Quantum chemical QSAR models to predict the anticancer activity of nitrosoureas

• Main Authors: Ibrahim Ali , Noorbatcha, Mohd Salleh, Hamzah, Syed Osman Idid, Syed Zahir Idid
• Format: Conference or Workshop Item
• Language: English; English; English
• Published: 2010
• Subjects: TP248.13 Biotechnology
• Online Access: http://irep.iium.edu.my/4083/4/MSTC2010_IBRAHIM_acceptance_letter.pdf; http://irep.iium.edu.my/4083/6/MSTC_2010_PARALLEL_TECHNICAL_SESSION__PROG_Book.pdf; http://irep.iium.edu.my/4083/1/MSTC2010_nitrosourea_QSAR.pdf; http://irep.iium.edu.my/4083/
• Institution: Universiti Islam Antarabangsa Malaysia

Discovery of new anticancer drugs is a daunting task confronting the scientists for a long time. Computer aided methods can provide important insights in this direction. In this work we have investigated the anticancer activity of aliphatic nitrosoureas using quantum chemical quantitative structure activity relationship (qcQSAR) approach. In this method the structures of the active compounds are optimized using quantum chemical methods and several quantum chemical properties are calculated in the process. These properties are subjected to detailed statistical analysis and the selected descriptors are used in the construction of the quantitative structure activity relationship. Semi empirical quantum chemical RM1 (Recife Model 1) method was used for structure optimization, while Heuristic and Multi-Linear Regression (MLR) methods were applied to get the best QSAR correlation. Quantum chemical descriptors such as partially charged surface area, resonance energy between carbon and hydrogen in C-H bonds and topological descriptors such as Kier-Hall index have been found to influence the anticancer activity of the selected compounds. The QSAR correlation obtained in this work can be used to predict the anticancer activity of new drugs, without resorting to experimental studies. Keywords: anticancer activity, Recife Model 1 (RM1), quantum chemical quantitative structure activity relationship (qcQSAR); Multi-Linear Regression (MLR);