Barry A. Bunin

TL;DR: This work leveraged public high-throughput screening data to experimentally validate a virtual screening approach employing Bayesian models built with bioactivity information as well as bioactivity and cytotoxicity information (dual-event model).

TL;DR: Open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery is discussed.

TL;DR: This study provides novel insights into the key 1D molecular descriptors, 2D chemical substructures and 3D pharmacophores which can be used to mine the chemistry space, prioritizing those molecules with a higher probability of activity against Mtb.

TL;DR: A computational approach was developed that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi to demonstrate how combining chemoinformatics and bioinformatics for T., cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked.

TL;DR: Cheminformatics analysis on in vitro small molecules screened against Mycobacterium tuberculosis indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive.