Giuseppe Felice Mangiatordi

TL;DR: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches and the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.

TL;DR: A number of approaches used to build the Applicability Domain are surveyed, focusing on strategies based on: a) physico-chemical, b) structural and c) response domains, which represents the chemical space from which a model is derived and where a prediction is considered to be reliable.

TL;DR: A millimetre-sized field effect transistor capable of selective single-molecule Immunoglobulin-G detection is demonstrated and can revolutionize the current approach to protein detection.

TL;DR: The Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts are described, which follows the steps of the Collaborative Estrogen Recept Activity Prediction Project (CERAPP).

TL;DR: Among all of the functionals tested, the ωB97X, BMK, B1LYP, and PBE0-DH approaches are those providing a good error balance on all four trials and allow for some general considerations, such as those on the role of Hartree-Fock exchange in reaction barriers or the relation between structure and energetics.