QSAR without borders

Cites (335)

Items 201–335 of 335

1. General Theory for Multiple Input-Output Perturbations in Complex Molecular Systems. 1. Linear QSPR Electronegativity Models in Physical, Organic, and Medicinal Chemistry
   Gonzalez-Diaz, Humberto, Arrasate, Sonia, Gomez-SanJuan, Asier, Sotomayor, Nuria, Lete, Esther, Besada-Porto, Lina, Ruso, Juan · 2013 · Current Topics in Medicinal Chemistry
2. Can literature analysis identify innovation drivers in drug discovery?
   Agarwal, Pankaj, Searls, David B. · 2009 · Nature Reviews Drug Discovery
3. Lessons Learned from Molecular Scaffold Analysis
   Ye Hu, Dagmar Stumpfe, Jürgen Bajorath · 2011 · Journal of Chemical Information and Modeling
4. Minimum information about a bioactive entity (MIABE)
   Sandra E. Orchard, Bissan Al‐Lazikani, Steve H Bryant, Dominic A. Clark, Elizabeth Calder, Ian Dix, Ola Engkvist, Mark J. Forster, Anna Gaulton, Michael K. Gilson et al. · 2011 · Nature Reviews Drug Discovery
5. Quasi-SMILES-Based Nano-Quantitative Structure–Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells
   Tung X. Trinh, Jang-Sik Choi, Hyunpyo Jeon · 2018 · Chemical Research in Toxicology
6. Seven-Day NIHSS Is a Sensitive Outcome Measure for Exploratory Clinical Trials in Acute Stroke: Evidence From the Virtual International Stroke Trials Archive
   Kerr, Daniel M., Fulton, Rachael L., Lees, Kennedy R., for the VISTA Collaborators · 2012 · Stroke
7. Data-driven identification of structural alerts for mitigating the risk of drug-induced human liver injuries
   Liu, Ruifeng, Yu, Xueping, Wallqvist, Anders · 2015 · Journal of Cheminformatics
8. Exploring Quantitative Nanostructure-Activity Relationships (QNAR) Modeling as a Tool for Predicting Biological Effects of Manufactured Nanoparticles
   Fourches, Denis, Pu, Dongqiuye, Tropsha, Alexander · 2011 · Combinatorial Chemistry & High Throughput Screening
9. Prediction of flow duration curves for ungauged basins
   Maya Atieh, Graham W. Taylor, Ahmed M. A. Sattar, Bahram Gharabaghi · 2017 · Journal of Hydrology
10. Machine Learning in Drug Discovery
    Günter Klambauer, Sepp Hochreiter, Matthias Rarey · 2019 · Journal of Chemical Information and Modeling
11. Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches
    Sherif Abdulkader Tawfik, Olexandr Isayev, Catherine Stampfl, Joseph George Shapter, David Alan Winkler, Michael J. Ford · 2018 · Advanced Theory and Simulations
12. Next generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated annealing method
    Seyyed Mohammad Mousavi, Elham Sadat Mostafavi, Pengcheng Jiao · 2017 · Energy Conversion and Management
13. Expert System for Predicting Reaction Conditions: The Michael Reaction Case
    Marcou, G., Aires de Sousa, J., Latino, D. A. R. S., de Luca, A., Horvath, D., Rietsch, V., Varnek, A. · 2015 · Journal of Chemical Information and Modeling
14. Inductive Transfer of Knowledge: Application of Multi-Task Learning and Feature Net Approaches to Model Tissue-Air Partition Coefficients
    Varnek, Alexandre, Gaudin, Cédric, Marcou, Gilles, Baskin, Igor, Pandey, Anil Kumar, Tetko, Igor V. · 2009 · Journal of Chemical Information and Modeling
15. Rationalizing Three-Dimensional Activity Landscapes and the Influence of Molecular Representations on Landscape Topology and the Formation of Activity Cliffs
    Peltason, Lisa, Iyer, Preeti, Bajorath, Jürgen · 2010 · Journal of Chemical Information and Modeling
16. The optimal one dimensional periodic table: a modified Pettifor chemical scale from data mining
    Henning Glawe, Antonio Sanna, Eberhard K. U. Gross · 2016 · New Journal of Physics
17. Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?
    Sheridan, Robert P. · 2019 · Journal of Chemical Information and Modeling
18. Knowledge-Based Approach to <i>de Novo</i> Design Using Reaction Vectors
    Patel, Hina, Bodkin, Michael J., Chen, Beining, Gillet, Valerie J. · 2009 · Journal of Chemical Information and Modeling
19. Characterization of Softwood and Hardwood LignoBoost Kraft Lignins with Emphasis on their Antioxidant Activity
    Jevgenija Ponomarenko, Tatiana Dizhbite, Māris Lauberts, Artūrs Vīksna, Gaļina Dobele, Oskars Bikovens, Галина Телышева · 2014 · BioResources
20. Application of Bayesian networks for hazard ranking of nanomaterials to support human health risk assessment
    Marvin, Hans J. P., Bouzembrak, Yamine, Janssen, Esmée M., van der Zande, Meike, Murphy, Finbarr, Sheehan, Barry, Mullins, Martin, Bouwmeester, Hans · 2017 · Nanotoxicology
21. AOP-DB: A database resource for the exploration of Adverse Outcome Pathways through integrated association networks
    Pittman, Maureen E., Edwards, Stephen W., Ives, Cataia, Mortensen, Holly M. · 2018 · Toxicology and Applied Pharmacology
22. QSAR Modeling and Prediction of Drug–Drug Interactions
    Alexey V. Zakharov, Ekaterina V. Varlamova, Alexey A. Lagunin, Alexander V. Dmitriev, Eugene N. Muratov, Denis Fourches, V. Е. Kuz’min, Vladimir Poroikov, Alexander Tropsha, Marc C. Nicklaus · 2015 · Molecular Pharmaceutics
23. Universal Approach for Structural Interpretation of QSAR/QSPR Models
    Polishchuk, Pavel G., Kuz'min, Victor E., Artemenko, Anatoly G., Muratov, Eugene N. · 2013 · Molecular Informatics
24. Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods
    Frank R. Burden, David Alan Winkler · 2009 · QSAR & Combinatorial Science
25. Chronic Obstructive Pulmonary Disease Phenotypes: Implications for Care
    Shireen Mirza, Roberto Benzo · 2017 · Mayo Clinic Proceedings
26. Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?
    Vinícius M. Alves, Eugene N. Muratov, Alexey V. Zakharov, N. N. MURATOV, Carolina Horta Andrade, Alexander Tropsha · 2017 · Food and Chemical Toxicology
27. The quest for new functionality
    Aron Walsh · 2015 · Nature Chemistry
28. Sparse feature selection methods identify unexpected global cellular response to strontium-containing materials
    Hélène Autefage, Eileen Gentleman, Elena Littmann, Martin A.B. Hedegaard, Thomas C. von Erlach, Matthew O’Donnell, Frank R. Burden, David Alan Winkler, Molly M. Stevens · 2015 · Proceedings of the National Academy of Sciences
29. Knowledge Discovery in Reaction Databases: Landscaping Organic Reactions by a Self-Organizing Neural Network
    Lingran Chen, Johann Gasteiger · 1997 · Journal of the American Chemical Society
30. What if the number of nanotoxicity data is too small for developing predictive Nano-QSAR models? An alternative read-across based approach for filling data gaps
    Gajewicz, Agnieszka · 2017 · Nanoscale
31. QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids
    Ioana Oprisiu, Ekaterina V. Varlamova, Eugene N. Muratov, Anatoly G. Artemenko, Gilles Marcou, Павел Геннадьевич Полищук, V. Е. Kuz’min, Alexandre A. Varnek · 2012 · Molecular Informatics
32. Compositional optimization of hard-magnetic phases with machine-learning models
    Johannes J. Möller, Wolfgang Körner, Georg Krugel, Daniel F. Urban, Christian Elsässer · 2018 · Acta Materialia
33. The use of Gene Ontology terms for predicting highly-connected 'hub' nodes in protein-protein interaction networks
    Hsing, Michael, Byler, Kendall Grant, Cherkasov, Artem · 2008 · BMC Systems Biology
34. A Novel Method for Building Regression Tree Models for QSAR Based on Artificial Ant Colony Systems
    Izrailev, Sergei, Agrafiotis, Dimitris · 2001 · Journal of Chemical Information and Computer Sciences
35. AFLUX: The LUX materials search API for the AFLOW data repositories
    Frisco Rose, Cormac Toher, Eric Gossett, Corey Oses, Marco Buongiorno Nardelli, Marco Fornari, Stefano Curtarolo A · 2017 · Computational Materials Science
36. A REPRESENTATION TO APPLY USUAL DATA MINING TECHNIQUES TO CHEMICAL REACTIONS — ILLUSTRATION ON THE RATE CONSTANT OF <font>S</font> _{<font>N</font>} 2 REACTIONS IN WATER
    HOONAKKER, FRANK, LACHICHE, NICOLAS, VARNEK, ALEXANDRE, WAGNER, ALAIN · 2011 · International Journal on Artificial Intelligence Tools
37. <i>Per Aspera Ad Astra</i> : Application of Simplex Qsar Approach in Antiviral Research
    Muratov, Eugene N, Artemenko, Anatoly G, Varlamova, Ekaterina V, Polischuk, Pavel G, Lozitsky, Victor P, Fedchuk, Alla S, Lozitska, Regina L, Gridina, Tat’yana L, Koroleva, Ludmila S, Sil’nikov, Vladimir N et al. · 2010 · Future Medicinal Chemistry
38. Prediction of riprap stone size under overtopping flow using data-driven models
    Mohammad Najafzadeh, Mohammad Rezaie-Balf, Ali Tafarojnoruz · 2018 · International Journal of River Basin Management
39. Support-Vector-Machine-Based Ranking Significantly Improves the Effectiveness of Similarity Searching Using 2D Fingerprints and Multiple Reference Compounds
    Geppert, Hanna, Horváth, Tamás, Gärtner, Thomas, Wrobel, Stefan, Bajorath, Jürgen · 2008 · Journal of Chemical Information and Modeling
40. 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
41. Gene expression programming as a basis for new generation of electricity demand prediction models
    Seyyed Mohammad Mousavi, Elham Sadat Mostafavi, Fariba Hosseinpour · 2014 · Computers & Industrial Engineering
42. Gene expression models for prediction of dam breach parameters
    Ahmed M. A. Sattar · 2013 · Journal of Hydroinformatics
43. 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
44. Prediction of Timing of Watermain Failure Using Gene Expression Models
    Ahmed M. A. Sattar, Bahram Gharabaghi, Edward Arthur McBean · 2016 · Water Resources Management
45. Factors influencing temporal patterns in crime in a large American city: A predictive analytics perspective
    Towers, Sherry, Chen, Siqiao, Malik, Abish, Ebert, David · 2018 · PLOS ONE
46. Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform
    Vasyl Kovalishyn, Natalia B. Abramenko, Iryna Kopernyk · 2017 · Food and Chemical Toxicology
47. A comparative study on groundwater spring potential analysis based on statistical index, index of entropy and certainty factors models
    Enke Hou, Jiale Wang, Wei Chen · 2017 · Geocarto International
48. 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
49. FBA reveals guanylate kinase as a potential target for antiviral therapies against SARS-CoV-2
    Alina Renz, Lina Widerspick, Andreas Dräger · 2020 · Bioinformatics
50. Modelling human embryoid body cell adhesion to a combinatorial library of polymer surfaces
    Epa, V. Chandana, Yang, Jing, Mei, Ying, Hook, Andrew L., Langer, Robert, Anderson, Daniel G., Davies, Martyn C., Alexander, Morgan R., Winkler, David A. · 2012 · Journal of Materials Chemistry
51. Ties in Proximity and Clustering Compounds
    John David MacCuish, Christos A. Nicolaou, Norah E. MacCuish · 2000 · Journal of Chemical Information and Computer Sciences
52. Improved wheat yield and production forecasting with a moisture stress index, AVHRR and MODIS data
    Antonius G. T. SCHUT, D. J. Stephens, Richard Stovold, M.J. Adams, R. L. Craig · 2009 · Australian Journal of Agricultural Research
53. The effects of characteristics of substituents on toxicity of the nitroaromatics: HiT QSAR study
    V. Е. Kuz’min, Eugene N. Muratov, Anatoly G. Artemenko, Leonid Gorb, Mohammad Qasim, Jerzy Leszczyński · 2008 · Journal of Computer-Aided Molecular Design
54. Application of Random Forest and Multiple Linear Regression Techniques to QSPR Prediction of an Aqueous Solubility for Military Compounds
    Nikolay A. Kovdienko, Павел Геннадьевич Полищук, Eugene N. Muratov · 2010 · Molecular Informatics
55. Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches
    M. Ha, T. X. Trinh, Jang-Sik Choi, D. Maulina, H. Byun, T. Yoon · 2018 · Scientific Reports
56. Relevance Vector Machines: Sparse Classification Methods for QSAR
    Burden, Frank R., Winkler, David A. · 2015 · Journal of Chemical Information and Modeling
57. Automatized Assessment of Protective Group Reactivity: A Step Toward Big Reaction Data Analysis
    Lin, Arkadii I., Madzhidov, Timur I., Klimchuk, Olga, Nugmanov, Ramil I., Antipin, Igor S., Varnek, Alexandre · 2016 · Journal of Chemical Information and Modeling
58. In silico assessment of adverse drug reactions and associated mechanisms
    Ivanov, Sergey M., Lagunin, Alexey A., Poroikov, Vladimir V. · 2016 · Drug Discovery Today
59. New Ground-Motion Prediction Equations Using Multi Expression Programing
    Amir H. Alavi, Amir H. Gandomi, Minoo Modaresnezhad, Mehdi Mousavi · 2011 · Journal of Earthquake Engineering
60. Robust topological surface state in Kondo insulator SmB₆ thin films
    Yong, Jie, Jiang, Yeping, Usanmaz, Demet, Curtarolo, Stefano, Zhang, Xiaohang, Li, Linze, Pan, Xiaoqing, Shin, Jongmoon, Takeuchi, Ichiro, Greene, Richard L. · 2014 · Applied Physics Letters
61. A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms
    Lapins, Maris, Worachartcheewan, Apilak, Spjuth, Ola, Georgiev, Valentin, Prachayasittikul, Virapong, Nantasenamat, Chanin, Wikberg, Jarl E. S. · 2013 · PLoS ONE
62. Performance assessment of existing models to predict brittle failure modes of steel-to-timber connections loaded parallel-to-grain with dowel-type fasteners
    José Manuel Cabrero, Miguel Yurrita · 2018 · Engineering Structures
63. Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions
    2019 · Lecture notes in computer science
64. Toward Interpretable Machine Learning Models for Materials Discovery
    Mikulskis, Paulius, Alexander, Morgan R., Winkler, David Alan · 2019 · Advanced Intelligent Systems
65. Identification of individual structural fragments of N,N′-(bis-5-nitropyrimidyl)dispirotripiperazine derivatives for cytotoxicity and antiherpetic activity allows the prediction of new highly active compounds
    Anatoly G. Artemenko, Eugene N. Muratov, V. Е. Kuz’min, Nikolay A. Kovdienko, Alexander I. Hromov, Vadim Makarov, Olga Riabova, Peter Wutzler, Michaela Schmidtke · 2007 · Journal of Antimicrobial Chemotherapy
66. Perturbation-Theory and Machine Learning (PTML) Model for High-Throughput Screening of Parham Reactions: Experimental and Theoretical Studies
    Lorena Simón-Vidal, Oihane García‐Calvo, Uxue Oteo, Sonia Arrasate, Esther Lete, Nuria Sotomayor, Humberto González‐Díaz · 2018 · Journal of Chemical Information and Modeling
67. Indirect estimation of the ultimate bearing capacity of shallow foundations resting on rock masses
    Shervin Tajeri, Ehsan Sadrossadat, Jafar Bolouri Bazaz · 2015 · International Journal of Rock Mechanics and Mining Sciences
68. How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors
    Pogodin, Pavel V., Lagunin, Alexey A., Rudik, Anastasia V., Filimonov, Dmitry A., Druzhilovskiy, Dmitry S., Nicklaus, Mark C., Poroikov, Vladimir V. · 2018 · Frontiers in Chemistry
69. QSAR Modeling Using Large-Scale Databases: Case Study for HIV-1 Reverse Transcriptase Inhibitors
    Tarasova, Olga A., Urusova, Aleksandra F., Filimonov, Dmitry A., Nicklaus, Marc C., Zakharov, Alexey V., Poroikov, Vladimir V. · 2015 · Journal of Chemical Information and Modeling
70. QSAR Models for the Analysis of Bioresponse Data from Combinatorial Libraries of Biomaterials
    Smith, Jack R., Kholodovych, Vladyslav, Knight, Doyle, Welsh, William J., Kohn, Joachim · 2005 · QSAR &amp; Combinatorial Science
71. Proteochemometric modelling coupled to in silico target prediction: an integrated approach for the simultaneous prediction of polypharmacology and binding affinity/potency of small molecules
    Paricharak, Shardul, Cortés-Ciriano, Isidro, IJzerman, Adriaan P, Malliavin, Thérèse E, Bender, Andreas · 2015 · Journal of Cheminformatics
72. Regression Algorithms in Hyperspectral Data Analysis for Meat Quality Detection and Evaluation
    Tingtiao Pan, Da‐Wen Sun, Jun‐Hu Cheng · 2016 · Comprehensive Reviews in Food Science and Food Safety
73. On the use of machine learning techniques for the mechanical characterization of soft biological tissues
    Cilla, Myriam, Pérez‐Rey, Ignacio, Martínez, Miguel Angel, Peña, Estefania, Martínez, Javier · 2018 · International Journal for Numerical Methods in Biomedical Engineering
74. Environmental factors influencing snowfall and snowfall prediction in the Tianshan Mountains, Northwest China
    Xueting Zhang, Xuemei Li, Lanhai Li, Shan Zhang, Qirui Qin · 2018 · Journal of Arid Land
75. Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles
    Denis Fourches, Dongqiuye Pu, Liwen Li, Hongyu Zhou, Qingxin Mu, Gaoxing Su, Bing Yan, Alexander Tropsha · 2015 · Nanotoxicology
76. Application of Quantitative Structure–Activity Relationship Models of 5-HT_1A Receptor Binding to Virtual Screening Identifies Novel and Potent 5-HT_1A Ligands
    Luo, Man, Wang, Xiang Simon, Roth, Bryan L., Golbraikh, Alexander, Tropsha, Alexander · 2014 · Journal of Chemical Information and Modeling
77. Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells
    Yamane, Junko, Aburatani, Sachiyo, Imanishi, Satoshi, Akanuma, Hiromi, Nagano, Reiko, Kato, Tsuyoshi, Sone, Hideko, Ohsako, Seiichiroh, Fujibuchi, Wataru · 2016 · Nucleic Acids Research
78. Restricted attention to social cues in schizophrenia patients
    Alexandra S.C. Nikolaides, Susanne Miess, Isabella Auvera, Ralf Müller, Joachim Klosterkötter, Stephan Ruhrmann · 2016 · European Archives of Psychiatry and Clinical Neuroscience
79. Efficient Heuristics for Maximum Common Substructure Search
    Englert, Péter, Kovács, Péter · 2015 · Journal of Chemical Information and Modeling
80. Identifying potential endocrine disruptors among industrial chemicals and their metabolites – development and evaluation of in silico tools
    Aleksandra Rybacka, Christina Rudén, Igor V. Tetko, Patrik L. Andersson · 2015 · Chemosphere
81. Predictive Models for Kinetic Parameters of Cycloaddition Reactions
    Glavatskikh, Marta, Madzhidov, Timur, Horvath, Dragos, Nugmanov, Ramil, Gimadiev, Timur, Malakhova, Daria, Marcou, Gilles, Varnek, Alexandre · 2019 · Molecular Informatics
82. Machine Learning in Drug Discovery
    Hochreiter, Sepp, Klambauer, Guenter, Rarey, Matthias · 2018 · Journal of Chemical Information and Modeling
83. Toward structure-based predictive tools for the selection of chiral stationary phases for the chromatographic separation of enantiomers
    Robert P. Sheridan, Wes A. Schafer, Patrick Piras · 2016 · Journal of Chromatography A
84. Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors
    Capuzzi, Stephen J., Sun, Wei, Muratov, Eugene N., Martínez-Romero, Carles, He, Shihua, Zhu, Wenjun, Li, Hao, Tawa, Gregory, Fisher, Ethan G., Xu, Miao et al. · 2018 · Journal of Medicinal Chemistry
85. Evolutionary patterns and physicochemical properties explain macroinvertebrate sensitivity to heavy metals
    Egina Malaj, Guillaume Guénard, Ralf Bernhard Schäfer · 2016 · Ecological Applications
86. ToxCast EPA <i>in Vitro</i> to <i>in Vivo</i> Challenge: Insight into the Rank-I Model
    Novotarskyi, Sergii, Abdelaziz, Ahmed, Sushko, Yurii, Körner, Robert, Vogt, Joachim, Tetko, Igor V. · 2016 · Chemical Research in Toxicology
87. A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks
    Finbarr Murphy, Barry Sheehan, Martin Mullins, Hans Bouwmeester, H.J.P. Marvin, Yamine Bouzembrak, Anna Luisa Costa, Rasel Das, Vicki Stone, Syed A. M. Tofail · 2016 · Nanoscale Research Letters
88. Computational chemogenomics: Is it more than inductive transfer?
    J.B. Brown, Yasushi Okuno, Gilles Marcou, Alexandre A. Varnek, Dragos Horvath · 2014 · Journal of Computer-Aided Molecular Design
89. DeepCOP: deep learning-based approach to predict gene regulating effects of small molecules
    Woo, Godwin, Fernandez, Michael, Hsing, Michael, Lack, Nathan A, Cavga, Ayse Derya, Cherkasov, Artem · 2020 · Bioinformatics
90. Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data
    Hosseini, Reza, Newlands, Nathaniel, Dean, Charmaine, Takemura, Akimichi · 2015 · Remote Sensing
91. 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
92. Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver
    Money, Eric S., Barton, Lauren E., Dawson, Joseph, Reckhow, Kenneth H., Wiesner, Mark R. · 2014 · Science of The Total Environment
93. Chronic obstructive pulmonary disease phenotypes using cluster analysis of electronic medical records
    Vazquez Guillamet, Rodrigo, Ursu, Oleg, Iwamoto, Gary, Moseley, Pope L, Oprea, Tudor · 2018 · Health Informatics Journal
94. Multivariate analysis of ToF‐SIMS data using mass segmented peak lists
    Robert M. T. Madiona, Nicholas G. Welch, Stephanie B. Russell · 2018 · Surface and Interface Analysis
95. The ‘SAR Matrix’ method and its extensions for applications in medicinal chemistry and chemogenomics
    Disha Gupta‐Ostermann, Jürgen Bajorath · 2014 · F1000Research
96. Toward comprehension of multiple human cells uptake of engineered nano metal oxides: quantitative inter cell line uptake specificity (QICLUS) modeling
    Probir Kumar Ojha, Supratik Kar, Kunal Roy · 2018 · Nanotoxicology
97. Organic Reactions Classified by Neural Networks: Michael Additions, Friedel–Crafts Alkylations by Alkenes, and Related Reactions
    Chen, Lingran, Gasteiger, Johann · 1996 · Angewandte Chemie International Edition in English
98. An entrainment model for non‐uniform sediment
    Mohamed Elhakeem, Ahmed M. A. Sattar · 2015 · Earth Surface Processes and Landforms
99. Structure–reactivity modeling using mixture-based representation of chemical reactions
    Polishchuk, Pavel, Madzhidov, Timur, Gimadiev, Timur, Bodrov, Andrey, Nugmanov, Ramil, Varnek, Alexandre · 2017 · Journal of Computer-Aided Molecular Design
100. Multi-Parameter Optimization: Identifying High Quality Compounds with a Balance of Properties
     Matthew Segall · 2012 · Current Drug Metabolism
101. Physically interactive robotic technology for neuromotor rehabilitation
     Hogan, Neville, Krebs, Hermano I. · 2011 · Progress in Brain Research
102. Development of a model for quality evaluation of litchi fruit
     Ying Cao, Yueming Jiang, Haiyan Gao · 2014 · Computers and Electronics in Agriculture
103. Assessment of tautomer distribution using the condensed reaction graph approach
     Gimadiev, T. R., Madzhidov, T. I., Nugmanov, R. I., Baskin, I. I., Antipin, I. S., Varnek, A. · 2018 · Journal of Computer-Aided Molecular Design
104. Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix
     Madiona, Robert M. T., Bamford, Sarah E., Winkler, David A., Muir, Benjamin W., Pigram, Paul J. · 2018 · Analytical Chemistry
105. Post-High-Throughput Screening Analysis: An Empirical Compound Prioritization Scheme
     Oprea, Tudor I., Bologa, Cristian G., Edwards, Bruce S., Prossnitz, Eric R., Sklar, Larry A. · 2005 · SLAS Discovery
106. Predicting Bond Strength between FRP Plates and Concrete Sub-strate: Applications of GMDH and MNLR Approaches
     Seyed Mahmood Hamze‐Ziabari, Amir Yasavoli · 2017 · Journal of Advanced Concrete Technology
107. Expressed emotion as a predictor of the first psychotic episode — Results of the European prediction of psychosis study
     Theresa Katharina Haidl, Marlene Rosen, Frauke Schultze‐Lutter, Dorien H. Nieman, Susanne Eggers, Markus Heinimaa, Georg Juckel, Andreas Heinz, Anthony P. Morrison, DON H. LINSZEN et al. · 2018 · Schizophrenia Research
108. High-Throughput Assessment and Modeling of a Polymer Library Regulating Human Dental Pulp-Derived Stem Cell Behavior
     Soraya Rasi Ghaemi, Bahman Delalat, Stan Gronthos, Morgan R. Alexander, David Alan Winkler, Andrew L. Hook, Nicolas Hans Voelcker · 2018 · ACS Applied Materials & Interfaces
109. Sparse feature selection identifies H2A.Z as a novel, pattern-specific biomarker for asymmetrically self-renewing distributed stem cells
     Huh, Yang Hoon, Noh, Minsoo, Burden, Frank R., Chen, Jennifer C., Winkler, David A., Sherley, James L. · 2015 · Stem Cell Research
110. Self‐organizing superimposition algorithm for conformational sampling
     Fangqiang Zhu, Dimitris K. Agrafiotis · 2007 · Journal of Computational Chemistry
111. Practical Aspects of Computational Chemistry III
     Leszczynski, Jerzy, Shukla, Manoj K · 2014 · Springer eBooks
112. Modeling chemical reactions for drug design
     Gasteiger, Johann · 2007 · Journal of Computer-Aided Molecular Design
113. [The computerized prediction of the spectrum of biological activity of chemical compounds by their structural formula: the PASS system. Prediction of Activity Spectra for Substance].
     Dmitry A. Filimonov, Poroĭkov Vv, Karaicheva Ei · 1995 · PubMed
114. Corrigendum: Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem Activity Cliffs, and QSAR
     David Alan Winkler, Tu C. Le · 2017 · Molecular Informatics
115. Screening of rare-earth-lean intermetallic 1-11 and 1-11-X compounds of YNi9In2-type for hard-magnetic applications
     Wolfgang Körner, Georg Krugel, Daniel F. Urban · 2017 · Scripta Materialia
116. Development of “structure-property” models in nucleophilic substitution reactions involving azides
     Нугманов Рамиль Ирекович, Timur Madzhidov, Г.Р. Халиуллина · 2014 · Journal of Structural Chemistry
117. Use of genetic algorithm and neural network approaches for risk factor selection: A case study of West Nile virus dynamics in an urban environment
     Debarchana Ghosh, R. Guha · 2010 · Computers, Environment and Urban Systems
118. A new approach for modeling of flow number of asphalt mixtures
     Amir H. Alavi, Hassene Hasni, Imen Zaabar · 2016 · Archives of Civil and Mechanical Engineering
119. Development of a stage-dependent prognostic model to predict psychosis in ultra-high-risk patients seeking treatment for co-morbid psychiatric disorders
     Helga K. Ising, Stephan Ruhrmann, Nadine Burger, Judith Rietdijk, Sara Dragt, Rianne M. C. Klaassen, David P. G. van den Berg, Dorien H. Nieman, Nynke Boonstra, D.H. Linszen et al. · 2016 · Psychological Medicine
120. Molecular descriptor data explain market prices of a large commercial chemical compound library
     Jarosław Polański, Urszula Kucia, Roksana Duszkiewicz · 2016 · Scientific Reports
121. Genetic Programming Theory and Practice XII
     Rick Riolo, William P. Worzel, Mark E. Kotanchek · 2015 · Genetic and evolutionary computation
122. A novel protocol for assessment of aboveground biomass in rangeland environments
     Charity Mundava, A.G.T. Schut, Petra Helmholz · 2015 · The Rangeland Journal
123. SENSORY ANALYSIS OF RED WINES: DISCRIMINATION BY ADAPTIVE FUZZY PARTITION
     Nadège Piclin, Marco Pintore, Carmela Maria Lanza · 2008 · Journal of Sensory Studies
124. GENE EXPRESSION PROGRAMMING APPROACH TO COST ESTIMATION FORMULATION FOR UTILITY PROJECTS
     Neda Shahrara, Tahir Çelik, Amir H. Gandomi · 2017 · Journal of Civil Engineering and Management
125. Testing and Prediction of Material Compatibility of Biofuel Candidates with Elastomeric Materials
     Stefan Heitzig, Alexander Weinebeck, Hubertus Murrenhoff · 2015 · SAE international journal of fuels and lubricants
126. Forecasting experiments of a dynamical–statistical model of the sea surface temperature anomaly field based on the improved self-memorization principle
     Mei Hong, Xi Chen, Ren Zhang · 2018 · Ocean science
127. Venoms and Isolated Toxins from Snakes of Medical Impact in the Northeast Argentina: State of the Art. Potential Pharmacological Applications
     Garcia Denegri, María Emilia, Bustillo, Soledad, Gay, Claudia Carolina, Van De Velde, Andrea, Gomez, Gabriela, Echeverría, Silvina, Gauna Pereira, María Del Carmen, Maruñak, Silvana, Nuñez, Sandra, Bogado, Fabián et al. · 2019 · Current Topics in Medicinal Chemistry
128. 反強磁性Fe 1.03 Teの圧力誘起強磁性
     M. Bendele, A. Maisuradze, B. Roessli · 2013 · Physical Review B
129. Whole cell‐catalyzed transesterification of waste vegetable oil
     Man Xiao, Jeffrey Philip Obbard · 2010 · GCB Bioenergy
130. Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure–Activity Relationships
     Yen Low, Vinícius M. Alves, Denis Fourches · 2018 · Journal of Chemical Information and Modeling
131. Transfer RNA and aminoacyl-tRNA synthetases in cells of E. coli infected with phage MS2.
     Valdis Berzin, E Y Gren · 1973 · PubMed
132. Maize [Zea Mays (L.)] crop-nutrient response functions extrapolation for Sub-Saharan Africa
     Gebreyesus Brhane Tesfahunegn, Charles S. Wortmann · 2017 · Nutrient Cycling in Agroecosystems
133. Using Online Tool (iPrior) for Modeling ToxCast&amp;#8482; Assays Towards Prioritization of Animal Toxicity Testing
     Abdelaziz, Ahmed, Sushko, Yurii, Novotarskyi, Sergii, Korner, Robert, Brandmaier, Stefan, Tetko, Igor · 2015 · Combinatorial Chemistry &amp; High Throughput Screening
134. CHEMICAL AND BIOLOGICAL DESCRIPTOR INTEGRATION IMPROVES COMPUTATIONAL MODELING OF IN VIVO RAT TOXICITY.
     Cristian Bologa, Oleg Ursu, Liliana Halip · 2015 · PubMed
135. QSAR and Complex Network Recognition of miRNAs in Stem Cells
     Molina, Enrique, Uriarte, Eugenio, Santana, Lourdes, Matos, Maria, Borges, Fernanda · 2013 · Current Bioinformatics