Адылова Ф.Т., Давронов Р.Р. Компьютерные технологии в создании новых лекарств: виртуальный скрининг // Журнал теоретической и клинической медицины. – 2013. – № 5. – С. 51-55.

Адылова Ф.Т., Давронов Р.Р. Полифармакология: новые стратегии виртуального скрининга // Журнал теоретической и клинической медицины. – 2015. – № 1. – С. 53-57.

Адылова Ф.Т., Икрамов А.А. Новая парадигма описания химического пространства в компьютерных приложениях: миф или реальность? // Узбекский химический журнал. – 2016. – № 4. – С. 95-100.

Филимонов Д.А., Поройков В.В. Прогноз спектра биологической активности органических соединений // Российский химический журнал. – 2006. – Т. 50, № 2. – С. 66-75.

Maggiora G., Bajorath J. Chemical space networks: a powerful new paradigm for the description of chemical space // Journal of Computer-Aided Molecular Design. – 2014. – Vol. 28. – Issue 8. – Pp. 795-802. – DOI: 10.1007/s10822-014-9760-0.

Tanaka N. et. al. Small-world phenomena in chemical library networks: application to fragment-based drug discovery // Journal of Chemical Information and Modeling. – 2009. – Vol. 49. – Issue 8. – Pp. 2677-2686. – DOI: 10.1021/ci900123v.

Krein M.P., Sukumar N. Exploration of the topology of chemical spaces with network measures // The Journal of Physical Chemistry A. – 2011. – Vol. 115. – Issue 45. – Pp. 12905-12918. – DOI: 10.1021/jp204022u.

Wawer M. et. al. Structure-activity relationship anatomy by network-like similarity graphs and local structureactivity relationship indices // Journal of Medicinal Chemistry. – 2008. – Vol. 51. – Issue 19. – Pp. 6075-6084. – DOI: 10.1021/jm800867g.

Ripphausen P. et. al. Rationalizing the role of SAR tolerance for ligand-based virtual screening // Journal of Chemical Information and Modeling. – 2011. – Vol. 51. – Issue 4. – Pp. 837-842. – DOI: 10.1021/ci200064c.

Stumpfe D., Dimova D., Bajorath J. Composition and topology of activity cliff clusters formed by bioactive compounds // Journal of Chemical Information and Modeling. – 2014. – Vol. 54. – Issue 2. – Pp. 451-461. – DOI: 10.1021/ci400728r.

Cohen R., Havlin S. Scaling properties of complex networks and spanning trees // Handbook of large-scale random networks. – New York: Springer, 2009. – Pp. 143-169.

Man L. et. al. Comparative Analysis of QSAR-based vs. Chemical Similarity Based Predictors of GPCRs Binding Affinity // Molecular Informatics. – 2016. – Vol. 35. – Issue 1. – Pp. 36-41. – DOI: 10.1002/minf.201500038.

Golbraikh A., Tropsha A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection // Journal of Computer-Aided Molecular Design. – 2002. – Vol. 16. – Issue 5-6. – Pp. 357369.

Todd M. et. al. Does Rational Selection of Training and Test Sets Improve the Outcome of QSAR Modeling? // Journal of Chemical Information and Modeling. – 2012. – Vol. 52. – Issue 10. – Pp. 2570-2578.

Keiser M.J. Predicting new molecular targets for known drugs // Nature. – 2009. – No. 462(7270). – Pp. 175-181. – DOI: 10.1038/nature08506.

Yildirim M.A. et. al. Drug-target network // Nature Biotechnology. – 2007. – Vol. 25. – Pp. 1119-1126. – URL: https://goo.gl/4iP03k.