran, William W. Chen, Jerome Thomas Mettetal et al. · 2019 · Toxicological Sciences
79. Effect of hydrophobic and hydrogen bonding interactions on the potency of ß‐alanine analogs of G‐protein coupled glucagon receptor inhibitors
    Pushyaraga P. Venugopal, Bratin Kumar Das, E. Soorya, Debashree Chakraborty · 2019 · Proteins Structure Function and Bioinformatics
80. QSAR modeling, docking, ADME and reactivity of indazole derivatives as antagonizes of estrogen receptor alpha (ER-α) positive in breast cancer
    Afaf Zekri, Dalal Harkati, Samir Kenouche, Basil A. Saleh · 2020 · Journal of Molecular Structure
81. Quantitative structure-toxicity relationship model for acute toxicity of organophosphates via multiple administration routes in rats and mice
    Liang-Liang Wang, Junjie Ding, Pan Li, Li Fu, Jia-Hao Tian, Dongsheng Cao, Hui Jiang, Xiaoqin Ding · 2020 · Journal of Hazardous Materials
82. Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence
    José Teófilo Moreira‐Filho, Arthur C. Silva, Rafael Ferreira Dantas, Bárbara Figueira Gomes, Lauro Ribeiro Souza Neto, J. Brandão-Neto, Raymond J. Owens, Nicholas Furnham, Bruno J. Neves, Floriano Paes Silva et al. · 2021 · Frontiers in Immunology
83. Identification of potent aldose reductase inhibitors as antidiabetic (Anti-hyperglycemic) agents using QSAR based virtual Screening, molecular Docking, MD simulation and MMGBSA approaches
    Ravindra L. Bakal, Rahul D. Jawarkar, Jagdish V. Manwar, Minal S. Jaiswal, Arabinda Ghosh, Ajaykumar Gandhi, Magdi E. A. Zaki, Sami A. Al‐Hussain, Abdul Samad, Vijay Hariram Masand et al. · 2022 · Saudi Pharmaceutical Journal
84. Principles and procedures for assessment of acute toxicity incorporating in silico methods
    Craig M. Zwickl, Jessica C. Graham, Robert A. Jolly, Arianna Bassan, Ernst Ahlberg, Alexander Amberg, Lennart T. Anger, Lisa D. Beilke, Phillip Bellion, Alessandro Brigo et al. · 2022 · Computational Toxicology
85. Quantum-Mechanical Approach to Predicting the Carcinogenic Potency of N -Nitroso Impurities in Pharmaceuticals
    Jakub Kostal, Adelina M. Voutchkova‐Kostal · 2023 · Chemical Research in Toxicology
86. QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes
    Nikita Basant, Shikha Gupta, Kunwar Pal Singh · 2016 · Toxicology Research
87. Distinguishing between expert and statistical systems for application under ICH M7
    Chris Barber, Thierry Hanser, Philip Neville Judson, Richard Dafydd Williams · 2017 · Regulatory Toxicology and Pharmacology
88. RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells
    Omer Kaspi, Abraham Yosipof, Hanoch Senderowitz · 2017 · Journal of Cheminformatics
89. Curated mode-of-action data and effect concentrations for chemicals relevant for the aquatic environment
    Lena Kramer, Tobias Schulze, Nils Klüver, Rolf Altenburger, Jörg Hackermüller, Martin Krayer Von Krauss, Wibke Busch · 2024 · Scientific Data
90. Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation
    Yue Kong, Xiaoman Zhao, Ruizi Liu, Zhenwu Yang, Hongyan Yin, Bowen Zhao, J. Wang, Bingjie Qin, Aixia Yan · 2022 · Journal of Cheminformatics
91. Engineering the microenvironment: advanced biomaterials for humanized in vitro immunotoxicology and carcinogenicity assessment
    Sunil Choudhary, Abhishek Dubey, Anshumaan Singh, Paolo Zamboni, Neha Gupta, Royana Singh, Veronica Tisato, Lalit Mohan Aggarwal, Donato Gemmati · 2025 · Exploration of BioMat-X
92. Monte Carlo-based QSAR modeling of dimeric pyridinium compounds and drug design of new potent acetylcholine esterase inhibitors for potential therapy of myasthenia gravis
    Dusan T. Sokolovic, Veroljub Stanković, Dragan Toskić, Ljubiša M. Lilić, Goran Ž. Ranković, Jasmina Ranković, Gorana Nedin-Ranković, Aleksandar M. Veselinović · 2016 · Structural Chemistry
93. Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance
    Eugene N. Muratov, Margaret Lewis, Denis Fourches, Alexander Tropsha, Wendy C. Cox · 2017 · American Journal of Pharmaceutical Education
94. Atom Pair Contribution Method: Fast and General Procedure To Predict Molecular Formation Enthalpies
    Didier Mathieu · 2017 · Journal of Chemical Information and Modeling
95. QSAR modelling: a therapeutic patent review 2010-present
    Amit Kumar Halder, Ana S. Moura, M. Natália D. S. Cordeiro · 2018 · Expert Opinion on Therapeutic Patents
96. The integration of pharmacophore-based 3D QSAR modeling and virtual screening in safety profiling: A case study to identify antagonistic activities against adenosine receptor, A2A, using 1,897 known drugs
    Fan Fan, Dora Toledo Warshaviak, Hisham K. Hamadeh, Robert T. Dunn · 2019 · PLoS ONE
97. Discriminant models on mitochondrial toxicity improved by consensus modeling and resolving imbalance in training
    Weihao Tang, Jingwen Chen, Huixiao Hong · 2020 · Chemosphere
98. Rapid Prediction of Chemical Ecotoxicity Through Genetic Algorithm Optimized Neural Network Models
    Ping Hou, Bu Zhao, Olivier Jolliet, Ji Zhu, Peng Wang, Ming Xu · 2020 · ACS Sustainable Chemistry & Engineering
99. Molecular Modeling of µ Opioid Receptor Ligands with Various Functional Properties: PZM21, SR-17018, Morphine, and Fentanyl—Simulated Interaction Patterns Confronted with Experimental Data
    Sabina Podlewska, Ryszard Bugno, Lucja Kudla, Andrzej Jacek Bojarski, Ryszard Przewłocki · 2020 · Molecules
100. QSAR-Based Virtual Screening of Natural Products Database for Identification of Potent Antimalarial Hits
     Letícia Tiburcio Ferreira, Joyce Villa Verde Bastos Borba, José Teófilo Moreira‐Filho, Aline Rimoldi, Carolina Horta Andrade, Fábio Trindade Maranhão Costa · 2021 · Biomolecules