Francesca Grisoni

TL;DR: A review of the most prominent algorithmic concepts of explainable artificial intelligence, and forecasts future opportunities, potential applications as well as several remaining challenges is provided in this article. But, the review is limited to the use of deep learning for drug discovery.

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 study presents the first‐time prospective application of a deep learning model for designing new druglike compounds with desired activities and synthesized five top‐ranking compounds designed by the generative model.

TL;DR: This review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and dares a forecast of the future opportunities, potential applications, and remaining challenges.

TL;DR: In this study, different global measures of classification performances are compared by means of results achieved on an extended set of real multivariate datasets and a set of benchmark values based on different random classification scenarios are introduced.