Graph Attention Network based mapping of knowledge relations between chemical spaces of Nuclear factor kappa B and Centella asiatica.

The confounding nature of the innate immunity target Nuclear Factor kappa B (NF-κB) and its interaction with Centella asiatica (CA) molecules necessitate the intervention of advanced technologies, such as deep learning methods. The integration of chemical space concepts with deep learning technologies is a new way of knowledge mapping used to explore drug-target interactions, especially in molecular libraries derived from traditional medicine based molecular sources. The current constraint of virtual screening for mechanistic target hunting is the use of a binary classification model that includes active and inactive molecules from in vitro experiments to explore drug-target interaction.

In-vivo studies on Transitmycin, a potent Mycobacterium tuberculosis inhibitor.

This study involves the in-vitro and in-vivo anti-TB potency and in-vivo safety of Transitmycin (TR) (PubChem CID:90659753)- identified to be a novel secondary metabolite derived from Streptomyces sp (R2). TR was tested in-vitro against drug resistant TB clinical isolates (n = 49). 94% of DR-TB strains (n = 49) were inhibited by TR at 10μg ml-1.

GPURFSCREEN: a GPU based virtual screening tool using random forest classifier.

Background: In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task.