Breast cancer accounts for 9 % of global cases of cancer and represents the second most common of all types of cancer. It is only found in women and represents about 22.9 % of global cases of cancer in women. Pharmacologically inhibiting HSP 90 and a resultant inhibition of its client protein have been shown to be a great therapeutic target in the treatment of breast carcinoma because these inhibitors have the potential of suppressing multiple oncogenic signaling pathways. Sixty (60) compounds from literature work; 42 train and 18 test dataset compounds were selected for this study respectively. Best selected 3D physiochemical descriptors with significant positive contributions towards the development of the model includes; RDFM5, MoRSEN28, MoRSEC20, RDFM9, MoRSEV11, RDFC16, RDFE15, ASPAN, E3u, RDFC25, RDFU8, and MoRSEV13 which all contribute significantly to the bioactivity. MLR statistical regression analysis expressed potential predictive power compared to PLS method. The 3D QSAR model developed by the trained dataset with R2 = 0.97463, Q2 =0.900, Rm 2 =0.8654, and r2 = 0.6415 for external validation depicts the predictive potential of the 3D QSAR model. The 3D-QSAR model may lead to a better understanding of structural requirements of 8-arylsulfanyl, 8-arylsulfoxyl, and 8-arylsulfonyl adenines derivatives which could also aid in designing novel anti-carcinogenic molecules.
