Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins (vol 21, pg 511, 2017)

• Type: Other Publications
• Date: 2017

Authors

Speck Planche, A (Author) Other; Natalia N D S Cordeiro (Author) FCUP

Other information

Authenticus ID: P-00M-FBJ

DOI: 10.1007/s11030-017-9766-3

Abstract (EN): Breast cancer is the most frequent cancer reported in women, being responsible for hundreds of thousands of deaths. Chemotherapy has proven to be effective against this malignant neoplasm depending on different biological factors such as the histopathology, grade, and stage, among others. However, breast cancer cells have become resistant to current chemotherapeutic regimens, urging the discovery of new anti-breast cancer drugs. Computational approaches have the potential to offer promising alternatives to accelerate the search for potent and versatile anti-breast cancer agents. In the present work, we introduce the first multitasking (mtk) computational model devoted to the in silico fragment-based design of new molecules with high inhibitory activity against 19 different proteins involved in breast cancer. The mtk-computational model was created from a dataset formed by 24,285 cases, and it exhibited accuracy around 93% in both training and prediction (test) sets. Several molecular fragments were extracted from the molecules present in the dataset, and their quantitative contributions to the inhibitory activities against all the proteins under study were calculated. The combined use of the fragment contributions and the physicochemical interpretations of the different molecular descriptors in the mtk-computational model allowed the design of eight new molecular entities not reported in our dataset. These molecules were predicted as potent multi-target inhibitors against all the proteins, and they exhibited a desirable druglikeness according to the Lipinski¿s rule of five and its variants. © 2017 Springer International Publishing Switzerland

Language: English

Type (Professor's evaluation): Scientific