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|
| Type |
Symposium |
| Area |
Recent Trends in Drug Discovery |
| Room No. |
Room 302 |
| Time |
THU 16:55-: |
| Code |
MEDI-3 |
| Subject |
Target-based drug discovery using Big Data Analytics and Computational Chemistry
(빅데이터 분석과 계산화학을 이용한 표적신약의 개발) |
| Authors |
Jay Liu
Department of Chemical Engineering, Pukyong National University, Korea |
| Abstract |
The targeted drug screening method proposed in this paper is based on the synergy effect of big data analytics and computational chemistry. It can provide screening results with higher reliability and has a wide range of application. The method was applied to the development of a targeted drug based on 45 sulphonamide derivatives (33 training compounds and 12 testing compounds) with carbonic anhydrase IX (CA IX) as a drug target. For each sulphonamide compound, about 5,000 molecular descriptors were calculated using quantum mechanics, and lipophilicity (logkw) and inhibitory activity (logKi) were experimentally measured as drug prop-erties. A quantitative structure-property-relationship (QSPR) model with high accuracy was obtained using genetic algorithm-partial least squares (GA-PLS) employing only 7 descriptors. Mean relative errors were 10.2% (logkw) and 7.07% (logKi), respectively for testing com-pounds. The ideal drug structure was obtained by inverting the QSPR model numerically in respect to properties of a reference drug (one of testing compounds). 12 testing compounds were ranked using the ideal drug structure and the rank was further validated through molecular dynamic simulation of a drug candidate-CA IX complex. Parameterization of MD simulation was verified experimentally using structural analyses of the complexes including MALDI-TOF/TOF-MS. |
| E-mail |
jayliu@pknu.ac.kr |
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121st General Meeting of the KCS