Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works

LIBSVM: A library for support vector machines

Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918 · see citing works

A tutorial on support vector regression

Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works

Libsvm : A library for support vector machines

Ferrari, V. (2008) · Medical Entomology and Zoology · Cited by 10,124 · see citing works

PLS-regression: a basic tool of chemometrics

Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084 · see citing works

Extended-Connectivity Fingerprints

Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works

Partial least-squares regression: a tutorial

Geladi, P. L. M., & Kowalski, B. R. (1986) · Analytica Chimica Acta · doi:10.1016/0003-2670(86)80028-9 · Cited by 7,016 · see citing works

The price of innovation: new estimates of drug development costs

DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003) · Journal of Health Economics · doi:10.1016/s0167-6296(02)00126-1 · Cited by 4,265 · see citing works

Handbook of Molecular Descriptors

Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107 · see citing works

Beware of q2!

Golbraikh, A., & Tropsha, A. (2002) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(01)00123-1 · Cited by 3,735 · see citing works

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

Svetnik, V., Liaw, A. I., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034160g · Cited by 3,613 · see citing works

The problem of overfitting.

Hawkins, D. M. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0342472 · Cited by 2,522 · see citing works

<i>p</i>-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure

Hansch, C., & Fujita, T. (1964) · Journal of the American Chemical Society · doi:10.1021/ja01062a035 · Cited by 2,313 · see citing works

The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models

Tropsha, A., Gramatica, P., & Gombar, V. K. (2003) · QSAR & Combinatorial Science · doi:10.1002/qsar.200390007 · Cited by 2,195 · see citing works

Nonlinear neural networks: Principles, mechanisms, and architectures

Grossberg, S. (1988) · Neural Networks · doi:10.1016/0893-6080(88)90021-4 · Cited by 1,623 · see citing works

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

Eriksson, L., Jaworska, J., Worth, A. P., Cronin, M. T. D., McDowell, R. M., & Gramatica, P. (2003) · Environmental Health Perspectives · doi:10.1289/ehp.5758 · Cited by 1,325 · see citing works

Zur Theorie der Alkoholnarkose

Meyer, H. (1899) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/bf01834479 · Cited by 1,007 · see citing works

Quantitative approach to biochemical structure-activity relationships

Hansch, C. H. (1969) · Accounts of Chemical Research · doi:10.1021/ar50020a002 · Cited by 921 · see citing works

Inhibition of HIV-1 Replication by a Nonnucleoside Reverse Transcriptase Inhibitor

Merluzzi, V. J., Hargrave, K. D., Labadia, M. E., Grözinger, K. G., Skoog, M. T., Wu, J. C., Shih, C.-K., Eckner, K., Hattox, S. E., Adams, J., et al. (1990) · Science · doi:10.1126/science.1701568 · Cited by 744 · see citing works

New Chemical Descriptors Relevant for the Design of Biologically Active Peptides. A Multivariate Characterization of 87 Amino Acids

Sandberg, M., Eriksson, L., Jönsson, J., Sjöstróm, M., & Wold, S. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm9700575 · Cited by 679 · see citing works

Allosteric binding sites on cell-surface receptors: novel targets for drug discovery

Christopoulos, A. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd746 · Cited by 665 · see citing works

Molecular similarity: a key technique in molecular informatics

Bender, A., & Glen, R. C. (2004) · Organic & Biomolecular Chemistry · doi:10.1039/b409813g · Cited by 616 · see citing works

Advanced Lectures on Machine Learning

Bousquet, O., Rätsch, G., & Luxburg, U. (2004) · Lecture notes in computer science · doi:10.1007/b100712 · Cited by 565 · see citing works

Peptide quantitative structure-activity relationships, a multivariate approach

Hellberg, S., SJOESTROEM, M., Skagerberg, B., & Wold, S. (1987) · Journal of Medicinal Chemistry · doi:10.1021/jm00390a003 · Cited by 541 · see citing works

Comparison of descriptor spaces for chemical compound retrieval and classification

Wale, N., Watson, I. A., & Karypis, G. (2007) · Knowledge and Information Systems · doi:10.1007/s10115-007-0103-5 · Cited by 468 · see citing works

GRid-INdependent Descriptors (GRIND): A Novel Class of Alignment-Independent Three-Dimensional Molecular Descriptors

Pastor, M., Cruciani, G., McLay, I. M., Pickett, S. D., & Clementi, S. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm000941m · Cited by 466 · see citing works

Overfitting in Making Comparisons Between Variable Selection Methods

Reunanen, J., Guyon, I., & Elisseeff, A. (2003) · Cited by 420 · see citing works

Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs

Guha, R., & Drie, J. H. V. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci7004093 · Cited by 361 · see citing works

Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D):  Evaluation of Performance

Bender, A., Mussa, H. Y., Glen, R. C., & Reiling, S. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0498719 · Cited by 322 · see citing works

How Similar Are Similarity Searching Methods? A Principal Component Analysis of Molecular Descriptor Space

Bender, A., Jenkins, J. L., Scheiber, J., Sukuru, S. C. K., Glick, M., & Davies, J. W. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800249s · Cited by 294 · see citing works

Variable and subset selection in PLS regression

Höskuldsson, A. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(00)00113-1 · Cited by 283 · see citing works

Chemogenomic approaches to rational drug design

Rognan, D. (2007) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707307 · Cited by 265 · see citing works

Cross-validation as the objective function for variable-selection techniques

Baumann, K. (2003) · TrAC Trends in Analytical Chemistry · doi:10.1016/s0165-9936(03)00607-1 · Cited by 248 · see citing works

VALIDATE: A New Method for the Receptor-Based Prediction of Binding Affinities of Novel Ligands

Head, R. D., Smythe, M. L., Oprea, T. I., Waller, C. L., Green, S. M., & Marshall, G. R. (1996) · Journal of the American Chemical Society · doi:10.1021/ja9539002 · Cited by 247 · see citing works

Three-Dimensional Quantitative Similarity−Activity Relationships (3D QSiAR) from SEAL Similarity Matrices

Kubinyi, H., Hamprecht, F. A., & Mietzner, T. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970732a · Cited by 242 · see citing works

Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

Yao, X., Panaye, A. D., Doucet, J. P., et al. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049965i · Cited by 239 · see citing works

Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification

Wale, N., & Karypis, G. (2006) · Proceedings · doi:10.1109/icdm.2006.39 · Cited by 227 · see citing works

QSAR − How Good Is It in Practice? Comparison of Descriptor Sets on an Unbiased Cross Section of Corporate Data Sets

Gedeck, P., Rohde, B., & Bartels, C. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050413p · Cited by 219 · see citing works

A new set of amino acid descriptors and its application in peptide QSARs

Hu, M. Y., Liao, Z., Zhou, Y., & Li, S. Z. (2005) · Biopolymers · doi:10.1002/bip.20296 · Cited by 214 · see citing works

Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties

Obrezanova, O., Cśanyi, G., Gola, J. M. R., & Segall, M. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci7000633 · Cited by 214 · see citing works

How to Recognize and Workaround Pitfalls in QSAR Studies: A Critical Review

Scior, T., Medina-Franco, J. L., Do, Q.-T., Martinez-Mayorga, K., Yunes Rojas, J. A., & Bernard, P. (2009) · Current Medicinal Chemistry · doi:10.2174/092986709789578213 · Cited by 197 · see citing works

A Discussion of Measures of Enrichment in Virtual Screening: Comparing the Information Content of Descriptors with Increasing Levels of Sophistication

Bender, A., & Glen, R. C. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0500177 · Cited by 180 · see citing works

Megavariate analysis of environmental QSAR data. Part I – A basic framework founded on principal component analysis (PCA), partial least squares (PLS), and statistical molecular design (SMD)

Eriksson, L., Andersson, P. L., Johansson, E., et al. (2006) · Molecular Diversity · doi:10.1007/s11030-006-9024-6 · Cited by 162 · see citing works

MS-WHIM Scores for Amino Acids: A New 3D-Description for Peptide QSAR and QSPR Studies

Zaliani, A., & Gancia, E. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci980211b · Cited by 156 · see citing works

Chemogenomic approaches to drug discovery: similar receptors bind similar ligands

Klabunde, T. (2007) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0707308 · Cited by 154 · see citing works

Characterization of Activity Landscapes Using 2D and 3D Similarity Methods: Consensus Activity Cliffs

Medina‐Franco, J. L., Martínez‐Mayorga, K., Bender, A., Marín, R. M., Giulianotti, M. A., Pinilla, C., & Houghten, R. A. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800379q · Cited by 152 · see citing works

Alanine Scanning Mutagenesis of Aβ(1-40) Amyloid Fibril Stability

Williams, A. D., Shivaprasad, S., & Wetzel, R. B. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.01.041 · Cited by 152 · see citing works

Multivariate design and modeling in QSAR

Eriksson, L., & Johansson, E. (1996) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(96)00023-8 · Cited by 150 · see citing works

A Comparative Study on Feature Selection Methods for Drug Discovery

Liu, Y. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci049875d · Cited by 134 · see citing works

Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions

Lapinsh, M., Prusis, P., Gutcaits, A., Lundstedt, T., & Wikberg, J. E. S. (2001) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/s0304-4165(00)00187-2 · Cited by 134 · see citing works

Handbook of Chemoinformatics Algorithms

Faulon, J., & Bender, A. (2010) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420082999 · Cited by 122 · see citing works

What has computer-aided molecular design ever done for drug discovery?

Clark, D. E. (2006) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.1.2.103 · Cited by 113 · see citing works

Virtual screening of GPCRs: An in silico chemogenomics approach

Jacob, L., Hoffmann, B., Stoven, V., & Vert, J. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-363 · Cited by 106 · see citing works

Interpretable Numerical Descriptors of Amino Acid Space

Georgiev, A. G. (2009) · Journal of Computational Biology · doi:10.1089/cmb.2008.0173 · Cited by 101 · see citing works

Virtual Screen for Ligands of Orphan G Protein-Coupled Receptors

Bock, J. R., & Gough, D. A. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci050006d · Cited by 96 · see citing works

Chemogenomic Data Analysis: Prediction of Small-Molecule Targets and the Advent of Biological Fingerprints

Bender, A., Young, D. W., Jenkins, J. L., et al. (2007) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620707782507313 · Cited by 93 · see citing works

Biospectra Analysis: Model Proteome Characterizations for Linking Molecular Structure and Biological Response

Fliri, A. F. J., Loging, W., Thadeio, P. F., & Volkmann, R. A. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050494g · Cited by 92 · see citing works

Prediction of Aqueous Solubility and Partition Coefficient Optimized by a Genetic Algorithm Based Descriptor Selection Method

Wegner, J. K., & Zell, A. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034006u · Cited by 90 · see citing works

Proteochemometrics Modeling of the Interaction of Amine G-Protein Coupled Receptors with a Diverse Set of Ligands

Lapins, M., Prūsis, P., Lundstedt, T., & Wikberg, J. E. S. (2002) · Molecular Pharmacology · doi:10.1124/mol.61.6.1465 · Cited by 87 · see citing works

Development and Validation of a Novel Protein−Ligand Fingerprint To Mine Chemogenomic Space: Application to G Protein-Coupled Receptors and Their Ligands

Weill, N., & Rognan, D. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800447g · Cited by 86 · see citing works

Allosteric modulation of G protein-coupled receptors: perspectives and recent developments

Soudijn, W., van Wijngaarden, I., & IJzerman, A. P. (2004) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03220-9 · Cited by 83 · see citing works

Improved approach for proteochemometrics modeling: application to organic compound—amine G protein-coupled receptor interactions

Lapins, M., Prūsis, P., Uhlén, S., & Wikberg, J. E. S. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti703 · Cited by 81 · see citing works

Proteochemometric modeling of HIV protease susceptibility

Lapins, M., Eklund, M., Spjuth, O., Prūsis, P., & Wikberg, J. E. S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-181 · Cited by 77 · see citing works

Allosteric Modulation of the Adenosine Family of Receptors

Gao, Z., Kim, S., IJzerman, A. P., & Jacobson, K. A. (2005) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/1389557054023242 · Cited by 70 · see citing works

Interpretation of Nonlinear QSAR Models Applied to Ames Mutagenicity Data

Carlsson, L., Helgee, E. A., & Boyer, S. K. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci9002206 · Cited by 65 · see citing works

Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques

Lapins, M., & Wikberg, J. E. S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-339 · Cited by 63 · see citing works

Matrix Metalloproteinase Target Family Landscape: A Chemometrical Approach to Ligand Selectivity Based on Protein Binding Site Analysis

Pirard, B., & Matter, H. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm050363f · Cited by 60 · see citing works

Integrating Statistical Predictions and Experimental Verifications for Enhancing Protein-Chemical Interaction Predictions in Virtual Screening

Nagamine, N., Shirakawa, T., Minato, Y., Torii, K., Kobayashi, H., Imoto, M., & Sakakibara, Y. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000397 · Cited by 58 · see citing works

Assessment of Additive/Nonadditive Effects in Structure−Activity Relationships: Implications for Iterative Drug Design

Patel, Y., Gillet, V. J., Howe, T. J., Pastor, J., Oyarzábal, J., & Willett, P. (2008) · Journal of Medicinal Chemistry · doi:10.1021/jm801070q · Cited by 58 · see citing works

Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors

Geppert, H., Humrich, J., Stumpfe, D., et al. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900004a · Cited by 56 · see citing works

Proteochemometrics analysis of substrate interactions with dengue virus NS3 proteases

Prūsis, P., Lapins, M., Yahorava, S., Petrovska, R., Niyomrattanakit, P., Katzenmeier, G., & Wikberg, J. E. S. (2008) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2008.08.081 · Cited by 55 · see citing works

Ties in Proximity and Clustering Compounds

MacCuish, J. D., Nicolaou, C. A., & MacCuish, N. E. (2000) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci000069q · Cited by 54 · see citing works

PLS modeling of chimeric MS04/MSH-peptide and MC1/MC3-receptor interactions reveals a novel method for the analysis of ligand–receptor interactions

Prūsis, P., Muceniece, R., Andersson, P. M., Post, C., Lundstedt, T., & Wikberg, J. E. S. (2001) · Biochimica et Biophysica Acta (BBA) - Protein Structure and Molecular Enzymology · doi:10.1016/s0167-4838(00)00249-1 · Cited by 46 · see citing works

Prediction of indirect interactions in proteins

Prūsis, P., Uhlén, S., Petrovska, R., Lapins, M., & Wikberg, J. E. S. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-167 · Cited by 44 · see citing works

QSAR and Proteo-chemometric Analysis of the Interaction of a Series of Organic Compounds with Melanocortin Receptor Subtypes

Lapins, M., Prūsis, P., Mutule, I., Mutulis, F., & Wikberg, J. E. S. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm020945m · Cited by 43 · see citing works

Generalized modeling of enzyme–ligand interactions using proteochemometrics and local protein substructures

Strömbergsson, H., Kryshtafovych, A., Prūsis, P., Fidelis, K. A., Wikberg, J. E. S., Komorowski, J., & Hvidsten, T. R. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21163 · Cited by 43 · see citing works

A two‐entropies analysis to identify functional positions in the transmembrane region of class A G protein‐coupled receptors

Ye, K., Lameijer, E.-W., Beukers, M. W., et al. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20899 · Cited by 42 · see citing works

PSI: indexing protein structures for fast similarity search

Çamoǧlu, O., Kahveci, T., & Singh, A. K. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg1009 · Cited by 42 · see citing works

Multi-Assay-Based Structure−Activity Relationship Models: Improving Structure−Activity Relationship Models by Incorporating Activity Information from Related Targets

Ning, X., Rangwala, H., & Karypis, G. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci900182q · Cited by 41 · see citing works

Feature Selection for Descriptor Based Classification Models. 1. Theory and GA-SEC Algorithm

Wegner, J. K., Fröhlich, H., & Zell, A. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0342324 · Cited by 40 · see citing works

Proteochemometric Mapping of the Interaction of Organic Compounds with Melanocortin Receptor Subtypes

Lapins, M., Veikšina, S., Uhlén, S., Petrovska, R., Mutule, I., Mutulis, F., Yahorava, S., Prūsis, P., & Wikberg, J. E. S. (2004) · Molecular Pharmacology · doi:10.1124/mol.104.002857 · Cited by 39 · see citing works

Generalized Proteochemometric Model of Multiple Cytochrome P450 Enzymes and Their Inhibitors

Kontijevskis, A., Komorowski, J., & Wikberg, J. E. S. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci8000953 · Cited by 38 · see citing works

Fragment‐Based Drug Discovery: A Practical Approach. Edited by Edward R. Zartler and Michael J. Shapiro.

Rastelli, G. (2009) · ChemMedChem · doi:10.1002/cmdc.200900251 · Cited by 38 · see citing works

Biological data mining with neural networks: implementation and application of a flexible decision tree extraction algorithm to genomic problem domains

Browne, A., Hudson, B. D., Whitley, D. C., Ford, M. G., & Picton, P. D. (2003) · Neurocomputing · doi:10.1016/j.neucom.2003.10.007 · Cited by 37 · see citing works

Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space

Strömbergsson, H., Daniluk, P., Kryshtafovych, A., Fidelis, K. A., Wikberg, J. E. S., Kleywegt, G. J., & Hvidsten, T. R. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci800200e · Cited by 35 · see citing works

Rough set‐based proteochemometrics modeling of G‐protein‐coupled receptor‐ligand interactions

Strömbergsson, H., Prūsis, P., Midelfart, H., Lapins, M., Wikberg, J. E. S., & Komorowski, J. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20777 · Cited by 35 · see citing works

Peptide binding to the HLA-DRB1 supertype: A proteochemometrics analysis

Dimitrov, I., Garnev, P., Flower, D. R., & Doytchinova, I. A. (2009) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2009.09.049 · Cited by 34 · see citing works

Proteochemometric Recognition of Stable Kinase Inhibition Complexes Using Topological Autocorrelation and Support Vector Machines

Fernández, M., Ahmad, S., & Sarai, A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci1000532 · Cited by 33 · see citing works

Proteochemometric Modeling of Drug Resistance over the Mutational Space for Multiple HIV Protease Variants and Multiple Protease Inhibitors

Lapins, M., & Wikberg, J. E. S. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800453k · Cited by 31 · see citing works

Noncovalent Interactions: Defining Cooperativity. Ligand Binding Aided by Reduced Dynamic Behavior of Receptors. Binding of Bacterial Cell Wall Analogues to Ristocetin A

Williams, D. H., Davies, N. L., Zerella, R., et al. (2004) · Journal of the American Chemical Society · doi:10.1021/ja039409p · Cited by 31 · see citing works

Hierarchical PLS Modeling for Predicting the Binding of a Comprehensive Set of Structurally Diverse Protein−Ligand Complexes

Lindström, A., Pettersson, F. H., Almqvist, F., et al. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050323k · Cited by 30 · see citing works

Predicting Lipophilicity of Drug‐Discovery Molecules using Gaussian Process Models

Schroeter, T. S., Schwaighofer, A., Mika, S., et al. (2007) · ChemMedChem · doi:10.1002/cmdc.200700041 · Cited by 28 · see citing works

Elucidation of Structure−Activity Relationship Pathways in Biological Screening Data

Wawer, M. J., Peltason, L., & Bajorath, J. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm8014102 · Cited by 26 · see citing works

Fragment-Based Drug Discovery: A Practical Approach

Petros, A. M., & Hajduk, P. J. (2009) · Journal of the American Chemical Society · doi:10.1021/ja902461y · Cited by 26 · see citing works

A New Method to Estimate Ligand-Receptor Energetics

Bock, J. R., & Gough, D. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m200054-mcp200 · Cited by 26 · see citing works

Mining protein dynamics from sets of crystal structures using “consensus structures”

van Westen, G. J. P., Wegner, J. K., Bender, A., et al. (2010) · Protein Science · doi:10.1002/pro.350 · Cited by 21 · see citing works

Local descriptors of protein structure: A systematic analysis of the sequence‐structure relationship in proteins using short‐ and long‐range interactions

Hvidsten, T. R., Kryshtafovych, A., & Fidelis, K. A. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22296 · Cited by 20 · see citing works

Proteochemometric modelling of antibody-antigen interactions using SPOT synthesised peptide arrays

Mandrika, I., Prūsis, P., Yahorava, S., Shikhagaie, M., & Wikberg, J. E. S. (2007) · Protein Engineering Design and Selection · doi:10.1093/protein/gzm022 · Cited by 19 · see citing works

Proteochemometric modeling reveals the interaction site for Trp9 modified α‐MSH peptides in melanocortin receptors

Lapins, M., Prūsis, P., Petrovska, R., Uhlén, S., Mutule, I., Veikšina, S., & Wikberg, J. E. S. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21323 · Cited by 18 · see citing works

Towards Proteome–Wide Interaction Models Using the Proteochemometrics Approach

Strömbergsson, H., Lapins, M., Kleywegt, G. J., & Wikberg, J. E. S. (2010) · Molecular Informatics · doi:10.1002/minf.201000052 · Cited by 15 · see citing works

Proteochemometric analysis of small cyclic peptides' interaction with wild‐type and chimeric melanocortin receptors

Kontijevskis, A., Petrovska, R., Mutule, I., Uhlén, S., Komorowski, J., Prūsis, P., & Wikberg, J. E. S. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21461 · Cited by 15 · see citing works

Proteochemometrics mapping of the interaction space for retroviral proteases and their substrates

Kontijevskis, A., Petrovska, R., Yahorava, S., et al. (2009) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2009.05.045 · Cited by 13 · see citing works

Chemogenomics: Looking at biology through the lens of chemistry

Doddareddy, M. R., van Westen, G. J. P., van der Horst, E., Peironcely, J. E., Corthals, F., IJzerman, A. P., Emmerich, M., Jenkins, J. L., & Bender, A. (2009) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10046 · Cited by 11 · see citing works

Molecular Modeling of Melanocortin Receptors

Sun, H., & Fry, D. C. (2007) · Current Topics in Medicinal Chemistry · doi:10.2174/156802607780906573 · Cited by 6 · see citing works

Proteochemometric Modeling of the Inhibition Complexes of Matrix Metalloproteinases with N‐Hydroxy‐2‐[(Phenylsulfonyl)Amino]Acetamide Derivatives Using Topological Autocorrelation Interaction Matrix and Model Ensemble Averaging

Fernández, M., Fernández, L., Caballero, J., et al. (2008) · Chemical Biology & Drug Design · doi:10.1111/j.1747-0285.2008.00675.x · Cited by 5 · see citing works

QSAR without borders

Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works

A unified drug–target interaction prediction framework based on knowledge graph and recommendation system

Ye, Q., Hsieh, C.-Y., Yang, Z., Kang, Y., Chen, J., Cao, D., He, S., & Hou, T. (2021) · Nature Communications · doi:10.1038/s41467-021-27137-3 · Cited by 261 · see citing works

SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots

Moreira, I. S., Koukos, P. I., Melo, R., Almeida, J. G., Preto, A. J., Schaarschmidt, J., Trellet, M., Gümüş, Z. H., Costa, J., & Bonvin, A. M. J. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-08321-2 · Cited by 98 · see citing works

Proteochemometrics – recent developments in bioactivity and selectivity modeling

Bongers, B. J., IJzerman, A. P., & Van Westen, G. J. P. (2019) · Drug Discovery Today: Technologies · doi:10.1016/j.ddtec.2020.08.003 · Cited by 54 · see citing works

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, S., Cortés-Ciriano, I., IJzerman, A. P., Malliavin, T. E., & Bender, A. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0063-9 · Cited by 38 · see citing works

Study the QSAR/QSPR on Compound Molecules

Chen, L. (2004)

How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)

Dearden, J. C., Cronin, M. T. D., & Kaiser, K. L. E. (2009) · SAR and QSAR in Environmental Research · doi:10.1080/10629360902949567 · Cited by 468 · see citing works

Development of QSAR Model Based on the Key Molecular Descriptors Selection and Computational Toxicology for Prediction of Toxicity of PCBs

Kim, D., Lee, S., Kim, M., et al. (2016) · Korean Chemical Engineering Research · doi:10.9713/kcer.2016.54.5.621 · Cited by 4 · see citing works

Quantitative Structure-Activity Relationship (QSAR) Paradigm - Hansch Era to New Millennium

Debnath, A. K. (2001) · Mini-Reviews in Medicinal Chemistry · doi:10.2174/1389557013407061 · Cited by 58 · see citing works

Multi-dimensional QSAR Studies on Aurora-A kinase InhibitorsPreprint

Patil, R. B., & Tambe, S. (2020) · SSRN Electronic Journal · Cited by 1 · see citing works