logo
CONTENT TYPES

Figure 1Loading Img

The Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies

View Author Information
Sandia National Laboratories, P.O. Box 969, MS 9951, Livermore, California 94551
Department of Chemical Engineering, Tennessee Technological University, Box 5013, Cookeville, Tennessee 38502
Cite this: J. Chem. Inf. Comput. Sci. 2003, 43, 3, 707–720
Publication Date (Web):March 26, 2003
https://doi.org/10.1021/ci020345w
Copyright © 2003 American Chemical Society
Article Views
1468
Altmetric
-
Citations
LEARN ABOUT THESE METRICS

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

Read OnlinePDF (221 KB)

Abstract

We present a new descriptor named signature based on extended valence sequence. The signature of an atom is a canonical representation of the atom's environment up to a predefined height h. The signature of a molecule is a vector of occurrence numbers of atomic signatures. Two QSAR and QSPR models based on signature are compared with models obtained using popular molecular 2D descriptors taken from a commercially available software (Molconn-Z). One set contains the inhibition concentration at 50% for 121 HIV-1 protease inhibitors, while the second set contains 12865 octanol/water partitioning coefficients (Log P). For both data sets, the models created by signature performed comparable to those from the commercially available descriptors in both correlating the data and in predicting test set values not used in the parametrization. While probing signature's QSAR and QSPR performances, we demonstrates that for any given molecule of diameter D, there is a molecular signature of height hD+1, from which any 2D descriptor can be computed. As a consequence of this finding any QSAR or QSPR involving 2D descriptors can be replaced with a relationship involving occurrence number of atomic signatures.

*

 Corresponding author phone:  (925)294-1279; fax:  (925)294-3020; e-mail:  [email protected]

Cited By


This article is cited by 154 publications.

  1. Francois Berenger, Yoshihiro Yamanishi. Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain Included. Journal of Chemical Information and Modeling 2020, 60 (9) , 4376-4387. https://doi.org/10.1021/acs.jcim.9b01075
  2. Hongchao Ji, Hanzi Deng, Hongmei Lu, Zhimin Zhang. Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks. Analytical Chemistry 2020, 92 (13) , 8649-8653. https://doi.org/10.1021/acs.analchem.0c01450
  3. Sule Atahan-Evrenk, F. Betul Atalay. Prediction of Intramolecular Reorganization Energy Using Machine Learning. The Journal of Physical Chemistry A 2019, 123 (36) , 7855-7863. https://doi.org/10.1021/acs.jpca.9b02733
  4. Shunsuke Tamura, Tomoyuki Miyao, Kimito Funatsu. Development of R-Group Fingerprints Based on the Local Landscape from an Attachment Point of a Molecular Structure. Journal of Chemical Information and Modeling 2019, 59 (6) , 2656-2663. https://doi.org/10.1021/acs.jcim.9b00122
  5. Hongchao Ji, Yamei Xu, Hongmei Lu, Zhimin Zhang. Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification. Analytical Chemistry 2019, 91 (9) , 5629-5637. https://doi.org/10.1021/acs.analchem.8b05405
  6. Ruben Buendia, Thierry Kogej, Ola Engkvist, Lars Carlsson, Henrik Linusson, Ulf Johansson, Paolo Toccaceli, Ernst Ahlberg. Accurate Hit Estimation for Iterative Screening Using Venn–ABERS Predictors. Journal of Chemical Information and Modeling 2019, 59 (3) , 1230-1237. https://doi.org/10.1021/acs.jcim.8b00724
  7. Francois Berenger, Yoshihiro Yamanishi. A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data. Journal of Chemical Information and Modeling 2019, 59 (1) , 463-476. https://doi.org/10.1021/acs.jcim.8b00499
  8. Felix A. Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S. Schoenholz, George E. Dahl, Oriol Vinyals, Steven Kearnes, Patrick F. Riley, and O. Anatole von Lilienfeld . Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. Journal of Chemical Theory and Computation 2017, 13 (11) , 5255-5264. https://doi.org/10.1021/acs.jctc.7b00577
  9. Hongbin Yang, Jie Li, Zengrui Wu, Weihua Li, Guixia Liu, and Yun Tang . Evaluation of Different Methods for Identification of Structural Alerts Using Chemical Ames Mutagenicity Data Set as a Benchmark. Chemical Research in Toxicology 2017, 30 (6) , 1355-1364. https://doi.org/10.1021/acs.chemrestox.7b00083
  10. Vincent Libis, Baudoin Delépine, and Jean-Loup Faulon . Expanding Biosensing Abilities through Computer-Aided Design of Metabolic Pathways. ACS Synthetic Biology 2016, 5 (10) , 1076-1085. https://doi.org/10.1021/acssynbio.5b00225
  11. Jean-Philippe Métivier, Alban Lepailleur, Aleksey Buzmakov, Guillaume Poezevara, Bruno Crémilleux, Sergei O. Kuznetsov, Jérémie Le Goff, Amedeo Napoli, Ronan Bureau, and Bertrand Cuissart . Discovering Structural Alerts for Mutagenicity Using Stable Emerging Molecular Patterns. Journal of Chemical Information and Modeling 2015, 55 (5) , 925-940. https://doi.org/10.1021/ci500611v
  12. Behrooz Torabi Moghadam, Jonathan Alvarsson, Marcus Holm, Martin Eklund, Lars Carlsson, and Ola Spjuth . Scaling Predictive Modeling in Drug Development with Cloud Computing. Journal of Chemical Information and Modeling 2015, 55 (1) , 19-25. https://doi.org/10.1021/ci500580y
  13. Jonathan Alvarsson, Martin Eklund, Claes Andersson, Lars Carlsson, Ola Spjuth, and Jarl E. S. Wikberg . Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines. Journal of Chemical Information and Modeling 2014, 54 (11) , 3211-3217. https://doi.org/10.1021/ci500344v
  14. Ernst Ahlberg, Lars Carlsson, and Scott Boyer . Computational Derivation of Structural Alerts from Large Toxicology Data Sets. Journal of Chemical Information and Modeling 2014, 54 (10) , 2945-2952. https://doi.org/10.1021/ci500314a
  15. Jonathan Alvarsson, Martin Eklund, Ola Engkvist, Ola Spjuth, Lars Carlsson, Jarl E. S. Wikberg, and Tobias Noeske . Ligand-Based Target Prediction with Signature Fingerprints. Journal of Chemical Information and Modeling 2014, 54 (10) , 2647-2653. https://doi.org/10.1021/ci500361u
  16. Mats Eriksson, Hongming Chen, Lars Carlsson, J. Willem M. Nissink, John G. Cumming, and Ingemar Nilsson . Beyond the Scope of Free-Wilson Analysis. 2: Can Distance Encoded R-Group Fingerprints Provide Interpretable Nonlinear Models?. Journal of Chemical Information and Modeling 2014, 54 (4) , 1117-1128. https://doi.org/10.1021/ci500075q
  17. Hongming Chen, Lars Carlsson, Mats Eriksson, Peter Varkonyi, Ulf Norinder, and Ingemar Nilsson . Beyond the Scope of Free-Wilson Analysis: Building Interpretable QSAR Models with Machine Learning Algorithms. Journal of Chemical Information and Modeling 2013, 53 (6) , 1324-1336. https://doi.org/10.1021/ci4001376
  18. Nishanth G. Chemmangattuvalappil and Mario R. Eden . A Novel Methodology for Property-Based Molecular Design Using Multiple Topological Indices. Industrial & Engineering Chemistry Research 2013, 52 (22) , 7090-7103. https://doi.org/10.1021/ie302516v
  19. Pablo Carbonell, Lars Carlsson, and Jean-Loup Faulon . Stereo Signature Molecular Descriptor. Journal of Chemical Information and Modeling 2013, 53 (4) , 887-897. https://doi.org/10.1021/ci300584r
  20. Aurélie de Luca, Dragos Horvath, Gilles Marcou, Vitaly Solov’ev, and Alexandre Varnek . Mining Chemical Reactions Using Neighborhood Behavior and Condensed Graphs of Reactions Approaches. Journal of Chemical Information and Modeling 2012, 52 (9) , 2325-2338. https://doi.org/10.1021/ci300149n
  21. Shawn Martin . Lattice Enumeration for Inverse Molecular Design Using the Signature Descriptor. Journal of Chemical Information and Modeling 2012, 52 (7) , 1787-1797. https://doi.org/10.1021/ci3001748
  22. Pu Liu, Dimitris K. Agrafiotis, and Dmitrii N. Rassokhin . Power Keys: A Novel Class of Topological Descriptors Based on Exhaustive Subgraph Enumeration and their Application in Substructure Searching. Journal of Chemical Information and Modeling 2011, 51 (11) , 2843-2851. https://doi.org/10.1021/ci200282z
  23. Ola Spjuth, Martin Eklund, Ernst Ahlberg Helgee, Scott Boyer, and Lars Carlsson . Integrated Decision Support for Assessing Chemical Liabilities. Journal of Chemical Information and Modeling 2011, 51 (8) , 1840-1847. https://doi.org/10.1021/ci200242c
  24. Oleg Ursu and Tudor I. Oprea. Model-Free Drug-Likeness from Fragments. Journal of Chemical Information and Modeling 2010, 50 (8) , 1387-1394. https://doi.org/10.1021/ci100202p
  25. David Rogers and Mathew Hahn . Extended-Connectivity Fingerprints. Journal of Chemical Information and Modeling 2010, 50 (5) , 742-754. https://doi.org/10.1021/ci100050t
  26. Lars Carlsson, Ernst Ahlberg Helgee and Scott Boyer . Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data. Journal of Chemical Information and Modeling 2009, 49 (11) , 2551-2558. https://doi.org/10.1021/ci9002206
  27. Ernst Ahlberg Helgee, Lars Carlsson and Scott Boyer. A Method for Automated Molecular Optimization Applied to Ames Mutagenicity Data. Journal of Chemical Information and Modeling 2009, 49 (11) , 2559-2563. https://doi.org/10.1021/ci900221r
  28. Andreas Bender, Dmitri Mikhailov, Meir Glick, Josef Scheiber, John W. Davies, Stephen Cleaver, Stephen Marshall, John A. Tallarico, Edmund Harrington, Ivan Cornella-Taracido and Jeremy L. Jenkins . Use of Ligand Based Models for Protein Domains To Predict Novel Molecular Targets and Applications To Triage Affinity Chromatography Data. Journal of Proteome Research 2009, 8 (5) , 2575-2585. https://doi.org/10.1021/pr900107z
  29. Frank R. Burden, Mitchell J. Polley and David A. Winkler . Toward Novel Universal Descriptors: Charge Fingerprints. Journal of Chemical Information and Modeling 2009, 49 (3) , 710-715. https://doi.org/10.1021/ci800290h
  30. Cornel Catana. Simple Idea to Generate Fragment and Pharmacophore Descriptors and Their Implications in Chemical Informatics. Journal of Chemical Information and Modeling 2009, 49 (3) , 543-548. https://doi.org/10.1021/ci800339p
  31. Huixiao Hong, Qian Xie, Weigong Ge, Feng Qian, Hong Fang, Leming Shi, Zhenqiang Su, Roger Perkins and Weida Tong . Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics. Journal of Chemical Information and Modeling 2008, 48 (7) , 1337-1344. https://doi.org/10.1021/ci800038f
  32. Ana G. Maldonado,, Jean-Pierre Doucet,, Michel Petitjean, and, Bo-Tao Fan. MolDiA:  A Novel Molecular Diversity Analysis Tool. 1. Principles and Architecture. Journal of Chemical Information and Modeling 2007, 47 (6) , 2197-2207. https://doi.org/10.1021/ci700120v
  33. W. Michael Brown,, Shawn Martin,, Mark D. Rintoul, and, Jean-Loup Faulon. Designing Novel Polymers with Targeted Properties Using the Signature Molecular Descriptor. Journal of Chemical Information and Modeling 2006, 46 (2) , 826-835. https://doi.org/10.1021/ci0504521
  34. Derick C. Weis,, Jean-Loup Faulon,, Richard C. LeBorne, and, Donald P. Visco, Jr.. The Signature Molecular Descriptor. 5. The Design of Hydrofluoroether Foam Blowing Agents Using Inverse-QSAR. Industrial & Engineering Chemistry Research 2005, 44 (23) , 8883-8891. https://doi.org/10.1021/ie050330y
  35. Richard A. Lewis. A General Method for Exploiting QSAR Models in Lead Optimization. Journal of Medicinal Chemistry 2005, 48 (5) , 1638-1648. https://doi.org/10.1021/jm049228d
  36. Andreas Bender,, Hamse Y. Mussa, and, Robert C. Glen, , Stephan Reiling. Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D):  Evaluation of Performance. Journal of Chemical Information and Computer Sciences 2004, 44 (5) , 1708-1718. https://doi.org/10.1021/ci0498719
  37. Jean-Loup Faulon,, Michael J. Collins, and, Robert D. Carr. The Signature Molecular Descriptor. 4. Canonizing Molecules Using Extended Valence Sequences. Journal of Chemical Information and Computer Sciences 2004, 44 (2) , 427-436. https://doi.org/10.1021/ci0341823
  38. Andreas Bender,, Hamse Y. Mussa, and, Robert C. Glen, , Stephan Reiling. Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier. Journal of Chemical Information and Computer Sciences 2004, 44 (1) , 170-178. https://doi.org/10.1021/ci034207y
  39. Jean-Loup Faulon and, Carla J. Churchwell, , Donald P. Visco, Jr.. The Signature Molecular Descriptor. 2. Enumerating Molecules from Their Extended Valence Sequences. Journal of Chemical Information and Computer Sciences 2003, 43 (3) , 721-734. https://doi.org/10.1021/ci020346o
  40. Pavel Polishchuk. CReM: chemically reasonable mutations framework for structure generation. Journal of Cheminformatics 2020, 12 (1) https://doi.org/10.1186/s13321-020-00431-w
  41. Tuan Le, Robin Winter, Frank Noé, Djork-Arné Clevert. Neuraldecipher – reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures. Chemical Science 2020, 11 (38) , 10378-10389. https://doi.org/10.1039/D0SC03115A
  42. Abdulelah S. Alshehri, Rafiqul Gani, Fengqi You. Deep learning and knowledge-based methods for computer-aided molecular design—toward a unified approach: State-of-the-art and future directions. Computers & Chemical Engineering 2020, 141 , 107005. https://doi.org/10.1016/j.compchemeng.2020.107005
  43. Shahin Ahmadi, Alla P. Toropova, Andrey A. Toropov. Correlation intensity index: mathematical modeling of cytotoxicity of metal oxide nanoparticles. Nanotoxicology 2020, 42 , 1-9. https://doi.org/10.1080/17435390.2020.1808252
  44. Junichi Taira, Tomohiro Umei, Keitaro Inoue, Mitsuru Kitamura, Francois Berenger, James C. Sacchettini, Hiroshi Sakamoto, Shunsuke Aoki. Improvement of the novel inhibitor for Mycobacterium enoyl-acyl carrier protein reductase (InhA): a structure–activity relationship study of KES4 assisted by in silico structure-based drug screening. The Journal of Antibiotics 2020, 73 (6) , 372-381. https://doi.org/10.1038/s41429-020-0293-6
  45. Cindy Vallieres, Andrew L. Hook, Yinfeng He, Valentina Cuzzucoli Crucitti, Grazziela Figueredo, Catheryn R. Davies, Laurence Burroughs, David A. Winkler, Ricky D. Wildman, Derek J. Irvine, Morgan R. Alexander, Simon V. Avery. Discovery of (meth)acrylate polymers that resist colonization by fungi associated with pathogenesis and biodeterioration. Science Advances 2020, 6 (23) , eaba6574. https://doi.org/10.1126/sciadv.aba6574
  46. Kai Cong Cheng, Zhi Sheng Khoo, Newton Well Lo, Wei Jie Tan, Nishanth G. Chemmangattuvalappil. Design and performance optimisation of detergent product containing binary mixture of anionic-nonionic surfactants. Heliyon 2020, 6 (5) , e03861. https://doi.org/10.1016/j.heliyon.2020.e03861
  47. Kirridharhapany T. Radhakrishnapany, Chee Yan Wong, Fang Khai Tan, Jia Wen Chong, Raymond R. Tan, Kathleen B. Aviso, Jose Isagani B. Janairo, Nishanth G. Chemmangattuvalappil. Design of fragrant molecules through the incorporation of rough sets into computer-aided molecular design. Molecular Systems Design & Engineering 2020, 12 https://doi.org/10.1039/D0ME00067A
  48. Paulius Mikulskis, Morgan R. Alexander, David Alan Winkler. Toward Interpretable Machine Learning Models for Materials Discovery. Advanced Intelligent Systems 2019, 1 (8) , 1900045. https://doi.org/10.1002/aisy.201900045
  49. Kristijan Vukovic, Domenico Gadaleta, Emilio Benfenati. Methodology of aiQSAR: a group-specific approach to QSAR modelling. Journal of Cheminformatics 2019, 11 (1) https://doi.org/10.1186/s13321-019-0350-y
  50. Francois Berenger, Kam Y. J. Zhang, Yoshihiro Yamanishi. Chemoinformatics and structural bioinformatics in OCaml. Journal of Cheminformatics 2019, 11 (1) https://doi.org/10.1186/s13321-019-0332-0
  51. Yang Su, Zihao Wang, Saimeng Jin, Weifeng Shen, Jingzheng Ren, Mario R. Eden. An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures. AIChE Journal 2019, 65 (9) https://doi.org/10.1002/aic.16678
  52. Samuel Lampa, Martin Dahlö, Jonathan Alvarsson, Ola Spjuth. SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines. GigaScience 2019, 8 (5) https://doi.org/10.1093/gigascience/giz044
  53. Jonathan J. Chen, Lyndsey N. Schmucker, Donald P. Visco. Identifying de‐NEDDylation inhibitors: Virtual high‐throughput screens targeting SENP8. Chemical Biology & Drug Design 2019, 93 (4) , 590-604. https://doi.org/10.1111/cbdd.13457
  54. Jonathan J. Chen, Lyndsey N. Schmucker, Donald P. Visco. Virtual high-throughput screens identifying hPK-M2 inhibitors: Exploration of model extrapolation. Computational Biology and Chemistry 2019, 78 , 317-329. https://doi.org/10.1016/j.compbiolchem.2018.12.006
  55. Ioana Oprisiu, Susanne Winiwarter. In Silico ADME Modeling. 2019,,https://doi.org/10.1016/B978-0-12-801238-3.11532-6
  56. Sule Atahan-Evrenk. A quantitative structure–property study of reorganization energy for known p-type organic semiconductors. RSC Advances 2018, 8 (70) , 40330-40337. https://doi.org/10.1039/C8RA07866A
  57. Laeeq Ahmed, Valentin Georgiev, Marco Capuccini, Salman Toor, Wesley Schaal, Erwin Laure, Ola Spjuth. Efficient iterative virtual screening with Apache Spark and conformal prediction. Journal of Cheminformatics 2018, 10 (1) https://doi.org/10.1186/s13321-018-0265-z
  58. Maris Lapins, Staffan Arvidsson, Samuel Lampa, Arvid Berg, Wesley Schaal, Jonathan Alvarsson, Ola Spjuth. A confidence predictor for logD using conformal regression and a support-vector machine. Journal of Cheminformatics 2018, 10 (1) https://doi.org/10.1186/s13321-018-0271-1
  59. Samuel Lampa, Jonathan Alvarsson, Staffan Arvidsson Mc Shane, Arvid Berg, Ernst Ahlberg, Ola Spjuth. Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction. Frontiers in Pharmacology 2018, 9 https://doi.org/10.3389/fphar.2018.01256
  60. Jonathan J. Chen, Lyndsey N. Schmucker, Donald P. Visco. Identifying new clotting factor XIa inhibitors in virtual high‐throughput screens using PCA‐GA‐SVM models and signature. Biotechnology Progress 2018, 34 (6) , 1553-1565. https://doi.org/10.1002/btpr.2693
  61. Jonathan Chen, Lyndsey Schmucker, Donald Visco. Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s. Biomolecules 2018, 8 (2) , 24. https://doi.org/10.3390/biom8020024
  62. Irene Luque Ruiz, Miguel Ángel Gómez Nieto. A new data representation based on relative measurements and fingerprint patterns for the development of QSAR regression models. Chemometrics and Intelligent Laboratory Systems 2018, 176 , 53-65. https://doi.org/10.1016/j.chemolab.2018.03.007
  63. Chihae Yang, James F. Rathman, Aleksey Tarkhov, Oliver Sacher, Thomas Kleinoeder, Jie Liu, Thomas Magdziarz, Aleksandra Mostraq, Joerg Marusczyk, Darshan Mehta, Christof Schwab, Bruno Bienfait. Chemoinformatics in Modern Regulatory Science. 2018,,, 439-470. https://doi.org/10.1002/9783527806539.ch8
  64. O. Anatole von Lilienfeld. Quantum Machine Learning im chemischen Raum. Angewandte Chemie 2018, 130 (16) , 4235-4240. https://doi.org/10.1002/ange.201709686
  65. O. Anatole von Lilienfeld. Quantum Machine Learning in Chemical Compound Space. Angewandte Chemie International Edition 2018, 57 (16) , 4164-4169. https://doi.org/10.1002/anie.201709686
  66. B. Christopher Rinderspacher, Jennifer M. Elward. Enriched optimization of molecular properties under constraints: an electrochromic example. Molecular Systems Design & Engineering 2018, 3 (3) , 485-495. https://doi.org/10.1039/C7ME00126F
  67. Egon L. Willighagen, John W. Mayfield, Jonathan Alvarsson, Arvid Berg, Lars Carlsson, Nina Jeliazkova, Stefan Kuhn, Tomáš Pluskal, Miquel Rojas-Chertó, Ola Spjuth, Gilleain Torrance, Chris T. Evelo, Rajarshi Guha, Christoph Steinbeck. The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. Journal of Cheminformatics 2017, 9 (1) https://doi.org/10.1186/s13321-017-0220-4
  68. Jonathan Jun Feng Chen, Donald P. Visco. Identifying novel factor XIIa inhibitors with PCA-GA-SVM developed vHTS models. European Journal of Medicinal Chemistry 2017, 140 , 31-41. https://doi.org/10.1016/j.ejmech.2017.08.056
  69. Claus Bendtsen, Andrea Degasperi, Ernst Ahlberg, Lars Carlsson. Improving machine learning in early drug discovery. Annals of Mathematics and Artificial Intelligence 2017, 81 (1-2) , 155-166. https://doi.org/10.1007/s10472-017-9541-2
  70. Paolo Toccaceli, Ilia Nouretdinov, Alexander Gammerman. Conformal prediction of biological activity of chemical compounds. Annals of Mathematics and Artificial Intelligence 2017, 81 (1-2) , 105-123. https://doi.org/10.1007/s10472-017-9556-8
  71. Pavel Polishchuk, Timur Madzhidov, Timur Gimadiev, Andrey Bodrov, Ramil Nugmanov, Alexandre Varnek. Structure–reactivity modeling using mixture-based representation of chemical reactions. Journal of Computer-Aided Molecular Design 2017, 31 (9) , 829-839. https://doi.org/10.1007/s10822-017-0044-3
  72. Tomoyuki Miyao, Kimito Funatsu. Finding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space. Molecular Informatics 2017, 36 (8) , 1700030. https://doi.org/10.1002/minf.201700030
  73. Xingang Fang, Sikha Bagui, Subhash Bagui. Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models. Computational Biology and Chemistry 2017, 69 , 110-119. https://doi.org/10.1016/j.compbiolchem.2017.05.007
  74. Matteo Floris, Giuseppa Raitano, Ricardo Medda, Emilio Benfenati. Fragment Prioritization on a Large Mutagenicity Dataset. Molecular Informatics 2017, 36 (7) , 1600133. https://doi.org/10.1002/minf.201600133
  75. Raghunathan Ramakrishnan, O. Anatole von Lilienfeld. Machine Learning, Quantum Chemistry, and Chemical Space. 2017,,, 225-256. https://doi.org/10.1002/9781119356059.ch5
  76. Jonathan Jun Feng Chen, Donald Patrick Visco Jr.. Developing an in silico pipeline for faster drug candidate discovery: Virtual high throughput screening with the Signature molecular descriptor using support vector machine models. Chemical Engineering Science 2017, 159 , 31-42. https://doi.org/10.1016/j.ces.2016.02.037
  77. Jennifer De León, Ana M. Velásquez, Bibian A. Hoyos. A stochastic method for asphaltene structure formulation from experimental data: avoidance of implausible structures. Physical Chemistry Chemical Physics 2017, 19 (15) , 9934-9944. https://doi.org/10.1039/C6CP06380B
  78. Tomoyuki Miyao, Kimito Funatsu, Jürgen Bajorath. Exploring differential evolution for inverse QSAR analysis. F1000Research 2017, 6 , 1285. https://doi.org/10.12688/f1000research.12228.1
  79. Nick D. Austin, Nikolaos V. Sahinidis, Daniel W. Trahan. Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques. Chemical Engineering Research and Design 2016, 116 , 2-26. https://doi.org/10.1016/j.cherd.2016.10.014
  80. Isidro Cortes-Ciriano. Bioalerts: a python library for the derivation of structural alerts from bioactivity and toxicity data sets. Journal of Cheminformatics 2016, 8 (1) https://doi.org/10.1186/s13321-016-0125-7
  81. Jonathan Alvarsson, Samuel Lampa, Wesley Schaal, Claes Andersson, Jarl E. S. Wikberg, Ola Spjuth. Large-scale ligand-based predictive modelling using support vector machines. Journal of Cheminformatics 2016, 8 (1) https://doi.org/10.1186/s13321-016-0151-5
  82. Samuel Lampa, Jonathan Alvarsson, Ola Spjuth. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. Journal of Cheminformatics 2016, 8 (1) https://doi.org/10.1186/s13321-016-0179-6
  83. Lorena Parlea, Eckart Bindewald, Rishabh Sharan, Nathan Bartlett, Daniel Moriarty, Jerome Oliver, Kirill A. Afonin, Bruce A. Shapiro. Ring Catalog: A resource for designing self-assembling RNA nanostructures. Methods 2016, 103 , 128-137. https://doi.org/10.1016/j.ymeth.2016.04.016
  84. Paolo Toccaceli, Ilia Nouretdinov, Alexander Gammerman. Conformal Predictors for Compound Activity Prediction. 2016,,, 51-66. https://doi.org/10.1007/978-3-319-33395-3_4
  85. Vikrant A. Dev, Nishanth G. Chemmangattuvalappil, Mario R. Eden. Multi-Objective Computer-Aided Molecular Design of Reactants and Products. 2016,,, 2055-2060. https://doi.org/10.1016/B978-0-444-63428-3.50347-7
  86. D.P. Visco, J.J. Chen. The Signature Molecular Descriptor in Molecular Design. 2016,,, 315-343. https://doi.org/10.1016/B978-0-444-63683-6.00011-3
  87. Robert H. Herring, Mario R. Eden. Evolutionary algorithm for de novo molecular design with multi-dimensional constraints. Computers & Chemical Engineering 2015, 83 , 267-277. https://doi.org/10.1016/j.compchemeng.2015.06.012
  88. O. Anatole von Lilienfeld, Raghunathan Ramakrishnan, Matthias Rupp, Aaron Knoll. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties. International Journal of Quantum Chemistry 2015, 115 (16) , 1084-1093. https://doi.org/10.1002/qua.24912
  89. Martin Eklund, Ulf Norinder, Scott Boyer, Lars Carlsson. The application of conformal prediction to the drug discovery process. Annals of Mathematics and Artificial Intelligence 2015, 74 (1-2) , 117-132. https://doi.org/10.1007/s10472-013-9378-2
  90. Srinidhi Varadharajan, Susanne Winiwarter, Lars Carlsson, Ola Engkvist, Ajay Anantha, Thierry Kogej, Markus Fridén, Jonna Stålring, Hongming Chen. Exploring In Silico Prediction of the Unbound Brain-to-Plasma Drug Concentration Ratio: Model Validation, Renewal, and Interpretation. Journal of Pharmaceutical Sciences 2015, 104 (3) , 1197-1206. https://doi.org/10.1002/jps.24301
  91. Vishwesh Venkatraman, Bjørn Kåre Alsberg. A quantitative structure-property relationship study of the photovoltaic performance of phenothiazine dyes. Dyes and Pigments 2015, 114 , 69-77. https://doi.org/10.1016/j.dyepig.2014.10.026
  92. Ernst Ahlberg, Ola Spjuth, Catrin Hasselgren, Lars Carlsson. Interpretation of Conformal Prediction Classification Models. 2015,,, 323-334. https://doi.org/10.1007/978-3-319-17091-6_27
  93. Vikrant A. Dev, Nishanth G. Chemmangattuvalappil, Mario R. Eden. Designing Reactants and Products with Properties Dependent on Both Structures. 2015,,, 1445-1450. https://doi.org/10.1016/B978-0-444-63577-8.50086-3
  94. Jennifer M. Elward, B. Christopher Rinderspacher. Smooth heuristic optimization on a complex chemical subspace. Physical Chemistry Chemical Physics 2015, 17 (37) , 24322-24335. https://doi.org/10.1039/C5CP02177D
  95. Alfred Fernández-Castané, Tamás Fehér, Pablo Carbonell, Cyrille Pauthenier, Jean-Loup Faulon. Computer-aided design for metabolic engineering. Journal of Biotechnology 2014, 192 , 302-313. https://doi.org/10.1016/j.jbiotec.2014.03.029
  96. Tomoyuki Miyao, Hiromasa Kaneko, Kimito Funatsu. Ring-System-Based Exhaustive Structure Generation for Inverse-QSPR/QSAR. Molecular Informatics 2014, 33 (11-12) , 764-778. https://doi.org/10.1002/minf.201400072
  97. Lu Tan, Johannes Kirchmair. Software for Metabolism Prediction. 2014,,, 27-52. https://doi.org/10.1002/9783527673261.ch02
  98. Jim Jing-Yan Wang, Xin Gao. Semi-supervised sparse coding. 2014,,, 1630-1637. https://doi.org/10.1109/IJCNN.2014.6889449
  99. Vikrant A. Dev, Nishanth G. Chemmangattuvalappil, Mario R. Eden. Reactant Structure Generation by Signature Descriptors and Real Coded Genetic Algorithm. 2014,,, 291-296. https://doi.org/10.1016/B978-0-444-63433-7.50033-X
  100. Vikrant A. Dev, Nishanth G. Chemmangattuvalappil, Mario R. Eden. Structure Generation of Candidate Reactants Using Signature Descriptors. 2014,,, 151-156. https://doi.org/10.1016/B978-0-444-63456-6.50026-0
  101. Laeeq Ahmed, Ake Edlund, Erwin Laure, Ola Spjuth. Using Iterative MapReduce for Parallel Virtual Screening. 2013,,, 27-32. https://doi.org/10.1109/CloudCom.2013.99
  102. John G. Cumming, Andrew M. Davis, Sorel Muresan, Markus Haeberlein, Hongming Chen. Chemical predictive modelling to improve compound quality. Nature Reviews Drug Discovery 2013, 12 (12) , 948-962. https://doi.org/10.1038/nrd4128
  103. B. Christopher Rinderspacher. Electro-optic and spectroscopic properties of push–pull-chromophores with non-aromatic π-bridges. Chemical Physics Letters 2013, 585 , 21-26. https://doi.org/10.1016/j.cplett.2013.08.082
  104. Grégoire Montavon, Matthias Rupp, Vivekanand Gobre, Alvaro Vazquez-Mayagoitia, Katja Hansen, Alexandre Tkatchenko, Klaus-Robert Müller, O Anatole von Lilienfeld. Machine learning of molecular electronic properties in chemical compound space. New Journal of Physics 2013, 15 (9) , 095003. https://doi.org/10.1088/1367-2630/15/9/095003
  105. Michał Rostkowski, Ola Spjuth, Patrik Rydberg. WhichCyp: prediction of cytochromes P450 inhibition. Bioinformatics 2013, 29 (16) , 2051-2052. https://doi.org/10.1093/bioinformatics/btt325
  106. Maris Lapins, Apilak Worachartcheewan, Ola Spjuth, Valentin Georgiev, Virapong Prachayasittikul, Chanin Nantasenamat, Jarl E. S. Wikberg, . A Unified Proteochemometric Model for Prediction of Inhibition of Cytochrome P450 Isoforms. PLoS ONE 2013, 8 (6) , e66566. https://doi.org/10.1371/journal.pone.0066566
  107. O. Anatole von Lilienfeld. First principles view on chemical compound space: Gaining rigorous atomistic control of molecular properties. International Journal of Quantum Chemistry 2013, 113 (12) , 1676-1689. https://doi.org/10.1002/qua.24375
  108. O. Spjuth, V. Georgiev, L. Carlsson, J. Alvarsson, A. Berg, E. Willighagen, J. E. S. Wikberg, M. Eklund. Bioclipse-R: integrating management and visualization of life science data with statistical analysis. Bioinformatics 2013, 29 (2) , 286-289. https://doi.org/10.1093/bioinformatics/bts681
  109. Rajarshi Guha. On Exploring Structure–Activity Relationships. 2013,,, 81-94. https://doi.org/10.1007/978-1-62703-342-8_6
  110. Nishanth G. Chemmangattuvalappil, Denny K.S. Ng. A systematic methodology for optimal product design in an integrated biorefinery. 2013,,, 91-96. https://doi.org/10.1016/B978-0-444-63234-0.50016-6
  111. Robert H. Herring, J. Colin Haser, Subin Hada, Mario R. Eden. Structure Based Design of Non-Peptide Mimetics. 2013,,, 175-180. https://doi.org/10.1016/B978-0-444-63234-0.50030-0
  112. Ulf Norinder, Maria E. Ek. QSAR investigation of NaV1.7 active compounds using the SVM/Signature approach and the Bioclipse Modeling platform. Bioorganic & Medicinal Chemistry Letters 2013, 23 (1) , 261-263. https://doi.org/10.1016/j.bmcl.2012.10.102
  113. Peter Ertl, Richard Lewis. IADE: a system for intelligent automatic design of bioisosteric analogs. Journal of Computer-Aided Molecular Design 2012, 26 (11) , 1207-1215. https://doi.org/10.1007/s10822-012-9609-3
  114. Xin Yan, Qiong Gu, Feng Lu, Jiabo Li, Jun Xu. GSA: a GPU-accelerated structure similarity algorithm and its application in progressive virtual screening. Molecular Diversity 2012, 16 (4) , 759-769. https://doi.org/10.1007/s11030-012-9403-0
  115. G. Marcou, D. Horvath, V. Solov'ev, A. Arrault, P. Vayer, A. Varnek. Interpretability of SAR/QSAR Models of any Complexity by Atomic Contributions. Molecular Informatics 2012, 31 (9) , 639-642. https://doi.org/10.1002/minf.201100136
  116. Martin Eklund, Ulf Norinder, Scott Boyer, Lars Carlsson. Benchmarking Variable Selection in QSAR. Molecular Informatics 2012, 31 (2) , 173-179. https://doi.org/10.1002/minf.201100142
  117. Martin Eklund, Ulf Norinder, Scott Boyer, Lars Carlsson. Application of Conformal Prediction in QSAR. 2012,,, 166-175. https://doi.org/10.1007/978-3-642-33412-2_17
  118. Nishanth G. Chemmangattuvalappil, Christopher B. Roberts, Mario R. Eden. Signature Descriptors for Process and Molecular Design in Reactive Systems. 2012,,, 1356-1360. https://doi.org/10.1016/B978-0-444-59506-5.50102-4
  119. Robert H. Herring, Rudolfs Namikis, Nishanth G. Chemmangattuvalappil, Christopher B. Roberts, Mario R. Eden. Molecular Design using Three-Dimensional Signature Descriptors. 2012,,, 225-229. https://doi.org/10.1016/B978-0-444-59507-2.50037-8
  120. Derick C. Weis, Douglas R. MacFarlane. Computer-Aided Molecular Design of Ionic Liquids: An Overview. Australian Journal of Chemistry 2012, 65 (11) , 1478. https://doi.org/10.1071/CH12344
  121. Kalai Vanii Jayaseelan, Pablo Moreno, Andreas Truszkowski, Peter Ertl, Christoph Steinbeck. Natural product-likeness score revisited: an open-source, open-data implementation. BMC Bioinformatics 2012, 13 (1) , 106. https://doi.org/10.1186/1471-2105-13-106
  122. Egon Willighagen, Roman Affentranger, Roland Grafström, Barry Hardy, Nina Jeliazkova, Ola Spjuth. Interactive predictive toxicology with Bioclipse and OpenTox. 2012,,, 35-61. https://doi.org/10.1533/9781908818249.35
  123. Milind Misra, Shawn Martin, Jean-Loup Faulon. Graphs: Flexible Representations of Molecular Structures and Biological Networks. 2011,,, 145-177. https://doi.org/10.1002/9781118131411.ch6
  124. Oleg Ursu, Anwar Rayan, Amiram Goldblum, Tudor I. Oprea. Understanding drug‐likeness. WIREs Computational Molecular Science 2011, 1 (5) , 760-781. https://doi.org/10.1002/wcms.52
  125. Diogo A. R. S. Latino, João Aires-de-Sousa. Classification of Chemical Reactions and Chemoinformatic Processing of Enzymatic Transformations. 2011,,, 325-340. https://doi.org/10.1007/978-1-60761-839-3_13
  126. Nishanth G. Chemmangattuvalappil, Charles C. Solvason, Susilpa Bommareddy, Mario R. Eden. Reverse problem formulation approach to molecular design using property operators based on signature descriptors. Computers & Chemical Engineering 2010, 34 (12) , 2062-2071. https://doi.org/10.1016/j.compchemeng.2010.07.009
  127. Genetha A. Gray, Pamela J. Williams, W. Michael Brown, Jean-Loup Faulon, Kenneth L. Sale. Disparate data fusion for protein phosphorylation prediction. Annals of Operations Research 2010, 174 (1) , 219-235. https://doi.org/10.1007/s10479-008-0347-9
  128. Tomoyuki Miyao, Masamoto Arakawa, Kimito Funatsu. Exhaustive Structure Generation for Inverse-QSPR/QSAR. Molecular Informatics 2010, 29 (1-2) , 111-125. https://doi.org/10.1002/minf.200900038
  129. Tomasz Puzyn, Agnieszka Gajewicz, Danuta Leszczynska, Jerzy Leszczynski. Nanomaterials – the Next Great Challenge for Qsar Modelers. 2010,,, 383-409. https://doi.org/10.1007/978-1-4020-9783-6_14
  130. Alexandre Varnek. Fragment Descriptors in Structure–Property Modeling and Virtual Screening. 2010,,, 213-243. https://doi.org/10.1007/978-1-60761-839-3_9
  131. Nishanth G. Chemmangattuvalappil, Charles C. Solvason, Susilpa Bommareddy, Mario R. Eden. Molecular Signature Descriptors for Integrated Flowsheet and Molecular Design. 2010,,, 1267-1272. https://doi.org/10.1016/S1570-7946(10)28212-3
  132. William WL Wong, Forbes J Burkowski. A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-QSAR problem. Journal of Cheminformatics 2009, 1 (1) https://doi.org/10.1186/1758-2946-1-4
  133. Tomasz Puzyn, Danuta Leszczynska, Jerzy Leszczynski. Toward the Development of âNano-QSARsâ: Advances and Challenges. Small 2009, 5 (22) , 2494-2509. https://doi.org/10.1002/smll.200900179
  134. . Bibliography. 2009,,, 1-241. https://doi.org/10.1002/9783527628766.biblio
  135. Tomasz Puzyn, Danuta Leszczynska, Jerzy Leszczynski. Quantitative Structure–Activity Relationships (QSARs) in the European REACH System: Could These Approaches be Applied to Nanomaterials?. 2009,,, 201-216. https://doi.org/10.1007/978-90-481-2687-3_9
  136. Nishanth G. Chemmangattuvalappil, Charles C. Solvason, Susilpa Bommareddy, Mario R. Eden. Incorporating Molecular Signature Descriptors in Reverse Problem Formulations. 2009,,, 73-78. https://doi.org/10.1016/S1570-7946(09)70233-0
  137. Nishanth G. Chemmangattuvalappil, Charles C. Solvason, Susilpa Bommareddy, Mario R. Eden. Novel Molecular Design Technique Using Property Operators Based on Signature Descriptors. 2009,,, 897-902. https://doi.org/10.1016/S1570-7946(09)70370-0
  138. Joshua D. Jackson, Derick C. Weis, Donald P. Visco Jr. Potential Glucocorticoid Receptor Ligands with Pulmonary Selectivity Using I-QSAR with the Signature Molecular Descriptor. Chemical Biology & Drug Design 2008, 72 (6) , 540-550. https://doi.org/10.1111/j.1747-0285.2008.00732.x
  139. Derick C. Weis, Donald P. Visco, Jean-Loup Faulon. Data mining PubChem using a support vector machine with the Signature molecular descriptor: Classification of factor XIa inhibitors. Journal of Molecular Graphics and Modelling 2008, 27 (4) , 466-475. https://doi.org/10.1016/j.jmgm.2008.08.004
  140. Eckart Bindewald, Calvin Grunewald, Brett Boyle, Mary O’Connor, Bruce A. Shapiro. Computational strategies for the automated design of RNA nanoscale structures from building blocks using NanoTiler. Journal of Molecular Graphics and Modelling 2008, 27 (3) , 299-308. https://doi.org/10.1016/j.jmgm.2008.05.004
  141. Shawn Martin, W. Michael Brown, Jean-Loup Faulon. Using Product Kernels to Predict Protein Interactions. 2007,,, 215-245. https://doi.org/10.1007/10_2007_084
  142. E. L. Willighagen, R. Wehrens, L. M. C. Buydens. Molecular Chemometrics. Critical Reviews in Analytical Chemistry 2006, 36 (3-4) , 189-198. https://doi.org/10.1080/10408340600969601
  143. Jörg K. Wegner, Holger Fröhlich, Holger M. Mielenz, Andreas Zell. Data and Graph Mining in Chemical Space for ADME and Activity Data Sets. QSAR & Combinatorial Science 2006, 25 (3) , 205-220. https://doi.org/10.1002/qsar.200510009
  144. W. Michael Brown, Shawn Martin, Joseph P. Chabarek, Charlie Strauss, Jean-Loup Faulon. Prediction of β-strand packing interactions using the signature product. Journal of Molecular Modeling 2006, 12 (3) , 355-361. https://doi.org/10.1007/s00894-005-0052-4
  145. Ana G. Maldonado, J. P. Doucet, Michel Petitjean, Bo-Tao Fan. Molecular similarity and diversity in chemoinformatics: From theory to applications. Molecular Diversity 2006, 10 (1) , 39-79. https://doi.org/10.1007/s11030-006-8697-1
  146. Jean-Loup Faulon, W. Michael Brown, Shawn Martin. Reverse engineering chemical structures from molecular descriptors: how many solutions?. Journal of Computer-Aided Molecular Design 2005, 19 (9-10) , 637-650. https://doi.org/10.1007/s10822-005-9007-1
  147. A. Varnek, D. Fourches, F. Hoonakker, V. P. Solov’ev. Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures. Journal of Computer-Aided Molecular Design 2005, 19 (9-10) , 693-703. https://doi.org/10.1007/s10822-005-9008-0
  148. S. Martin, D. Roe, J.-L. Faulon. Predicting protein-protein interactions using signature products. Bioinformatics 2005, 21 (2) , 218-226. https://doi.org/10.1093/bioinformatics/bth483
  149. Lutz Weber. . QSAR & Combinatorial Science 2005,,, 809. https://doi.org/10.1002/qsar.200510120
  150. John M. Prausnitz, Frederico W. Tavares. Thermodynamics of fluid-phase equilibria for standard chemical engineering operations. AIChE Journal 2004, 50 (4) , 739-761. https://doi.org/10.1002/aic.10069
  151. Carla J Churchwell, Mark D Rintoul, Shawn Martin, Donald P Visco, Archana Kotu, Richard S Larson, Laurel O Sillerud, David C Brown, Jean-Loup Faulon. The signature molecular descriptor. Journal of Molecular Graphics and Modelling 2004, 22 (4) , 263-273. https://doi.org/10.1016/j.jmgm.2003.10.002
  152. Jean-Loup Faulon, Donald P. Jr. Visco, Ramdas S. Pophale. The Signature Molecular Descriptor. Part 1. Using Extended Valence Sequences in QSAR and QSPR Studies.. ChemInform 2003, 34 (33) https://doi.org/10.1002/chin.200333232
  153. Rajni Garg, Barun Bhhatarai. QSAR and Molecular Modeling Studies of HIV Protease Inhibitors. ,,, 181-271. https://doi.org/10.1007/7081_038
  154. S. Martin, W.M. Brown, J.-L. Faulon, D. Weis, D. Visco. Inverse Design of Large Molecules using Linear Diophantine Equations. ,,, 11-16. https://doi.org/10.1109/CSBW.2005.79

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

Pair your accounts.

Export articles to Mendeley

Get article recommendations from ACS based on references in your Mendeley library.

You’ve supercharged your research process with ACS and Mendeley!

STEP 1:
Click to create an ACS ID

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

OOPS

You have to login with your ACS ID befor you can login with your Mendeley account.

MENDELEY PAIRING EXPIRED
Your Mendeley pairing has expired. Please reconnect

This website uses cookies to improve your user experience. By continuing to use the site, you are accepting our use of cookies. Read the ACS privacy policy.

CONTINUE