Yasemin Yesiltepe

TL;DR: ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties—specifically NMR chemical shifts in this manuscript—via the open source, high-performance computational chemistry software, NWChem.

TL;DR: This study applies a variety of ligand- and structure-based assessment techniques used in standard drug discovery analyses to the deep learning-generated compounds, and presents twelve candidate antagonists that are not available in existing chemical databases to provide an example of what this type of workflow can achieve.

TL;DR: A customizable Python module, chespa, built to easily assess different chemical space definitions through clustering of compounds in these spaces and visualizing trends of these clusters is introduced.

TL;DR: In this article, a generative deep learning model has been applied to de novo drug design as a means to expand the amount of chemical space that can be explored for potential drug-like compounds, and the authors assess the application of the generative model to the N-methyl D-aspartate receptor (NMDAR) to achieve two primary objectives: (i) the creation and release of a comprehensive library of experimentally validated NMDAR phencyclidine (PCP) site antagonists to assist the drug discovery community and (ii) an analysis of both the advantages

TL;DR: SPECTRe as mentioned in this paper is a Python-based tool that provides all substructures in a given molecular structure, regardless of the molecule size, employing efficient enumeration and generation of sub-structures represented in a humanreadable SMILES format through the use of classical graph traversal (breadth-first and depth-first search) algorithms.