Pirhadi S, Sunseri J, Koes DR (2016) Open source molecular modeling. J Mol Graph Modell 69:127–43
Article
CAS
Google Scholar
Swiss Institute of Bioinformatics (2013) Click2Drug website. http://www.click2drug.org/. Accessed 18 Dec 2018
Daina A, Blatter MC, Baillie Gerritsen V, Palagi PM, Marek D, Xenarios I, Schwede T, Michielin O, Zoete V (2017) Drug design workshop: a web-based educational tool to introduce computer-aided drug design to the general public. J Chem Educ 94:335–344
Article
CAS
Google Scholar
Swiss Institute of Bioinformatics (2015) Drug Design Workshop website. www.drug-design-workshop.ch. Accessed 18 Dec 2018
Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E (2003) The Chemistry Development Kit (CDK): an open-source java library for chemo- and bioinformatics. J Chem Inf Comput Sci 43:493–500
Article
CAS
Google Scholar
Steinbeck C, Hoppe C, Kuhn S, Floris M, Guha R, Willighagen EL (2006) Recent developments of the Chemistry Development Kit (CDK)—an open-source java library for chemo- and bioinformatics. Curr Pharm Des 12:2111–20
Article
CAS
Google Scholar
May JW, Steinbeck C (2014) Efficient ring perception for the Chemistry Development Kit. J Cheminf 6:3
Article
Google Scholar
Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chertó M, Torrance G, Evelo CT, Guha R, Steinbeck C (2017) The Chemistry Development Kit (cdk) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminf 9:33
Article
Google Scholar
Chemistry Development Kit (2017) Chemistry Development Kit (CDK) website. https://cdk.github.io/, Accessed 18 Dec 2018
Jansen JM, Cornell W, Tseng YJ, Amaro RE (2012) Teach–Discover–Treat (TDT): collaborative computational drug discovery for neglected diseases. J Mol Graph Modell 38:360–2
Article
CAS
Google Scholar
Riniker S, Landrum GA, Montanari F, Villalba SD, Maier J, Jansen JM, Walters WP, Shelat AA (2017) Virtual-screening workflow tutorials and prospective results from the Teach–Discover–Treat competition 2014 against malaria. F1000Research 6:1136
Article
Google Scholar
Riniker S, Landrum GA, Montanari F, Villalba SD, Maier J, Jansen JM, Walters WP, Shelat AA (2017) Tutorial for the Teach–Discover–Treat (TDT) Competition 2014—Challenge 1: anti-malaria hit finding using classifier-fusion boosted predictive models. https://github.com/sriniker/TDT-tutorial-2014. Accessed 18 Dec 2018
Kluyver T, Ragan-Kelley B, Pérez F, Granger B, Bussonnier M, Frederic J, Kelley K, Hamrick J, Grout J, Corlay S, Ivanov P, Avila D, Abdalla S, Willing C, Team Jupyter Development (2016) Jupyter Notebooks—a publishing format for reproducible computational workflows. Agents and agendas. In: Loizides F, Schmidt B (eds) Positioning and power in academic publishing: players. IOS Press, Amsterdam, pp 87–90
Google Scholar
Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:1100–7
Article
Google Scholar
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–42
Article
CAS
Google Scholar
RDKit (2018) RDKit: Open-Source Cheminformatics, Version 2018.09.1. http://www.rdkit.org
Davies M, Nowotka M, Papadatos G, Dedman N, Gaulton A, Atkinson F, Bellis L, Overington JP (2015) ChEMBL web services: streamlining access to drug discovery data and utilities. Nucleic Acids Res 43:W612–W620
Article
CAS
Google Scholar
Gilpin W (2015) PyPDB: a Python API for the protein data bank. Bioinformatics 32:159–60
PubMed
Google Scholar
Raschka S (2017) BioPandas: working with molecular structures in pandas DataFrames. J Open Source Softw 2:279
Article
Google Scholar
Schrödinger L (2015) The PyMOL molecular graphics system. Version 1.8
Oliphant T (2006) A guide to NumPy. Trelgol Publishing
van der Walt S, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13(2):22–30
Article
Google Scholar
McKinney W (2010) Data structures for statistical computing in Python. In: van der Walt S, Millman J (eds) Proceedings of the 9th Python in science conference, pp 51–56
McKinney W (2011) pandas: a foundational Python library for data analysis and statistics
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Google Scholar
Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9:90–95
Article
Google Scholar
Waskom M (2018) seaborn v0.9.0
Continuum Analytics Inc (dba Anaconda Inc) (2017) conda. https://www.anaconda.com. Accessed 18 Dec 2018
Chen J, Zeng F, Forrester SJ, Eguchi S, Zhang MZ, Harris RC (2016) Expression and function of the epidermal growth factor receptor in physiology and disease. Physiol Rev 96:1025–1069
Article
CAS
Google Scholar
Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213
Article
CAS
Google Scholar
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:D668–D672
Article
CAS
Google Scholar
Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25
Article
CAS
Google Scholar
Brenk R, Schipani A, James D, Krasowski A, Gilbert IH, Frearson J, Wyatt PG (2008) Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem 3:435–444
Article
CAS
Google Scholar
Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53:2719–2740
Article
CAS
Google Scholar
Johnson MA, Maggiora GM (1990) Concepts and applications of molecular similarity, 1st edn. Wiley, New York
Google Scholar
Bender A, Glen RC (2004) Molecular similarity: a key technique in molecular informatics. Org Biomol Chem 2:3204
Article
CAS
Google Scholar
Bajorath J (2017) Representation and identification of activity cliffs. Expert Opin Drug Discov 12:879–883
Article
Google Scholar
Accelrys Inc, San Diego, CA, USA (2011) MACCS structural keys
Morgan HL (1965) The generation of a unique machine description for chemical structures—a technique developed at Chemical Abstracts Service. J Chem Doc 5:107–113
Article
CAS
Google Scholar
Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754
Article
CAS
Google Scholar
Maggiora G, Vogt M, Stumpfe D, Bajorath J (2014) Molecular similarity in medicinal chemistry. J Med Chem 57:3186–3204
Article
CAS
Google Scholar
Butina D (1999) Unsupervised data base clustering based on Daylight’s fingerprint and Tanimoto similarity: a fast and automated way to cluster small and large data sets. J Chem Inf and Model 39:747–750
CAS
Google Scholar
RDKit (2018) RDKFingerprint. http://rdkit.org/docs/source/rdkit.Chem.rdmolops.html. Accessed 18 Dec 2018
Raymond JW, Willett P (2002) Maximum common subgraph isomorphism algorithms for the matching of chemical structures. J Comput-Aided Mol Des 16:521–33
Article
CAS
Google Scholar
Dalke A, Hastings J (2013) FMCS: a novel algorithm for the multiple MCS problem. J Cheminf 5:O6
Article
Google Scholar
Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol 1. IEEE Comput Soc Press, Los Alamitos, California, pp 278–282
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Google Scholar
van Gerven M, Bohte S (2017) Editorial: artificial neural networks as models of neural information processing. Front Comput Neurosci 11:114
Article
Google Scholar
Wermuth CG, Ganellin CR, Lindberg P, Mitscher LA (1998) Glossary of terms used in medicinal chemistry (IUPAC Recommendations 1998). Pure Appl Chem 70:1129–1143
Article
CAS
Google Scholar
Seidel T, Wolber G, Murgueitio MS (2018) Pharmacophore perception and applications. Applied chemoinformatics. Wiley, Weinheim, pp 259–282
Chapter
Google Scholar
Macqueen J (1967) Some methods for classification and analysis of multivariate observations. In: 5th Berkeley symposium on mathematical statistics and probability, pp 281–297
Stiefl N (2016) 3D pharmacophores in the RDKit. https://github.com/rdkit/UGM_2016/blob/master/Notebooks/Stiefl_RDKitPh4FullPublication.ipynb. Accessed 18 Dec 2018
Kellenberger E, Schalon C, Rognan D (2008) How to measure the similarity between protein ligand-binding sites? Curr Comput-Aided Drug Des 4:209–220
Article
CAS
Google Scholar
Ehrt C, Brinkjost T, Koch O (2016) Impact of binding site comparisons on medicinal chemistry and rational molecular design. J Med Chem 59:4121–4151
Article
CAS
Google Scholar
Winger JA, Hantschel O, Superti-Furga G, Kuriyan J (2009) The structure of the leukemia drug imatinib bound to human quinone reductase 2 (NQO2). BMC Struct Biol 9:7
Article
Google Scholar