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@@ -672,6 +672,38 @@
 	keywords = {Calibration-curve method, Electronic referencing, Metabolomics, NMR spectroscopy, Nuclear magnetic resonance (NMR), Pharmaceutical analysis, Quantitative NMR (qNMR), Referencing technique, Standard-addition method, Validation},
 	abstract = {This review illustrates the need to use nuclear magnetic resonance (NMR) spectroscopy for the quantitative analysis of small molecules in their crude forms and in mixtures. We provide the basic concepts of quantitative NMR (qNMR), a brief description of important acquisition and processing parameters responsible for obtaining high-quality, reproducible NMR spectra in order to maximize accuracy, and the latest referencing techniques used for quantitative analysis. We also describe methods that are used for quantitative analysis including calibration-curve and standard-addition methods. Further, we briefly address validation of qNMR spectroscopy and its major applications in various scientific disciplines.}
 }
+@article{cheminformatics,
+	author = {Wegner, J. K. and Sterling, A. and Guha, R. and Bender, A. and Faulon, J.-L. and Hastings, J. and O'Boyle, N. and Overington, J. and Van Vlijmen, H. and Willighagen, E.},
+	title = {Cheminformatics},
+	year = {2012},
+	issue_date = {November 2012},
+	publisher = {Association for Computing Machinery},
+	address = {New York, NY, USA},
+	volume = {55},
+	number = {11},
+	issn = {0001-0782},
+	url = {https://doi.org/10.1145/2366316.2366334},
+	doi = {10.1145/2366316.2366334},
+	abstract = {Open-source chemistry software and molecular databases broaden the research horizons of drug discovery.},
+	journal = {Commun. ACM},
+	month = nov,
+	pages = {65–75},
+	numpages = {11}
+}
+@article{cheminformatics2,
+	author = {Chen, H. and Kogej, T. and Engkvist, O.},
+	title = {Cheminformatics in Drug Discovery, an Industrial Perspective},
+	journal = {Molecular Informatics},
+	volume = {37},
+	number = {9-10},
+	pages = {1800041},
+	keywords = {Cheminformatics, Machine Learning, Library Design, High-Throughput Screening},
+	doi = {https://doi.org/10.1002/minf.201800041},
+	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/minf.201800041},
+	eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/minf.201800041},
+	abstract = {Abstract Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on “Big Data”, machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future. Traditional areas like virtual screening, library design and high-throughput screening analysis are highlighted in this review. Applying machine learning in drug discovery is an area that has become very important. Applications of machine learning in early drug discovery has been extended from predicting ADME properties and target activity to tasks like de novo molecular design and prediction of chemical reactions.},
+	year = {2018}
+}