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

Cited by (1,001)

Items 601–700 of 1,001

1. Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?
   Andrzej Bąk · 2021 · International Journal of Molecular Sciences
2. Accelerating the pace of ecotoxicological assessment using artificial intelligence
   Runsheng Song, Dingsheng Li, Alexander Chang, Mengya Tao, Yuwei Qin, Arturo A. Keller, Sangwon Suh · 2021 · AMBIO
3. Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks
   Mohit Kumar Pandey, Mariia Radaeva, Hazem Mslati, Olivia Garland, Michael Fernández, Martin Ester, Artem R Cherkasov · 2022 · Molecules
4. DNN-PP: A novel Deep Neural Network approach and its applicability in drug-related property prediction
   Magdalena Wiercioch, Johannes Kirchmair · 2022 · Expert Systems with Applications
5. Artificial intelligence for antiviral drug discovery in low resourced settings: A perspective
   Cyril T. Namba-Nzanguim, Gemma Turón, Conrad Veranso Simoben, Ian Tietjen, Luis J. Montaner, Simon M. N. Efange, Miquel Duran‐Frigola, Fidele Ntie‐Kang · 2022 · Frontiers in Drug Discovery
6. QSAR Studies, Molecular Docking, Molecular Dynamics, Synthesis, and Biological Evaluation of Novel Quinolinone-Based Thiosemicarbazones against Mycobacterium tuberculosis
   Jhesua Valencia, Vivian Vanesa Rubio, Gloria Puerto, Luisa Vasquez, Anthony Bernal, José R. Mora, Sebastián A. Cuesta, José Luis Paz, Braulio Insuasty O., Rodrigo Abonı́a et al. · 2022 · Antibiotics
7. DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application
   Ting Li, Zhichao Liu, Shraddha Thakkar, Ruth Angela Roberts, Weida Tong · 2023 · Regulatory Toxicology and Pharmacology
8. Conformalized Graph Learning for Molecular ADMET Property Prediction and Reliable Uncertainty Quantification
   Peiyao Li, Lan Hua, Zhechao Ma, Wenbo Hu, Ye Liu, Jun Zhu · 2024 · Journal of Chemical Information and Modeling
9. Monte Carlo method based QSAR modeling of maleimide derivatives as glycogen synthase kinase-3β inhibitors
   Jelena V. Živković, Nataša Trutić, Jovana B. Veselinović, Goran Milan Nikolić, Aleksandar M. Veselinović · 2015 · Computers in Biology and Medicine
10. Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme
    Max K. Leong, Ren-Guei Syu, Yi-Lung Ding, Ching‐Feng Weng · 2017 · Scientific Reports
11. Virulence factor activity relationships (VFARs): a bioinformatics perspective
    Hassan Waseem, Maggie R. Williams, Tiffany M. Stedtfeld, Benli Chai, Robert D. Stedtfeld, James R. Cole, James M. Tiedje, Syed A. Hashsham · 2017 · Environmental Science Processes & Impacts
12. Cheminformatics Modeling of Amine Solutions for Assessing their CO2Absorption Properties
    Mélaine A. Kuenemann, Denis Fourches · 2017 · Molecular Informatics
13. In Silico ADME Techniques Used in Early-Phase Drug Discovery
    Matthew L. Danielson, Bingjie Hu, Jie Shen, Prashant V. Desai · 2017 · AAPS advances in the pharmaceutical sciences series
14. Progress with modeling activity landscapes in drug discovery
    Martin Vogt · 2018 · Expert Opinion on Drug Discovery
15. Study of Data Set Modelability: Modelability, Rivality, and Weighted Modelability Indexes
    Irene Luque Ruiz, Miguel Ángel Gómez‐Nieto · 2018 · Journal of Chemical Information and Modeling
16. CODD-Pred: A Web Server for Efficient Target Identification and Bioactivity Prediction of Small Molecules
    Xiaodan Yin, Xiaorui Wang, Yuquan Li, Jike Wang, Yuwei Wang, Yafeng Deng, Tingjun Hou, Huanxiang Liu, Pei Luo, Xiaojun Yao · 2023 · Journal of Chemical Information and Modeling
17. Unlocking the potential of carbon dots in agriculture using data-driven approaches
    Jing Li, Xiao‐Na Li, Mélanie Kah, Le Yue, Bingxu Cheng, Chuanxi Wang, Zhenyu Wang, Baoshan Xing · 2024 · The Science of The Total Environment
18. Quantitative Nano-Structure–Property Relationships for the Nanoporous Carbon: Predicting the Performance of Energy Storage Materials
    Maike Käärik, Uko Maran, Mati Arulepp, Anti Perkson, Jaan Leis · 2018 · ACS Applied Energy Materials
19. Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability
    Giang Huong Ta, Cin-Syong Jhang, Ching‐Feng Weng, Max K. Leong · 2021 · Pharmaceutics
20. Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents
    Kushagra Kashyap, Mohammad Imran Siddiqi · 2021 · Molecular Diversity
21. Model-Driven Design of Redox Mediators: Quantifying the Impact of Quinone Structure on Bioelectrocatalytic Activity with Glucose Oxidase
    Lincoln Mtemeri, David P. Hickey · 2023 · The Journal of Physical Chemistry B
22. Read-across-based intelligent learning: development of a global q-RASAR model for the efficient quantitative predictions of skin sensitization potential of diverse organic chemicals
    Arkaprava Banerjee, Kunal Roy · 2023 · Environmental Science Processes & Impacts
23. Refining the chemical toolbox to be fit for educational and practical purpose for drug discovery in the 21st Century
    Marco L. Lolli, Sarah Narramore, Colin W. G. Fishwick, Klaus Pors · 2015 · Drug Discovery Today
24. Predicting the Minimal Inhibitory Concentration for Antimicrobial Peptides with Rana-Box Domain
    Mara Kozić, Damir Vukičević, Juraj Simunić, Tomislav Rončević, Nikolinka Antcheva, Alessandro Tossi, Davor Juretić · 2015 · Journal of Chemical Information and Modeling
25. Quantitative Structure-Antioxidant Activity Models of Isoflavonoids: A Theoretical Study
    Gloria Castellano, Francisco Torrens · 2015 · International Journal of Molecular Sciences
26. Multiscale quantum chemical approaches to QSAR modeling and drug design
    Pier Giuseppe De Benedetti, Francesca Fanelli · 2014 · Drug Discovery Today
27. Advances in quantitative structure–activity relationship models of anti-Alzheimer’s agents
    Pravin Sundarao Ambure, Kunal Roy · 2014 · Expert Opinion on Drug Discovery
28. Discovery and Development of Lead Compounds from Natural Sources Using Computational Approaches
    José L. Medina‐Franco · 2015 · Elsevier eBooks
29. Why are most phospholipidosis inducers also hERG blockers?
    Svetoslav H. Slavov, Iva B. Stoyanova‐Slavova, Shuaizhang Li, Jinghua Zhao, Ruili Huang, Menghang Xia, Richard D. Beger · 2017 · Archives of Toxicology
30. Quantitative structure–activity relationship (QSAR) directed the discovery of 3‐(pyridin‐2‐yl)benzenesulfonamide derivatives as novel herbicidal agents
    Yong Xie, Wei Peng, Fei Ding, Shu‐Jie Liu, MA Hong-juan, Changling Liu · 2017 · Pest Management Science
31. On the Virtues of Automated Quantitative Structure–Activity Relationship: The New Kid on the Block
    Marcelo Tavares de Oliveira, Edson Katekawa · 2018 · Future Medicinal Chemistry
32. Organic structure and solid characteristics determine reactivity of phenolic compounds with synthetic and reclaimed manganese oxides
    Emma Leverich Trainer, Matthew A. Ginder-Vogel, Christina K. Remucal · 2019 · Environmental Science Water Research & Technology
33. Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors
    Hajar Sirous, Giuseppe Campiani, Simone Brogi, Vincenzo Calderone, Giulia Chemi · 2020 · Molecules
34. Developing novel computational prediction models for assessing chemical-induced neurotoxicity using naïve Bayes classifier technique
    Hui Zhang, Jun Mao, Hua-Zhao Qi, Huanzhang Xie, Chen Shen, Chun‐Tao Liu, Lan Ding · 2020 · Food and Chemical Toxicology
35. Flame: an open source framework for model development, hosting, and usage in production environments
    Manuel Pastor, José Carlos Gómez-Tamayo, Ferrán Sanz · 2021 · Journal of Cheminformatics
36. ToxTeller: Predicting Peptide Toxicity Using Four Different Machine Learning Approaches
    Jen-Hung Wang, Ting‐Yi Sung · 2024 · ACS Omega
37. Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles
    Mingli Li, Qiao-Zhi Li, Yuliang Zhao, Xingfa Gao · 2025 · Chem & Bio Engineering
38. QSAR models for describing the toxicological effects of ILs against Candida albicans based on norm indexes
    Fangyou Yan, Wensi He, Qingzhu Jia, Shuqian Xia, Qiang Wang · 2018 · Chemosphere
39. Theoretical modeling of structure-toxicity relationship of cyanides
    Marcin Molski · 2021 · Toxicology Letters
40. Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database
    Chonny Herrera‐Acevedo, Camilo Perdomo‐Madrigal, Kenyi Herrera-Acevedo, Ericsson David Coy-Barrera, Luciana Scotti, Marcus Tullius Scotti · 2021 · Molecular Diversity
41. Hafnium vs. Zirconium, the Perpetual Battle for Supremacy in Catalytic Olefin Polymerization: A Simple Matter of Electrophilicity?
    Antonio Vittoria, Georgy P. Goryunov, Vyatcheslav V. Izmer, Dmitry S. Kononovich, Oleg V. Samsonov, Francesco Zaccaria, Gaia Urciuoli, Peter H. M. Budzelaar, Vincenzo Busico, Alexander Z. Voskoboynikov et al. · 2021 · Polymers
42. Limits of Prediction for Machine Learning in Drug Discovery
    Modest von Korff, Thomas Sander · 2022 · Frontiers in Pharmacology
43. Quantitative Structure–Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective
    Ranita Pal, Shanti G. Patra, Pratim Kumar Chattaraj · 2022 · Pharmaceuticals
44. Prediction of drug-induced mitochondrial dysfunction using succinate-cytochrome c reductase activity, QSAR and molecular docking
    Alicia Rosell-Hidalgo, Anthony L. Moore, Taravat Ghafourian · 2022 · Toxicology
45. Computer-Aided drug design of new 2-amino-thiophene derivatives as anti-leishmanial agents
    Isadora Silva Luna, Thalisson Amorim de Souza, Marcelo Sobral da Silva, Klinger Antônio da França Rodrigues, Luciana Scotti, Marcus Tullius Scotti, Francisco Jaime Bezerra Mendonça · 2023 · European Journal of Medicinal Chemistry
46. Explaining compound activity predictions with a substructure-aware loss for graph neural networks
    Kenza Amara, Raquel Rodríguez-Pérez, José Jiménez-Luna · 2023 · Journal of Cheminformatics
47. Modern approaches to accelerate discovery of new antischistosomal drugs
    Bruno J. Neves, Eugene N. Muratov, Renato Beilner Machado, Carolina Horta Andrade, Pedro Cravo · 2016 · Expert Opinion on Drug Discovery
48. Partition coefficients for the SAMPL5 challenge using transfer free energies
    Michael R. Jones, Bernard R. Brooks, Angela K. Wilson · 2016 · Journal of Computer-Aided Molecular Design
49. QSAR studies of the bioactivity of hepatitis C virus (HCV) NS3/4A protease inhibitors by multiple linear regression (MLR) and support vector machine (SVM)
    Zijian Qin, Maolin Wang, Aixia Yan · 2017 · Bioorganic & Medicinal Chemistry Letters
50. Anti-flavi: A Web Platform to Predict Inhibitors of Flaviviruses Using QSAR and Peptidomimetic Approaches
    Akanksha Rajput, Manoj Kumar · 2018 · Frontiers in Microbiology
51. In silico toxicity evaluation of dioxins using structure–activity relationship (SAR) and two-dimensional quantitative structure–activity relationship (2D-QSAR)
    Hong Yang, Zhe Du, Wenjuan Lv, Xiaoyun Zhang, Honglin Zhai · 2019 · Archives of Toxicology
52. Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events
    Andrew J. Wedlake, Maria Myrto Folia, Sam Piechota, Timothy E. H. Allen, Jonathan M. Goodman, Steve Gutsell, Paul J Russell · 2019 · Chemical Research in Toxicology
53. Modeling to predict the cytotoxicity of SiO2 and TiO2 nanoparticles
    Samira Jafari, Ali Shayanfar · 2019 · Journal of Research in Pharmacy
54. Strategies of Virtual Screening in Medicinal Chemistry
    Giovanna Ilaria Passeri, Daniela Trisciuzzi, Domenico Alberga, Lydia Siragusa, Francesco Leonetti, Giuseppe Felice Mangiatordi, Orazio Nicolotti · 2017 · International Journal of Quantitative Structure-Property Relationships
55. A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer
    Hong-Yi Zhi, Lu Zhao, Cheng-Chun Lee, Calvin Yu‐Chian Chen · 2021 · Biomolecules
56. Synthesis and Hybrid SAR Property Modeling of Novel Cholinesterase Inhibitors
    Jiří Kos, Violetta Kozik, Dominika Pindjaková, Timotej Jankech, Adam Smoliński, Šárka Štěpánková, Jan Hošek, Michal Oravec, Josef Jampílek, Andrzej Bąk · 2021 · International Journal of Molecular Sciences
57. Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood–Brain Barrier Passage
    Taeho Kim, Byoung Hoon You, Song‐Hee Han, Ho Chul Shin, Kee‐Choo Chung, Hwangseo Park · 2021 · International Journal of Molecular Sciences
58. Predicting chemical ecotoxicity by learning latent space chemical representations
    Feng Gao, Wei Zhang, Andrea Baccarelli, Yike Shen · 2022 · Environment International
59. Extended continuous similarity indices: theory and application for QSAR descriptor selection
    Anita Rácz, Timothy B. Dunn, Dávid Bajusz, Taewon D. Kim, Ramón Alain Miranda‐Quintana, Károly Héberger · 2022 · Journal of Computer-Aided Molecular Design
60. Working at the interfaces of data science and synthetic electrochemistry
    Jesus I. Martinez Alvarado, Jonathan M. Meinhardt, Song Lin · 2022 · Tetrahedron Chem
61. Quantitative Structure-Property Relationship (QSPR) Modeling Applications in Formulation Development
    Pankaj K. Wadhwa, Amit Mittal · 2022 · Springer eBooks
62. Leave-one-ion-out cross-validation for assisting in developing robust QSPR models of ionic liquids
    Xiao Liu, Mengxian Yu, Qingzhu Jia, Fangyou Yan, Yin‐Ning Zhou, Qiang Wang · 2023 · Journal of Molecular Liquids
63. Potent VEGFR-2 inhibitors for resistant breast cancer: a comprehensive 3D-QSAR, ADMET, molecular docking and MMPBSA calculation on triazolopyrazine derivatives
    Soukayna Baammi, Achraf El Allali, Rachid Daoud · 2023 · Frontiers in Molecular Biosciences
64. A Machine-Learning-Driven Pathophysiology-Based New Approach Method for the Dose-Dependent Assessment of Hazardous Chemical Mixtures and Experimental Validations
    Sarita Limbu, Eric F. Glasgow, Tessa Block, Sivanesan Dakshanamurthy · 2024 · Toxics
65. Comprehensive evaluation and systematic comparison of Gaussian process (GP) modelling applications in peptide quantitative structure-activity relationship
    Haiyang Ye, Yunyi Zhang, Zilong Li, Peng Yue, Peng Zhou · 2024 · Chemometrics and Intelligent Laboratory Systems
66. Prediction of Compound Potency Changes in Matched Molecular Pairs Using Support Vector Regression
    Antonio de la Vega de León, Jürgen Bajorath · 2014 · Journal of Chemical Information and Modeling
67. Synthesis, in vitro evaluation and QSAR modelling of potential antitumoral 3,4-dihydropyrimidin-2-(1H)-thiones
    Mariana Matias, Gonçalo Campos, Adriana O. Santos, Amílcar C. Falcão, Samuel Silvestre, Gilberto Alves · 2016 · Arabian Journal of Chemistry
68. Kinetic study of Friedel-Crafts acylation reactions over hierarchical MCM-22 zeolites
    Rodrigo Aleixo, Rúben Elvas‐Leitão, Filomena E. L. Martins, Ana P. Carvalho, Amadeu F. Brigas, Ângela Martins, Nélson Nunes · 2017 · Molecular Catalysis
69. Performance of In Silico Models for Mutagenicity Prediction of Food Contact Materials
    Melissa Van Bossuyt, Els Van Hoeck, Giuseppa Raitano, Tamara Vanhaecke, Emilio Benfenati, Birgit Mertens, Vera Rogiers · 2018 · Toxicological Sciences
70. Classifying antimicrobial and multifunctional peptides with Bayesian network models
    Rainier Barrett, Shaoyi Jiang, Andrew D. White · 2018 · Peptide Science
71. Using 3D-QSAR to predict the separation efficiencies of flotation collectors: Implications for rational design of non-polar side chains
    Xianglin Yang, Boris Albijanic, Yuan Zhou, You Zhou, Xiangnan Zhu · 2018 · Minerals Engineering
72. Classification of HIV-1 Protease Inhibitors by Machine Learning Methods
    Yang Li, Yujia Tian, Zijian Qin, Aixia Yan · 2018 · ACS Omega
73. Rational Use of Heterogeneous Data in Quantitative Structure–Activity Relationship (QSAR) Modeling of Cyclooxygenase/Lipoxygenase Inhibitors
    Alexey A. Lagunin, Athina A. Geronikaki, Phaedra T. Eleftheriou, Pavel V. Pogodin, Alexey V. Zakharov · 2019 · Journal of Chemical Information and Modeling
74. MaNGA: a novel multi-niche multi-objective genetic algorithm for QSAR modelling
    Angela Serra, Serli Önlü, Paola Festa, Vittorio Fortino, Dario Greco · 2019 · Bioinformatics
75. Molecular Field Analysis Using Intermediates in Enantio-Determining Steps Can Extract Information for Data-Driven Molecular Design in Asymmetric Catalysis
    Shigeru Yamaguchi, Mikiko Sodeoka · 2019 · Bulletin of the Chemical Society of Japan
76. Neural network activation similarity: a new measure to assist decision making in chemical toxicology
    Timothy E. H. Allen, Andrew J. Wedlake, Elena Gelžinytė, Charles Gong, Jonathan M. Goodman, Steve Gutsell, Paul J Russell · 2020 · Chemical Science
77. Optimal Piecewise Linear Regression Algorithm for QSAR Modelling
    Jonathan Cardoso‐Silva, George Papadatos, Lazaros G. Papageorgiou, Sophia Tsoka · 2018 · Molecular Informatics
78. Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome
    Aditya Sanjay Malwe, Vineet K. Sharma · 2023 · Frontiers in Microbiology
79. HotTiles: Accelerating SpMM with Heterogeneous Accelerator Architectures
    Gerasimos Gerogiannis, Sriram Aananthakrishnan, Josep Torrellas, Ibrahim Hur · 2024 · IEEE International Symposium on High-Performance Computer Architecture (HPCA)
80. MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data
    Katarzyna Arturi, Eliza Harris, Lilian Gasser, Beate I. Escher, Georg Braun, Robin Bosshard, Juliane Hollender · 2025 · Journal of Cheminformatics
81. Next generation risk assessment and new approach methodologies for safe and sustainable by design chemicals and materials: Perspectives and challenges for occupational health
    Veruscka Leso, Bernd Nowack, Achilleas Karakoltzidis, Fotini Nikiforou, S Karakitsios, Dimosthenis Andreas Sarigiannis, Ivo Iavicoli · 2025 · Toxicology
82. QSAR design of triazolopyridine mGlu2 receptor positive allosteric modulators
    Gary Tresadern, Jose Maria Cid, Andrés A. Trabanco · 2014 · Journal of Molecular Graphics and Modelling
83. The Use of Methods of Computer-Aided Drug Discovery in the Development of Topoisomerase II Inhibitors: Applications and Future Directions
    Mariia Radaeva, Xuesen Dong, Artem R Cherkasov · 2020 · Journal of Chemical Information and Modeling
84. Data considerations for predictive modeling applied to the discovery of bioactive natural products
    Hai Tao Xue, Michael Stanley-Baker, Adams Wai‐Kin Kong, HY Li, Wilson Wen Bin Goh · 2022 · Drug Discovery Today
85. Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents
    Joshua E. Hochuli, Sankalp Jain, Cleber Camilo Melo-Filho, Zoe L. Sessions, Tesia Bobrowski, Jun Choe, Johnny Zheng, Richard T. Eastman, Daniel C. Talley, Ganesha Rai et al. · 2022 · ACS Pharmacology & Translational Science
86. Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features
    Moritz Walter, Samuel J. Webb, Valerie J. Gillet · 2024 · Journal of Chemical Information and Modeling
87. Insights into the adsorption of simple benzene derivatives on carbon nanotubes
    Yonglan Liu, Jin Zhang, Xiaohua Chen, Jie Zheng, Guixue Wang, Guizhao Liang · 2014 · RSC Advances
88. Integrative approaches in HIV ‐1 non‐nucleoside reverse transcriptase inhibitor design
    Vasanthanathan Poongavanam, Vigneshwaran Namasivayam, Murugesan Vanangamudi, Hadi Al Shamaileh, Rakesh Naduvile Veedu, Jan O. Kihlberg, N. Arul Murugan · 2017 · Wiley Interdisciplinary Reviews Computational Molecular Science
89. Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study
    Veda Prachayasittikul, Ratchanok Pingaew, Apilak Worachartcheewan, Somkid Sitthimonchai, Chanin Nantasenamat, Supaluk Prachayasittikul, Somsak Ruchirawat, Virapong Prachayasittikul · 2017 · EXCLI journal
90. QSAR: What Else?
    Giuseppina C. Gini · 2018 · Methods in molecular biology
91. The Design, Synthesis, and Characterizations of Spore Germination Inhibitors Effective against an Epidemic Strain of Clostridium difficile
    Shiv Kumar Sharma, Christopher Yip, Emilio Xavier Esposito, Prateek V. Sharma, Matthew P. Simon, Ernesto Abel‐Santos, Steven M. Firestine · 2018 · Journal of Medicinal Chemistry
92. Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology
    Chenxiao Da, Dehui� Zhang, Michael A. Stashko, Eleana Vasileiadi, Rebecca E. Parker, Katherine A. Minson, Madeline G. Huey, Justus M. Huelse, Debra M. Hunter, Thomas S.K. Gilbert et al. · 2019 · Journal of the American Chemical Society
93. Best Practices for Constructing Reproducible QSAR Models
    Chanin Nantasenamat · 2020 · Methods in pharmacology and toxicology
94. Selection of antileishmanial sesquiterpene lactones from SistematX database using a combined ligand-/structure-based virtual screening approach
    Chonny Herrera‐Acevedo, Mayara dos Santos Maia, Élida Batista Vieira Sousa Cavalcanti, Ericsson David Coy-Barrera, Luciana Scotti, Marcus Tullius Scotti · 2020 · Molecular Diversity
95. Computer-Aided Drug Designing
    Thakor Rajkishan, Ailani Rachana, Surani Shruti, Bhumi Patel, Dhaval Patel · 2021 · Springer eBooks
96. Computational Method to Evaluate Progress in Lead Optimization
    Martin Vogt, Dimitar Yonchev, Jürgen Bajorath · 2018 · Journal of Medicinal Chemistry
97. FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction
    Zheng Wu, Nannan Wang, Zhuyifan Ye, Huanle Xu, Ging Chan, Defang Ouyang · 2024 · Molecular Pharmaceutics
98. Chemical space visual navigation in the era of deep learning and Big Data
    Sergey Sosnin · 2025 · Drug Discovery Today
99. Ligand-based 3D QSAR analysis of reactivation potency of mono- and bis-pyridinium aldoximes toward VX-inhibited rat acetylcholinesterase
    Rafael Doležal, Jan Korábečný, David Maliňák, Jan Honegr, Kamil Musílek, Kamil Kuča · 2014 · Journal of Molecular Graphics and Modelling
100. Computational Modeling of Mixture Toxicity
     Mainak Chatterjee, Kunal Roy · 2022 · Methods in molecular biology