Dragos Horvath

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: This work demonstrates that the DMs based on an ensemble (consensus) model provide systematically better performance than other DMs and can be used to halve the cost of experimental measurements by providing a similar prediction accuracy.

TL;DR: The application of the ISIDA (In SIlico design and Data Analysis) software to perform virtual screening of large databases of compounds and reactions and to assess some ADME/Tox properties is illustrated.

TL;DR: Because PDFs are represented by a mixture of Gaussian functions, the Bhattacharyya kernel has been proposed as a measure of the overlap of datasets, which leads to an elegant method of global comparison of chemical libraries.

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).