Application of in Silico Fragment and Data Analysis (ISIDA) Approaches for the Design of Novel Hydroxamic Acids Targeting HDAC2 - Hai, Pham The; Oanh, Dao Thi Kim; Nga, Doan Viet; Tung, Bui Thanh; Huong, Le Thi Thu (VNU Journal of Science: Medical and Pharmaceutical Sciences; Vol 33 No 2DO - 10.25073/2588-1132/vnumps.4073, 2017)
Finding a new treatment for cancer is one of the most interested fields for pharmaceutical research worldwide. Enzyme histone deacetylase 2 (HDAC2), being a member of HDAC class I appear to be an important druggable target.This study focused on rational design of novel HDAC2 inhibitorsusing molecular descriptors derived from ISIDA fragmentor methodology. Quantitative structure-activity relationship was explored to develop mathematical models able to predict HDAC2 inhibitory bioactivity of acid hydroxamic derivatives. Multiple linear regression (MLR) algorithm implemented in STATISTICA 8.0 was used for model development. Consequently 3 QSAR models were obtained showing acceptable performance r 2 > 0.70 for further use. Based on these models 10 important fragments attributing to better inhibitory potency were identified. Finally several novel hydroxamic derivatives were designed and screened for HDAC2 inhibitory activity.