Design, synthesis and evaluation of new thiazolidin-4-ones as LPA receptor antagonists for breast cancer therapy.

Breast cancer has been a leading cause of mortality among women worldwide in recent years. Targeting the lysophosphatidic acid (LPA)-LPA pathway using small molecules could improve breast cancer therapy. Thiazolidin-4-ones were developed and tested on MCF-7 cancer cells, and active compounds were analyzed for their effects on apoptosis, migration angiogenesis and LPA protein and gene expression.

Novel Antitubercular Agents: Design, Synthesis, Molecular Dynamic and Biological Studies of Pyrazole - 1,2,4-Triazole Conjugates.

Mycobacterium tuberculosis (Mtb) has numerous cell wall and non-cell wall mediated receptors for drug action, of which cell wall mediated targets were found to be more promising because of their pivotal role in bacterial protection and survival. Herein, we reported the design and synthesis of a series of pyrazole-linked triazoles based on the reported structural features of promising drug candidates that target DprE1 receptors through a Structure-based drug design (SBDD) approach (6a-6j and 7a-7j). The synthesized compounds were evaluated for their in-vitro antitubercular activity against virulent strains of Mtb H37Rv.

Restoration of p53 functions by suppression of mortalin-p53 sequestration: an emerging target in cancer therapy.

Functional inactivation of wild-type p53 is a major trait of cancerous cells. In many cases, such inactivation occurs by either gene mutations or due to overexpression of p53 binding partners. This review focuses on an overexpressed p53 binding partner called mortalin, a mitochondrial heat shock protein that sequesters both wild-type and mutant p53 in malignant cells due to changes in subcellular localization.

Machine learning and deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry.

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition.