Between Theory and Practice: Computational/Experimental Integrated Approaches to Understand the Solubility and Lipophilicity of PROTACs.

Emerging drug candidates more often fall in the beyond-rule-of-five chemical space. Among them, proteolysis targeting chimeras (PROTACs) have gained great attention in the past decade. Although physicochemical properties of small molecules accomplishing Lipinski's rule-of-five can now be easily predicted through models generated by large data collections, for PROTACs the knowledge is still limited and heterogeneous, hampering their prediction.

DReLAB - Deep REinforcement Learning Adversarial Botnet: A benchmark dataset for adversarial attacks against botnet Intrusion Detection Systems.

We present the first dataset that aims to serve as a benchmark to validate the resilience of botnet detectors against adversarial attacks. This dataset includes realistic adversarial samples that are generated by leveraging two widely used Deep Reinforcement Learning (DRL) techniques. These adversarial samples are proved to evade state of the art detectors based on Machine- and Deep-Learning algorithms.