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

Cited by (1,001)

Items 801–900 of 1,001

1. Advances in computer-aided drug design: From molecular docking to artificial intelligence-driven therapeutic discovery
   Asmaa Mahmoud Fahim · 2026 · ASPET Discovery
2. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy
   Olga A. Tarasova, Vladimir Poroikov · 2021 · Current Medicinal Chemistry
3. A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery
   Anjana Vidya Srivathsa, Nandini Markuli Sadashivappa, Apeksha Krishnamurthy Hegde, Srimathi Radha, Agasa Ramu Mahesh, Damodar Nayak Ammunje, Debanjan Sen, Panneerselvam Theivendren, G. Saravanan, Selvaraj Kunjiappan et al. · 2023 · Current Pharmaceutical Design
4. Pretrained transformer models for predicting the withdrawal of drugs from the market
   Eyal Mazuz, Guy Shtar, Nir Kutsky, Lior Rokach, Bracha Shapira · 2023 · Bioinformatics
5. Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review
   Baoyu He, Jingjing Guo, Henry H. Y. Tong, Wai Ming To · 2024 · Mini-Reviews in Medicinal Chemistry
6. In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches
   Yiqing Zhou, Ze Wang, Zejun Huang, Weihua Li, Yuanting Chen, Xinxin Yu, Yun Tang, Guixia Liu · 2024 · Journal of Applied Toxicology
7. Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling
   Ruibo Zhang, Daniel Nolte, Cesar Sanchez-Villalobos, Souparno Ghosh, Ranadip Pal · 2024 · Nature Communications
8. PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization
   Silong Zhai, Yahong Tan, Cheng Zhu, Chengyun Zhang, Yan Gao, Qingyi Mao, Youming Zhang, Hongliang Duan, Yizhen Yin · 2024 · European Journal of Medicinal Chemistry
9. Multi‐Task ADME/PK prediction at industrial scale: leveraging large and diverse experimental datasets**
   Moritz Walter, Jens Markus Borghardt, Lina Humbeck, Miha Škalič · 2024 · Molecular Informatics
10. Hybrid QSAR modeling of LD50 in organophosphorus nerve agents: a mechanistic approach using DFT and molecular docking
    Youngchan Jang, Jeongyun Kim, Doo‐Hee Lee, Jin San Yoo, Jeongwan Park, Ku Kang · 2025 · Computational Toxicology
11. A D3R prospective evaluation of machine learning for protein-ligand scoring
    Jocelyn Sunseri, Matthew Ragoza, Jasmine Collins, David Ryan Koes · 2016 · Journal of Computer-Aided Molecular Design
12. Computer-Aided Drug Discovery
    Pinyi Lu, David R. Bevan, Andrew J. Leber, Raquel Hontecillas, Nuria Tubau‐Juni, Josep Bassaganya-Riera · 2018 · Springer eBooks
13. Redesigning hazardous chemicals by learning from structure-based drug discovery
    Traci Clymer, Vanessa Vargas, Eric J. Corcoran, Robin Kleinberg, Jakub Kostal · 2019 · Green Chemistry
14. Predicting the Electrochemical Properties of Lithium-Ion Battery Electrode Materials with the Quantum Neural Network Algorithm
    Hwanho Choi, Kee‐Sun Sohn, Myoungho Pyo, Kee‐Choo Chung, Hwangseo Park · 2019 · The Journal of Physical Chemistry C
15. Computational modelling of mechanical properties for new polymeric materials with high molecular weight
    Fiorella Cravero, María Jimena Martínez, Ignacio Ponzoni, Mónica F. Díaz · 2019 · Chemometrics and Intelligent Laboratory Systems
16. Nonanimal toxicology testing approaches for traditional and deemed tobacco products in a complex regulatory environment: Limitations, possibilities, and future directions
    Dana Lauterstein, Matthew Savidge, Yanling Chen, Roxana E. Weil, R Philip Yeager · 2019 · Toxicology in Vitro
17. Systematic Activity Maturation of a Single-Domain Antibody with Non-canonical Amino Acids through Chemical Mutagenesis
    Philip R. Lindstedt, Francesco A. Aprile, Pietro Sormanni, Robertinah Rakoto, Christopher Martin Dobson, Gonçalo J. L. Bernardes, Michele Vendruscolo · 2020 · Cell chemical biology
18. Trends in application of advancing computational approaches in GPCR ligand discovery
    Siyu Zhu, Meixian Wu, Ziwei Huang, Jing An · 2021 · Experimental Biology and Medicine
19. Identification of Novel HBV/HDV Entry Inhibitors by Pharmacophore- and QSAR-Guided Virtual Screening
    Michael Kirstgen, Simon Franz Müller, Kira Alessandra Alicia Theresa Lowjaga, Nora Goldmann, Felix Lehmann, Sami Alakurtti, Jari T. Yli‐Kauhaluoma, Karl H. Baringhaus, Reimar Krieg, Dieter Glebe et al. · 2021 · Viruses
20. Target-Specific Native/Decoy Pose Classifier Improves the Accuracy of Ligand Ranking in the CSAR 2013 Benchmark
    Denis Fourches, Regina Politi, Alexander Tropsha · 2014 · Journal of Chemical Information and Modeling
21. Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome
    Yen Low, Ola Caster, Tomas Bergvall, Denis Fourches, Xiaoling Zang, G. Niklas Norén, Ivan Rusyn, Ralph Edwards, Alexander Tropsha · 2015 · Journal of the American Medical Informatics Association
22. Shallow Representation Learning via Kernel PCA Improves QSAR Modelability
    Stefano Rensi, Russ Biagio Altman · 2017 · Journal of Chemical Information and Modeling
23. Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity
    Stephen J. Barigye, Matheus P. Freitas, Priscila Ausina, Patrícia Zancan, Mauro Sola‐Penna, Juan A. Castillo‐Garit · 2018 · ACS Combinatorial Science
24. Precise modelling and interpretation of bioactivities of ligands targeting G protein-coupled receptors
    Jiansheng Wu, Ben Liu, Wallace K. B. Chan, Weijian Wu, Tao Pang, Haifeng Hu, Shancheng Yan, Xiaoyan Ke, Yang Zhang · 2019 · Bioinformatics
25. Cinnamic Derivatives as Antitubercular Agents: Characterization by Quantitative Structure–Activity Relationship Studies
    Cátia A. S. Teixeira, Cristina M. Ventura, José R. B. Gomes, Paula A. C. Gomes, Filomena E. L. Martins · 2020 · Molecules
26. Bioinformatics models in drug delivery: Predicting biomaterial-biological interactions for targeted therapies
    Varshika Singh, Sukrat Sinha, Jaya Verma · 2025 · Next Nanotechnology
27. Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market
    Kerstin J.F. von Borries, Katie V. Beckwith, Jonathan M. Goodman, Weihsueh A. Chiu, Olivier Jolliet, Peter Fantke · 2026 · Nature Communications
28. Machine learning assisted QSAR analysis to predict protein adsorption capacities on mixed-mode resins
    Qing-Yun Cai, Liangzhi Qiao, Shan‐Jing Yao, Dong‐Qiang Lin · 2024 · Separation and Purification Technology
29. Concurrent Optimizations of Efficacy and Blood–Brain Barrier Permeability in New Macrocyclic LRRK2 Inhibitors for Potential Parkinson’s Disease Therapeutics
    Kewon Kim, Ahyoung Jang, Ho‐Chul Shin, Inhae Ye, Ji‐Eun Lee, Taeho Kim, Hwangseo Park, Sungwoo Hong · 2024 · Journal of Medicinal Chemistry
30. FGTN: Fragment-based graph transformer network for predicting reproductive toxicity
    Jia-Nan Ren, Qiang Chen, Hong-Yu-Xiang Ye, Cheng Cao, Ya-Min Guo, Jinrong Yang, Hao Wang, Muhammad Zafar Irshad Khan, Jian‐Zhong Chen · 2024 · Archives of Toxicology
31. In Silico Study of In Vitro GPCR Assays by QSAR Modeling
    Kamel Mansouri, Richard Judson · 2016 · Methods in molecular biology
32. Preselection of A- and B- modified d-homo lactone and d-seco androstane derivatives as potent compounds with antiproliferative activity against breast and prostate cancer cells – QSAR approach and molecular docking analysis
    Strahinja Z. Kovačević, Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Vladimir Vukić, Marina P. Savić, Evgenija A. Djurendić · 2016 · European Journal of Pharmaceutical Sciences
33. Computational Predictions for Multi-Target Drug Design
    Neelima Gupta, Prateek Pandya, Seema Verma · 2018 · Methods in pharmacology and toxicology
34. Food ingredient safety evaluation: Utility and relevance of toxicokinetic methods
    Shruti V. Kabadi, Yu Zang, Jeffrey W. Fisher, Nikki Smith, Catherine Whiteside, Jason L. Aungst · 2019 · Toxicology and Applied Pharmacology
35. Artificial Intelligence-Based Drug Design and Discovery
    Yu‐Chen Lo, Gui Ren, Hiroshi Honda, Kara Lynn Davis · 2019 · IntechOpen eBooks
36. In Silico Prediction of Critical Micelle Concentration (CMC) of Classic and Extended Anionic Surfactants from Their Molecular Structural Descriptors
    Soufiane Rahal, Noureddine Hadidi, Mabrouk Hamadache · 2020 · Arabian Journal for Science and Engineering
37. Predictive Modeling of Critical Temperatures in Superconducting Materials
    Natalia Sizochenko, Markus Hofmann · 2020 · Molecules
38. Exploring in vitro to in vivo extrapolation for exposure and health impacts of e-cigarette flavor mixtures
    Xiaoqing Chang, Jaleh Abedini, Shannon M. Bell, K. Monica Lee · 2021 · Toxicology in Vitro
39. Insight into the structural requirement of aryl sulphonamide based gelatinases (MMP-2 and MMP-9) inhibitors – Part I: 2D-QSAR, 3D-QSAR topomer CoMFA and Naïve Bayes studies – First report of 3D-QSAR Topomer CoMFA analysis for MMP-9 inhibitors and jointly inhibitors of gelatinases together
    Sanjib Das, Sk. Abdul Amin, Tarun Jha · 2021 · SAR and QSAR in environmental research
40. Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata
    Vijay Hariram Masand, Magdi E. A. Zaki, Sami A. Al‐Hussain, Anis Ben Ghorbal, Siddhartha Akasapu, Israa Lewaa, Arabinda Ghosh, Rahul D. Jawarkar · 2021 · Aquatic Toxicology
41. Unsupervised Machine Learning Neural Gas Algorithm for Accurate Evaluations of the Hessian Matrix in Molecular Dynamics
    Michele Gandolfi, Michele Ceotto · 2021 · Journal of Chemical Theory and Computation
42. Using computers to ESKAPE the antibiotic resistance crisis
    Thiago H. da Silva, Tim Hachigian, Jeunghoon Lee, Matthew D. King · 2021 · Drug Discovery Today
43. Application of artificial neural networks to the prediction of antifungal activity of imidazole derivatives against Candida albicans
    Anna Badura, Jerzy Krysiński, Alicja Nowaczyk, Adam Buciński · 2022 · Chemometrics and Intelligent Laboratory Systems
44. In-silico and in-vitro studies on the efficacy of mangiferin against colorectal cancer
    Rohini Samadarsi, Linus Augustin, Chandan Kumar, Debjani Dutta · 2022 · BMC Chemistry
45. Viral informatics: bioinformatics-based solution for managing viral infections
    Sanjay Kumar, Geethu S Kumar, Subhrangsu Sundar Maitra, Petr Malý, Shiv Bharadwaj, Pradeep Sharma, Vivek Dhar Dwivedi · 2022 · Briefings in Bioinformatics
46. O data, where art thou? Revolutionizing data sharing to advance our sustainability goals through smart chemical innovation
    Jakub Kostal, Bryan W. Brooks, Christopher A. Smith, Geetesh Devineni · 2022 · iScience
47. Modeling structure–activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics
    Rameez Hassan Pirzada, Sheikh Bilal Ahmad, Naila Qayyum, Sangdun Choi · 2023 · Frontiers in Endocrinology
48. 2D‐Quantitative structure–activity relationship modeling for risk assessment of pharmacotherapy applied during pregnancy
    Gül Karaduman, Feyza Kelleci̇ Çeli̇k · 2023 · Journal of Applied Toxicology
49. Translation of off-target effects: prediction of ADRs by integrated experimental and computational approach
    László Urbán, Mateusz Maciejewski, Eugen Lounkine, Steven Whitebread, Jeremy L. Jenkins, Jacques Hamon, Alexander Fekete, Patrick Y. Muller · 2014 · Toxicology Research
50. Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action
    George Papamokos, Ilona Silins · 2016 · Frontiers in Pharmacology
51. Ligand-based computer aided drug design reveals new tropomycin receptor kinase a (TrkA) inhibitors
    Rand O. Shahin, Iman A. Mansi, Lubna Swellmeen, Tahani Alwidyan, Nabil N. AL‐Hashimi, Yaser Al-Qarar’h, Omar Shaheen · 2018 · Journal of Molecular Graphics and Modelling
52. Discovery and design of new PI3K inhibitors through pharmacophore-based virtual screening, molecular docking, and binding free energy analysis
    Saikiran Reddy Peddi, Sree Kanth Sivan, Vijjulatha Manga · 2018 · Structural Chemistry
53. In-silico driven design and development of spirobenzimidazo-quinazolines as potential DNA gyrase inhibitors
    Korrapati Suresh Babu, Yedla Poornachandra, Girinath G. Pillai, Faruq Mohammad, Venkata Ramana Reddy Ch., Pranav Bhamidipati, Ramars Amanchy, Riyaz Syed, Ähmed Kamal · 2020 · Biomedicine & Pharmacotherapy
54. Hologram Quantitative Structure–Activity Relationship and Comparative Molecular Interaction Field Analysis of Aminothiazole and Thiazolesulfonamide as Reversible LSD1 Inhibitors
    Vinícius Gonçalves Maltarollo, Káthia M. Honório, Flávio da Silva Emery, Arasu Ganesan, Gustavo Henrique Goulart Trossini · 2015 · Future Medicinal Chemistry
55. Reactants, Products, and Transition States of Elementary Chemical Reactions Based on Quantum Chemistry
    Colin A. Grambow, Lagnajit Pattanaik, William H. Green · 2019 · ChemRxiv
56. Synthesis of New Pyrazolo Pyrimidine Disazo Dyes, Theoretical and Experimental Comparison of their Characteristic Properties and in silico Prediction of their Possible Antibacterial Activity
    Alpaslan Bayrakdar, G.F. Nivetha, Aykut Demirçalı, Hazal Kizak, P. Tasli, Fati Yıldırım, Prasath Manivannan, Muhammad Ashfaq, Suraj N. Mali, Aseel Smerat · 2025 · Journal of Computational Biophysics and Chemistry
57. Relating Caco-2 permeability to molecular properties using block relevance analysis
    T. Potter, Giuseppe Ermondi, G. Newbury, Giulia Caron · 2014 · MedChemComm
58. Efficient identification of novel anti-glioma lead compounds by machine learning models
    Bruno J. Neves, Jonathan Paulo Agnes, Marcelo do Nascimento Gomes, Marcio Roberto Henriques Donza, Rosângela Mayer Gonçalves, Marina Delgobo, Lauro Ribeiro de Souza Neto, Mário Roberto Senger, Floriano Paes Silva, Sabrina Baptista Ferreira et al. · 2019 · European Journal of Medicinal Chemistry
59. Molecular field analysis for data-driven molecular design in asymmetric catalysis
    Shigeru Yamaguchi · 2022 · Organic & Biomolecular Chemistry
60. The importance of good practices and false hits for QSAR-driven virtual screening real application: a SARS-CoV-2 main protease (Mpro) case study
    Mateus Sá Magalhães Serafim, Simone Queiroz Pantaleão, Elany Barbosa da Silva, James Hobson McKerrow, Anthony John O’Donoghue, Bruno Eduardo Fernandes Mota, Káthia M. Honório, Vinícius Gonçalves Maltarollo · 2023 · Frontiers in Drug Discovery
61. Design of Cr-PNP catalysts for ethylene tri-/tetramerization assisted by a data-driven approach
    Zijuan Luo, Jiale Peng, Yue Mu, Li Sun, Zhihua Zhu, Zhen Liu · 2023 · Journal of Catalysis
62. Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach
    Feyza Kelleci̇ Çeli̇k, Seyyide Doğan, Gül Karaduman · 2024 · Computers in Biology and Medicine
63. Impact of geometry optimization methods on QSAR modelling: A case study for predicting human serum albumin binding affinity
    Serli Önlü, Melek Türker Saçan · 2017 · SAR and QSAR in environmental research
64. Development of a pharmacophore for cruzain using oxadiazoles as virtual molecular probes: quantitative structure–activity relationship studies
    Anacleto Silva de Souza, Marcelo Tavares de Oliveira, Adriano Defini Andricopulo · 2017 · Journal of Computer-Aided Molecular Design
65. Characterizing Chemical Similarity with Vibrational Spectroscopy: New Insights into the Substituent Effects in Monosubstituted Benzenes
    Yunwen Tao, Wenli Zou, Dieter Cremer, Elfi Kraka · 2017 · The Journal of Physical Chemistry A
66. Computational estimation of the acidities of purines and indoles
    Kara L. Geremia, Paul G. Seybold · 2019 · Journal of Molecular Modeling
67. Quinoxalinones Based Aldose Reductase Inhibitors: 2D and 3D‐QSAR Analysis
    Vijay Hariram Masand, Nahed N. E. El-Sayed, Sumer D. Thakur, Nandkishor D. Gawhale, Mithilesh M. Rathore · 2019 · Molecular Informatics
68. Chemometrics for QSAR Modeling
    Roberto Todeschini, Viviana Consonni, Davide Ballabio, Francesca Grisoni · 2019 · Elsevier eBooks
69. Predicting the safety of medicines in pregnancy: A workshop report
    J.M. Clements, R.G. Hawkes, David E. Jones, A. Adjei, Thomas J. G. Chambers, Lorène Simon, Henry E. Stemplewski, Neil J. Berry, S. Price, Munir Pirmohamed et al. · 2020 · Reproductive Toxicology
70. Molecular modelling study on pyrrolo[2,3-b]pyridine derivatives as c-Met kinase inhibitors: a combined approach using molecular docking, 3D-QSAR modelling and molecular dynamics simulation
    Pouria Shirvani, Afshin Fassihi · 2020 · Molecular Simulation
71. Antitumor Profile of Carbon-Bridged Steroids (CBS) and Triterpenoids
    Valery M. Dembitsky, Tatyana A. Gloriozova, Vladimir Poroikov · 2021 · Marine Drugs
72. QSAR Classification Models for Prediction of Hydroxamate Histone Deacetylase Inhibitor Activity against Malaria Parasites
    Eva Hesping, Ming Jang Chua, Marc Pflieger, Yunan Qian, Lilong Dong, Prabhakar Bachu, Ligong Liu, Thomas Kurz, Gillian M. Fisher, Tina S. Skinner‐Adams et al. · 2022 · ACS Infectious Diseases
73. The first report on predictive comparative ligand-based multi-QSAR modeling analysis of 4-pyrimidinone and 2-pyridinone based APJ inhibitors
    Suvankar Banerjee, Sandip Kumar Baidya, Balaram Ghosh, Nilanjan Adhikari, Tarun Jha · 2022 · New Journal of Chemistry
74. QSAR Evaluations to Unravel the Structural Features in Lysine-Specific Histone Demethylase 1A Inhibitors for Novel Anticancer Lead Development Supported by Molecular Docking, MD Simulation and MMGBSA
    Rahul D. Jawarkar, Ravindra L. Bakal, Nobendu Mukherjee, Arabinda Ghosh, Magdi E. A. Zaki, Sami A. Al‐Hussain, Aamal A. Al‐Mutairi, Abdul Samad, Ajaykumar Gandhi, Vijay Hariram Masand · 2022 · Molecules
75. Efficient design of peptide-binding polymers using active learning approaches
    Assima Rakhimbekova, Anton V. Lopukhov, Natalia L. Klyachko, Alexander V. Kabanov, Timur Madzhidov, Alexander Tropsha · 2022 · Journal of Controlled Release
76. In silico modeling revealed phytomolecules derived from Cymbopogon citratus (DC.) leaf extract as promising candidates for malaria therapy
    Ikponmwosa Owen Evbuomwan, Omokolade Oluwaseyi Alejolowo, Tobiloba Christiana Elebiyo, Charles Obiora Nwonuma, Oluwafemi Adeleke Ojo, Evelyn Uwa Edosomwan, J. I. Chikwendu, Nwanneka Victoria Elosiuba, Justina Chimezie Akulue, Festus A. Dogunro et al. · 2023 · Journal of Biomolecular Structure and Dynamics
77. Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists
    Nadim Ferdous, Mahjerin Nasrin Reza, Mohammad Uzzal Hossain, Shahin Mahmud, Suhami Napis, Kamal Chowdhury, A. K. M. Mohiuddin · 2023 · PLoS ONE
78. Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities
    RICHARD D. III CRAMER · 2015 · PLoS ONE
79. Smiles2Monomers: a link between chemical and biological structures for polymers
    Yoann Dufresne, Laurent Noé, Valérie Leclère, Maude Pupin · 2015 · Journal of Cheminformatics
80. Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties
    Hwanho Choi, Hongsuk Kang, Kee‐Choo Chung, Hwangseo Park · 2019 · Physical Chemistry Chemical Physics
81. Towards quantitative read across: Prediction of Ames mutagenicity in a large database
    Romualdo Benigni · 2019 · Regulatory Toxicology and Pharmacology
82. Automatically Updating Predictive Modeling Workflows Support Decision-Making in Drug Design
    Ingo Muegge, Jörg Bentzien, Prasenjit K. Mukherjee, Robert O Hughes · 2016 · Future Medicinal Chemistry
83. Construction of a Quantitative Structure Activity Relationship (QSAR) Model to Predict the Absorption of Cephalosporins in Zebrafish for Toxicity Study
    Ying Liu, Xia Zhang, Jingpu Zhang, HU Chang-qin · 2019 · Frontiers in Pharmacology
84. Computational Estimation of the Gas-Phase and Aqueous Acidities of Carbon Acids
    Corey A. Baldasare, Paul G. Seybold · 2020 · The Journal of Physical Chemistry A
85. Predictive toxicology today: the transition from biological knowledge to practicable models
    Romualdo Benigni · 2016 · Expert Opinion on Drug Metabolism & Toxicology
86. NanoToxRadar: A Multitarget Nano-QSAR Model for Predicting the Cytotoxicity Values of Multicomponent Nanoparticles
    Jaehyeon Park, Shahzad Rashid, Helena Copsey, Lang Tran, Alex Zabeo, Danail Rumenov Hristozov, Georgios P. Gakis, Costas A. Charitidis, Seokjoo Yoon, Hyun Kil Shin · 2025 · ACS Nanoscience Au
87. Comprehensive Assessment of 2-(3-Methoxyphenylamino)-2-Oxoethyl Methacrylate: Spectroscopic, Computational, Toxicological, Molecular Docking and Dynamic Studies with STAT3 Protein
    Nevin Çankaya, Hanifi Kebiroglu, Serap Yalçın Azarkan · 2025 · Brazilian Journal of Physics
88. Steroidal Compounds at the Crossroads of Inflammation and Cancer: Implications for Drug Discovery and Therapy
    Valery M. Dembitsky, Alexander Olegovich Terent'ev · 2026 · Biomedicines
89. A data-based exploration of the adverse outcome pathway for skin sensitization points to the necessary requirements for its prediction with alternative methods
    Romualdo Benigni, Cecilia Bossa, Olga Tcheremenskaia · 2016 · Regulatory Toxicology and Pharmacology
90. A comparative analysis of the ensemble methods for drug design
    Давронов Рифкат Раxимович, Fatima Аdilova · 2021 · AIP conference proceedings
91. Artificial intelligence-guided Approach for Efficient Virtual Screening of Hits Against Schistosoma Mansoni
    José Teófilo Moreira‐Filho, Bruno J. Neves, Rayssa A. Cajás, Josué de Moraes, Carolina Horta Andrade · 2023 · Future Medicinal Chemistry
92. Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners
    Hezha O. Rasul, Dlzar Dlshad Ghafoor, Bakhtyar K. Aziz, Bryar A. Hassan, Tarik Ahmed Rashid, Arif Kıvrak · 2024 · Applied Biochemistry and Biotechnology
93. Volatile Organic Compound–Drug Receptor Interactions: A Potential Tool for Drug Design in the Search for Remedies for Increasing Toxic Occupational Exposure
    John Onyebuchi Ogbodo, Simeon Ikechukwu Egba, Gavin Chibundu Ikechukwu, Promise Chibuike Paul, Joseph Obinna Mba, Okechukwu Paul-Chima Ugwu, Tobechukwu Christian Ezike · 2025 · Processes
94. Establishing Good Computer Modelling Practice (GCMP) in the Prediction of Chemical Toxicity
    Philip Neville Judson, Chris Barber, Steven J. Canipa, Géraldine Poignant, Richard Dafydd Williams · 2015 · Molecular Informatics
95. Computational design and chemometric QSAR modeling of Plasmodium falciparum carbonic anhydrase inhibitors
    Shalini Singh · 2014 · Bioorganic & Medicinal Chemistry Letters
96. SAR-mediated Similarity Assessment of the Property Profile for New, Silicon-Based AChE/BChE Inhibitors
    Andrzej Bąk, Hana Pížová, Violetta Kozik, Katarína Vorčáková, Jiří Kos, Jakub Treml, Klára Odehnalová, Michal Oravec, Aleš Imramovský, Pavel Bobáľ et al. · 2019 · International Journal of Molecular Sciences
97. Allosteric Mechanisms of Nonadditive Substituent Contributions to Protein-Ligand Binding
    Stephen Boulton, Katherine Van, Bryan VanSchouwen, Jerry Augustine, Madoka Akimoto, Giuseppe Melacini · 2020 · Biophysical Journal
98. Prediction of Compound Bioactivities Using Heat-Diffusion Equation
    Tadashi Hidaka, Keiko Imamura, Takeshi Hioki, Terufumi Takagi, Yoshikazu Giga, Mi-Ho Giga, Yoshiteru Nishimura, Yoshinobu Kawahara, Satoru Hayashi, Takeshi Niki et al. · 2020 · Patterns
99. Artificial Intelligence Applied to the Rapid Identification of New Antimalarial Candidates with Dual‐Stage Activity
    Marília N. N. Lima, Joyce Villa Verde Bastos Borba, Gustavo Capatti Cassiano, Melina Mottin, Sabrina Silva Mendonça, Arthur C. Silva, Kaira Cristina Peralis Tomaz, Juliana Calit, Daniel Y. Bargieri, Fábio Trindade Maranhão Costa et al. · 2020 · ChemMedChem
100. Benchmarking Small-Dataset Structure-Activity-Relationship Models for Prediction of Wnt Signaling Inhibition
     Mahtab Kokabi, Matthew Donnelly, Guangyu Xu · 2020 · IEEE Access