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Here, we present IDNet, a user authentication framework from smartphone-acquired motion signals. Its goal is to recognize a target user from their way of walking, using the accelerometer and gyroscope (inertial) signals provided by a commercial smartphone worn in the front pocket of the user's trousers. IDNet features several innovations including: i) a robust and smartphone-orientation-independent walking cycle extraction block, ii) a novel feature extractor based on convolutional neural networks, iii) a one-class support vector machine to classify walking cycles, and the coherent integration of these into iv) a multi-stage authentication technique. IDNet is the first system that exploits a deep learning approach as universal feature extractors for gait recognition, and that combines classification results from subsequent walking cycles into a multi-stage decision making framework. Experimental results show the superiority of our approach against state-of-the-art techniques, leading to misclassification rates (either false negatives or positives) smaller than 0.15% with fewer than five walking cycles. Design choices are discussed and motivated throughout, assessing their impact on the user authentication performance.

Molecular hybridization is a well-exploited medicinal chemistry strategy that aims to combine two molecules (or parts of them) in a new, single chemical entity. Recently, it has been recognized as an effective approach to design ligands able to modulate multiple targets of interest. Hybrid compounds can be obtained by linking (presence of a linker) or framework integration (merging or fusing) strategies. Although very promising to combat the multifactorial nature of complex diseases, the development of molecular hybrids faces the critical issues of selecting the right target combination and the achievement of a balanced activity towards them, while maintaining drug-like-properties. In this review, we present recent case histories from our own research group that demonstrate why and how molecular hybridization can be carried out to address the challenges of multitarget drug discovery in two therapeutic areas that are Alzheimer’s and parasitic diseases. Selected examples spanning from linker- to fragment- based hybrids will allow to discuss issues and consequences relevant to drug design.

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