Applications of
Cheminformatics & Chemical Modelling
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Rajamani, R



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Ramkumar Rajamani, Bristol Myers Squibb Co.
Ramkumar Rajamani completed his PhD from the University of Minnesota in 2003 under the guidance of Professor Jiali Gao. The focus of his thesis was the use of combined QM/MM techniques to understand the structure and function of membrane proteins. Mechanistic details of the cyclization process of Squalene catalyzed by Squalene Hopene Cyclase and spectral properties of membrane proteins such as Bacteriorhodopsin and Rhodopsin were probed using combined QM/MM methods. Following his graduate studies he joined Johnson & Johnson PRD LLC in 2003 as a post-doctoral fellow under the guidance of Dr. Charles Reynolds. During his post-doctoral work he was involved in applying quantum mechanics-based methods to address issues related to drug discovery. The protonation state of the aspartyl protease beta-Secretase was studied using the linear scaling quantum mechanics package DIVCON. Use of QM derived charges was also explored through binding affinity prediction studies. Following his post-doctoral work at Johnson & Johnson, he joined the computer assisted drug discovery (CADD) group at BMS (Bristol Myers Squibb Co.) in Feb 2005 where he currently employs a range of computational tools in guiding the process of drug discovery.
Abstract
Use of Quantum Mechanics in the Process of Drug Discovery

Ramkumar Rajamani, Computer-Aided Drug Design, Bristol Myers Squibb Co., Wallingford, CT, USA

Application of quantum mechanical approaches in addressing issues of relevance to the drug discovery process will be discussed. This will be primarily highlighted through modeling of the active site of a therapeutically important aspartyl protease, beta-Secretase (BACE). The protonation state of the catalytically important aspartates (Asp 32 and Asp 228) of BACE in the presence of known potent inhibitors was studied to determine the precise location of the protons. The derived state was used in building binding affinity models based on the LIE (Linear Interaction Energy) approach capable of rank ordering BACE inhibitors.
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