Novel BH4-BCL-2 Domain Antagonists Induce BCL-2-Mediated Apoptosis in Triple-Negative Breast Cancer.

Targeting the challenging tumors lacking explicit markers and predictors for chemosensitivity is one of the major impediments of the current cancer armamentarium. Triple-negative breast cancer (TNBC) is an aggressive and challenging molecular subtype of breast cancer, which needs astute strategies to achieve clinical success. The pro-survival B-cell lymphoma 2 (BCL-2) overexpression reported in TNBC plays a central role in deterring apoptosis and is a promising target.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery.

In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process.

Forging New Scaffolds from Old: Combining Scaffold Hopping and Hierarchical Virtual Screening for Identifying Novel Bcl-2 Inhibitors.

Background: Though virtual screening methods have proven to be potent in various instances, the technique is practically incomplete to quench the need of drug discovery process. Thus, the quest for novel designing approaches and chemotypes for improved efficacy of lead compounds has been intensified and logistic approaches such as scaffold hopping and hierarchical virtual screening methods were evolved. Till now, in all the previous attempts these two approaches were applied separately.

Importance of functional groups in predicting the activity of small molecule inhibitors for Bcl-2 and Bcl-xL.

Evasion of apoptosis owing to aberrant expression of Bcl-2 (B-cell lymphoma-2) anti-apoptotic proteins is a promising hallmark of cancer. These proteins are associated with resistance to chemotherapy and radiation. Currently available QSAR models are limited to a set of inhibitors corresponding to a particular chemical scaffold, and unified models are required to identify the differential specificity of diverse compounds toward inhibiting these targets.