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

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Items 201–300 of 1,001

1. In Silico Strategies in Tuberculosis Drug Discovery
   Stephani Joy Y. Macalino, Junie B. Billones, Voltaire G. Organo, Maria Constancia O. Carrillo · 2020 · Molecules
2. Cloud 3D-QSAR: a web tool for the development of quantitative structure–activity relationship models in drug discovery
   Yuliang Wang, Fan Wang, Xing-Xing Shi, Chen‐Yang Jia, Fengxu Wu, Ge‐Fei Hao, Guang‐Fu Yang · 2020 · Briefings in Bioinformatics
3. Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery
   Henry T. Hsueh, Renee Ti Chou, Usha Rai, Wathsala Liyanage, Yoo Chun Kim, Matthew B. Appell, Jahnavi Pejavar, Kirby T. Leo, Charlotte Davison, Patricia Kolodziejski et al. · 2023 · Nature Communications
4. Accelerating Big Data Analysis through LASSO-Random Forest Algorithm in QSAR Studies
   Fahimeh Motamedi, Horacio Pérez‐Sánchez, Alireza Mehridehnavi, Afshin Fassihi, Fahimeh Ghasemi · 2021 · Bioinformatics
5. Delfos: deep learning model for prediction of solvation free energies in generic organic solvents
   Hyuntae Lim, YounJoon Jung · 2019 · Chemical Science
6. In Silico ADME Methods Used in the Evaluation of Natural Products
   Robert Ancuceanu, Beatrice Elena Lascu, Doina Drăgănescu, Mihaela Dinu · 2025 · Pharmaceutics
7. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies
   Katarina M. Nikolic, Lazaros Mavridis, Teodora Djikić, Jelica Vučićević, Danica D. Agbaba, Kemal Yelekçi, John B. O. Mitchell · 2016 · Frontiers in Neuroscience
8. Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures
   Tuan Le, Robin Winter, Frank Noé, Djork-Arné Clevert · 2020 · Chemical Science
9. Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches
   Agneesh Pratim Das, Subhash Mohan Agarwal · 2023 · Molecular Diversity
10. Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials
    David Alan Winkler · 2015 · Toxicology and Applied Pharmacology
11. Integration of target discovery, drug discovery and drug delivery: A review on computational strategies
    Yorley Duarte, Valeria Márquez‐Miranda, Matthieu J. Miossec, Fernando Danilo González-Nilo · 2019 · Wiley Interdisciplinary Reviews Nanomedicine and Nanobiotechnology
12. AI-Integrated QSAR Modeling for Enhanced Drug Discovery: From Classical Approaches to Deep Learning and Structural Insight
    Mahesh Koirala, Lindy Yan, Zoser Mohamed, Mario DiPaola · 2025 · International Journal of Molecular Sciences
13. RRegrs: an R package for computer-aided model selection with multiple regression models
    Georgia Tsiliki, Cristian Robert Munteanu, José Antonio Seoane, Carlos Fernandez-Lozano, Haralambos Sarimveis, Egon Willighagen · 2015 · Journal of Cheminformatics
14. Why hasn’t there been more progress in new Chagas disease drug discovery?
    Alane Beatriz Vermelho, Giseli Capaci Rodrigues, Claudiu Trandafir Supuran · 2019 · Expert Opinion on Drug Discovery
15. A review of quantitative structure-activity relationship: The development and current status of data sets, molecular descriptors and mathematical models
    Jianmin Li, Tian Jiao Zhao, Qin Yang, Shijie Du, Lu Xu · 2024 · Chemometrics and Intelligent Laboratory Systems
16. Conformal Regression for Quantitative Structure–Activity Relationship Modeling—Quantifying Prediction Uncertainty
    Fredrik Svensson, Natália Aniceto, Ulf Norinder, Isidro Cortés‐Ciriano, Ola Spjuth, Lars Carlsson, Andreas Bender · 2018 · Journal of Chemical Information and Modeling
17. Benchmarking machine learning methods for modeling physical properties of ionic liquids
    Igor I. Baskin, Alon Epshtein, Yair Ein‐Eli · 2022 · Journal of Molecular Liquids
18. A novel workflow for semi-quantification of emerging contaminants in environmental samples analyzed by LC-HRMS
    Reza Aalizadeh, Varvara Nikolopoulou, ‪Nikiforos Alygizakis, Jaroslav Slobodnı́k, Νikolaos S. Τhomaidis · 2022 · Analytical and Bioanalytical Chemistry
19. Quantitative predictions from chemical read-across and their confidence measures
    Arkaprava Banerjee, Mainak Chatterjee, Priyanka De, Kunal Roy · 2022 · Chemometrics and Intelligent Laboratory Systems
20. Making use of available and emerging data to predict the hazards of engineered nanomaterials by means of in silico tools: A critical review
    Gianpietro Basei, Danail Rumenov Hristozov, Lara Lamon, Alex Zabeo, Nina Jeliazkova, Georgia Tsiliki, Antonio Marcomini, Andrea Torsello A · 2019 · NanoImpact
21. Computer-Aided Ligand Discovery for Estrogen Receptor Alpha
    Divya Bafna, Fuqiang Ban, Paul S. Rennie, Kriti Singh, Artem R Cherkasov · 2020 · International Journal of Molecular Sciences
22. Learning protein-ligand binding affinity with atomic environment vectors
    Rocco Meli, Andrew Anighoro, Mike J. Bodkin, Garrett M. Morris, Philip Charles Biggin · 2021 · Journal of Cheminformatics
23. Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure–Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients
    Arkaprava Banerjee, Kunal Roy · 2023 · Chemical Research in Toxicology
24. El Agente: An autonomous agent for quantum chemistry
    Yushi Zou, Austin Cheng, Abdulrahman Aldossary, Jiaru Bai, Shi Xuan Leong, Jorge A. Campos-Gonzalez-Angulo, Changhyeok Choi, Cher Tian Ser, Gary Tom, Andrew Wang et al. · 2025 · Matter
25. Imputation of Assay Bioactivity Data Using Deep Learning
    Thomas M. Whitehead, Benedict Irwin, Peter A. Hunt, Matthew Segall, G. J. Conduit · 2019 · Journal of Chemical Information and Modeling
26. A review on created QSPR models for predicting ionic liquids properties and their reliability from chemometric point of view
    Bakhtyar Sepehri · 2019 · Journal of Molecular Liquids
27. Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies
    Beatrice Pecoraro, Marco Tutone, Ewelina Hoffman, Victoria Hutter, Anna Maria Almerico, Matthew J. Traynor · 2019 · Journal of Chemical Information and Modeling
28. X-MOL: large-scale pre-training for molecular understanding and diverse molecular analysis
    Dongyu Xue, Han Zhang, Xiaohan Chen, Dongling Xiao, Yukang Gong, Guohui Chuai, Yu Sun, Hao Tian, Hua Ren Wu, Yukun Li et al. · 2022 · 中国科学通报：英文版
29. In silico prediction of chemical reproductive toxicity using machine learning
    Changsheng Jiang, Hongbin Yang, Peiwen Di, Weihua Li, Yun Tang, Guixia Liu · 2019 · Journal of Applied Toxicology
30. Using Physicochemical Measurements to Influence Better Compound Design
    Shenaz B. Bunally, Christopher N. Luscombe, Robert J. Young · 2019 · SLAS DISCOVERY
31. Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions
    Assima Rakhimbekova, Timur Madzhidov, Нугманов Рамиль Ирекович, Timur Rustemovich Gimadiev, Igor I. Baskin, Alexandre A. Varnek · 2020 · International Journal of Molecular Sciences
32. Curated Data In — Trustworthy In Silico Models Out: The Impact of Data Quality on the Reliability of Artificial Intelligence Models as Alternatives to Animal Testing
    Vinícius M. Alves, Scott S. Auerbach, Nicole Kleinstreuer, John P. Rooney, Eugene N. Muratov, Ivan Rusyn, Alexander Tropsha, Charles Schmitt · 2021 · Alternatives to Laboratory Animals
33. Antibiotic discovery in the artificial intelligence era
    Telmah Lluka, Jonathan M. Stokes · 2022 · Annals of the New York Academy of Sciences
34. Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products
    Christine Mae F. Ancajas, Abiodun Samuel Oyedele, Caitlin M. Butt, Allison S. Walker · 2024 · Natural Product Reports
35. Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method
    Zhenxing Wu, Dejun Jiang, Chang‐Yu Hsieh, Guangyong Chen, Ben Liao, Dongsheng Cao, Tingjun Hou · 2021 · Briefings in Bioinformatics
36. Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments
    Melanie T. Odenkirk, David M. Reif, Erin Shammel Baker · 2021 · Analytical Chemistry
37. Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods
    Barmak Mostofian, Holli‐Joi Martin, Asghar M. Razavi, Shivam Patel, Bryce K. Allen, Woody Sherman, Jesús A. Izaguirre · 2023 · Journal of Chemical Information and Modeling
38. Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered Nanomaterials
    Weihao Tang, Xuejiao Zhang, Huixiao Hong, Jingwen Chen, Qing Xun Zhao, Fengchang Wu · 2024 · Nanomaterials
39. Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis
    Peter Njogu, Eric M. Guantai, Elumalai Pavadai, Kelly Chibale · 2015 · ACS Infectious Diseases
40. Deep learning on chaos game representation for proteins
    Hannah Franziska Löchel, Dominic Eger, Theodor Sperlea, Dominik Heider · 2019 · Bioinformatics
41. Molecular complexity as a driving force for the advancement of organic synthesis
    Brandon A. Wright, Richmond Sarpong · 2024 · Nature Reviews Chemistry
42. Sweetness prediction of natural compounds
    Jean-Baptiste Chéron, Iuri Casciuc, Jérôme Golebiowski, Serge Antonczak, Sébastien Fiorucci · 2016 · Food Chemistry
43. SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network
    Shugang Zhang, Mingjian Jiang, Shuang Wang, Xiaofeng Wang, Zhiqiang Wei, Zhen Li · 2021 · International Journal of Molecular Sciences
44. Discovery of an Orally Bioavailable Inhibitor of Defective in Cullin Neddylation 1 (DCN1)-Mediated Cullin Neddylation
    Jared T. Hammill, Deepak Bhasin, Daniel C. Scott, Jaeki Min, Yizhe Chen, Yan Lü, Lei Yang, Ho Shin Kim, Michele Connelly, Courtney Vowell Hammill et al. · 2018 · Journal of Medicinal Chemistry
45. Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology
    Hyeoncheol Cho, Insung S. Choi · 2019 · ChemMedChem
46. Toward Rigorous Materials Production: New Approach Methodologies Have Extensive Potential to Improve Current Safety Assessment Practices
    Penny E. H. Nymark, Martine I Bakker, Susan Dekkers, Remy Franken, Wouter Fransman, Amaia García‐Bilbao, Dario Greco, Mary Gulumian, Niels Hadrup, Sabina S. Halappanavar et al. · 2020 · Small
47. A quantitative structure-biodegradation relationship (QSBR) approach to predict biodegradation rates of aromatic chemicals
    Kishor Acharya, David Werner, Jan Dolfing, Maciej Barycki, Paola Meynet, Wojciech Mrozik, Oladapo Komolafe, Tomasz Puzyn, Russell James Davenport · 2019 · Water Research
48. Targeting Tumors Using Peptides
    Pablo Scodeller, Eliana K. Asciutto · 2020 · Molecules
49. Predicting CO2 Absorption in Ionic Liquids with Molecular Descriptors and Explainable Graph Neural Networks
    Yue Jian, Yuyang Wang, Amir Barati Farimani · 2022 · ACS Sustainable Chemistry & Engineering
50. Antiviral and antimicrobial applications of chalcones and their derivatives: From nature to greener synthesis
    Mohammad Hadi Nematollahi, Mehrnaz Mehrabani, Yaser Hozhabri, Maryamossadat Mirtajaddini Goki, Siavash Iravani · 2023 · Heliyon
51. A review on the recent applications of deep learning in predictive drug toxicological studies
    Sinha, Krishnendu, Ghosh, Nabanita, Sil, Parames C. · 2023 · Chemical Research in Toxicology
52. High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures
    Ran Su, Sijing Xiong, Daniele Zink, Lit‐Hsin Loo · 2015 · Archives of Toxicology
53. Chemical Data Formats, Fingerprints, and Other Molecular Descriptions for Database Analysis and Searching
    Dávid Bajusz, Anita Rácz, Károly Héberger · 2017 · Elsevier eBooks
54. Computational approaches for skin sensitization prediction
    Anke Wilm, Jochen Kühnl, Johannes Kirchmair · 2018 · Critical Reviews in Toxicology
55. Physics-Based Method for Modeling Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules
    Andrei L. Lomize, Irina D. Pogozheva · 2019 · Journal of Chemical Information and Modeling
56. Closed-loop discovery platform integration is needed for artificial intelligence to make an impact in drug discovery
    Saikin, Semion K., Kreisbeck, Christoph, Sheberla, Dennis, Becker, Jill S., Aspuru-Guzik, Alán · 2019 · Expert Opinion on Drug Discovery
57. An Overview of Antiviral Peptides and Rational Biodesign Considerations
    Ying-Chiang Jeffrey Lee, Jaden D. Shirkey, Jongbeom Park, Karishma Bisht, Alexis J. Cowan · 2022 · BioDesign Research
58. Intercorrelation Limits in Molecular Descriptor Preselection for QSAR/QSPR
    Anita Rácz, Dávid Bajusz, Károly Héberger · 2019 · Molecular Informatics
59. KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
    Isidro Cortés‐Ciriano, Andreas Bender · 2019 · Journal of Cheminformatics
60. Deep Learning-driven research for drug discovery: Tackling Malaria
    Bruno J. Neves, Rodolpho C. Braga, Vinícius M. Alves, Marília N. N. Lima, Gustavo Capatti Cassiano, Eugene N. Muratov, Fábio Trindade Maranhão Costa, Carolina Horta Andrade · 2020 · PLoS Computational Biology
61. An Overview of Biological and Computational Methods for Designing Mechanism-Informed Anti-biofilm Agents
    Andy Y. An, Ka-Yee Grace Choi, Arjun S. Baghela, Robert E. W. Hancock · 2021 · Frontiers in Microbiology
62. Review of (Q)SAR models for regulatory assessment of nanomaterials risks
    Enrico Burello · 2017 · NanoImpact
63. Machine Learning Methods in Computational Toxicology
    Igor I. Baskin · 2018 · Methods in molecular biology
64. Cross-species extrapolation of chemical sensitivity
    Sanne J. P. van den Berg, Lorraine Maltby, Tom Sinclair, Ruoyu Liang, Paul J. Van den Brink · 2020 · The Science of The Total Environment
65. Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints
    Huawei Feng, Li Zhang, Shimeng Li, Lili Liu, Tianzhou Yang, Pengyu Yang, Jian Hua Zhao, Isaiah Tuvia Arkin, Hongsheng Liu · 2021 · Toxicology Letters
66. Harmonization Risks and Rewards: Nano-QSAR for Agricultural Nanomaterials
    Ajay Vikram Singh, Amruta V. Shelar, Mansi Rai, Peter Laux, Manali Thakur, Ievgen S. Donskyi, Giulia Santomauro, Alok Kumar Singh, Andreas Luch, Rajendra Patil et al. · 2024 · Journal of Agricultural and Food Chemistry
67. In silico design of low molecular weight protein–protein interaction inhibitors: Overall concept and recent advances
    Mélaine A. Kuenemann, Olivier Spérandio, Céline M. Labbé, David Lagorce, Maria A. Miteva, Bruno O. Villoutreix · 2015 · Progress in Biophysics and Molecular Biology
68. Improving quantitative structure–activity relationship models using Artificial Neural Networks trained with dropout
    Jeffrey Mendenhall, Jens Meiler · 2016 · Journal of Computer-Aided Molecular Design
69. Computational Method for the Systematic Identification of Analog Series and Key Compounds Representing Series and Their Biological Activity Profiles
    Dagmar Stumpfe, Dilyana Dimova, Jürgen Bajorath · 2016 · Journal of Medicinal Chemistry
70. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization
    Vinícius M. Alves, Stephen J. Capuzzi, Eugene N. Muratov, Rodolpho C. Braga, Thomas Thornton, Denis Fourches, Judy A. Strickland, Nicole Kleinstreuer, Carolina Horta Andrade, Alexander Tropsha · 2016 · Green Chemistry
71. Multi-objective optimization methods in novel drug design
    George Lambrinidis, Anna Tsantili‐Kakoulidou · 2020 · Expert Opinion on Drug Discovery
72. Object Oriented Data Analysis
    James Stephen Marron, Ian L. Dryden · 2021 · Chapman & Hall/CRC eBooks
73. Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules
    Chengkun Wu, Xiaochen Zhang, Zhijiang Yang, Aiping Lü, Tingjun Hou, Dongsheng Cao · 2021 · Briefings in Bioinformatics
74. Computational approaches to chemical hazard assessment
    Thomas Luechtefeld · 2017 · ALTEX
75. QSAR analysis of substituent effects on tambjamine anion transporters
    Nicola Knight, Elsa Hernando, Cally J. E. Haynes, Nathalie Busschaert, Harriet J. Clarke, Koji Takimoto, María García‐Valverde, Jeremy Graham Frey, Roberto Quesada, Philip Alan Gale · 2015 · Chemical Science
76. Secondary pharmacology: screening and interpretation of off-target activities – focus on translation
    Steven Whitebread, Berengere M Dumotier, Duncan Armstrong, Alexander Fekete, Shanni Chen, Andreas Hartmann, Patrick Y. Muller, László Urbán · 2016 · Drug Discovery Today
77. www.3d-qsar.com: a web portal that brings 3-D QSAR to all electronic devices—the Py-CoMFA web application as tool to build models from pre-aligned datasets
    Rino Ragno · 2019 · Journal of Computer-Aided Molecular Design
78. Is regression through origin useful in external validation of QSAR models?
    Ali Shayanfar, Shadi Shayanfar · 2014 · European Journal of Pharmaceutical Sciences
79. Prediction of ionic liquids viscosity at variable temperatures and pressures
    Fangyou Yan, Wensi He, Qingzhu Jia, Qiang Wang, Shuqian Xia, Peisheng Ma · 2018 · Chemical Engineering Science
80. Current computational methods for predicting protein interactions of natural products
    Aurélien F. A. Moumbock, Jianyu Li, Pankaj Mishra, Mingjie Gao, Stefan Günther · 2019 · Computational and Structural Biotechnology Journal
81. A comparison between 2D and 3D descriptors in QSAR modeling based on bio‐active conformations
    Malkeet Singh Bahia, Omer Kaspi, Meir Touitou, Idan Binayev, Seema Dhail, J. Dominik Spiegel, Netaly Khazanov, Abraham Yosipof, Hanoch Senderowitz · 2023 · Molecular Informatics
82. From Classical Toxicology to Tox21: Some Critical Conceptual and Technological Advances in the Molecular Understanding of the Toxic Response Beginning From the Last Quarter of the 20th Century
    Supratim Choudhuri, Geoffrey W. Patton, Ronald F. Chanderbhan, Antonia Mattia, Curtis D. Klaassen · 2017 · Toxicological Sciences
83. Modelling methods and cross-validation variants in QSAR: a multi-level analysis$
    Anita Rácz, Dávid Bajusz, Károly Héberger · 2018 · SAR and QSAR in environmental research
84. Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 1: Theory and characteristics†
    Ramón Alain Miranda‐Quintana, Dávid Bajusz, Anita Rácz, Károly Héberger · 2021 · Journal of Cheminformatics
85. Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs
    Alves, Vinicius M., Hwang, Duhyeong, Muratov, Eugene, Sokolsky-Papkov, Marina, Varlamova, Ekaterina, Vinod, Natasha, Lim, Chaemin, Andrade, Carolina H., Tropsha, Alexander, Kabanov, Alexander · 2019 · Science Advances
86. Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer’s disease
    Dhamodharan, G., Mohan, C. Gopi · 2022 · Molecular Diversity
87. Machine Learning Models Identify Inhibitors of SARS-CoV-2
    Victor Oliveira Gawriljuk, Phyo Phyo Kyaw Zin, Daniel H. Foil, Jean Bernatchez, Sungjun Beck, Nathan Beutler, James Ricketts, Linlin Yang, Thomas Rogers, Ana C. Puhl et al. · 2021 · Journal of Chemical Information and Modeling
88. The advancement of multidimensional QSAR for novel drug discovery - where are we headed?
    Tao Wang, Xinsong Yuan, Mianbin Wu, Jianping Lin, Li-Rong Yang · 2017 · Expert Opinion on Drug Discovery
89. Chemical Diversity of Soft Coral Steroids and Their Pharmacological Activities
    Ekaterina V. Ermolenko, Andrey B. Imbs, Tatyana A. Gloriozova, Vladimir Poroikov, Tatyana V. Sikorskaya, Valery M. Dembitsky · 2020 · Marine Drugs
90. QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni
    Cleber Camilo Melo-Filho, Rafael Ferreira Dantas, Rodolpho C. Braga, Bruno J. Neves, Mário Roberto Senger, Walter César Góes Valente, João M. Rezende-Neto, Willian T. Chaves, Eugene N. Muratov, Ross A. Paveley et al. · 2016 · Journal of Chemical Information and Modeling
91. Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches
    Muhammad Ishfaq, Zia-ur Rahman, Muhammad Aamir, Ihsan Ali, Yurong Guan, Zhihua Hu · 2022 · Molecular Diversity
92. A review of quantitative structure-activity relationship modelling approaches to predict the toxicity of mixtures
    Samuel J. Belfield, James W. Firman, Steven J. Enoch, Judith Clare Madden, Knut Erik Tollefsen, M Cronin · 2022 · Computational Toxicology
93. Identification and Characterization of Novel Bioactive Peptide From Red Seaweed (Pyropia vietnamensis) Proteins
    Nur Iliana Basri, Amiza Mat Amin, Fisal Ahmad · 2025 · Quality Assurance and Safety of Crops & Foods
94. Biological activity-based modeling identifies antiviral leads against SARS-CoV-2
    Ruili Huang, Miao Xu, Hu Zhu, Catherine Z. Chen, Wei Jian Zhu, Emily M. Lee, Shihua He, Li Zhang, Jinghua Zhao, Khalida Shamim et al. · 2021 · Nature Biotechnology
95. Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis
    Hongyu Chen, Shigeru Yamaguchi, Yuya Morita, Hiroyasu Nakao, Xiangning Zhai, Y. Shimizu, Harunobu Mitsunuma, Motomu Kanai · 2021 · Cell Reports Physical Science
96. Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution
    Dale Seddon, Erich A. Müller, João T. Cabral · 2022 · Journal of Colloid and Interface Science
97. Computer-aided drug discovery of Myc-Max inhibitors as potential therapeutics for prostate cancer
    Lavinia A. Carabet, Nada Lallous, Eric Leblanc, Fuqiang Ban, Hélène Morin, Samuel O. Lawn, Fariba Ghaidi, Joseph Lee, Ian Geoffrey Mills, Martin Edwin Gleave et al. · 2018 · European Journal of Medicinal Chemistry
98. Potential for Machine Learning to Address Data Gaps in Human Toxicity and Ecotoxicity Characterization
    Kerstin von Borries, Hanna Holmquist, Marissa B. Kosnik, Katie V. Beckwith, Olivier Jolliet, Jonathan M. Goodman, Peter Fantke · 2023 · Environmental Science & Technology
99. Chembench: A Publicly Accessible, Integrated Cheminformatics Portal
    Stephen J. Capuzzi, Ian Sang-June Kim, Wai In Lam, Thomas Thornton, Eugene N. Muratov, Diane Pozefsky, Alexander Tropsha · 2017 · Journal of Chemical Information and Modeling
100. Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies
     Mingyue Zheng, Jihui Zhao, Chen Cui, Zunyun Fu, Xutong Li, Xiaohong Liu, Xiaoyu Ding, Xiaoqin Tan, Fei Li, Xiaomin Luo et al. · 2018 · Medicinal Research Reviews