In the past few years, de novo molecular design has increasingly been using generative models from the emergent field of Deep Learning, proposing novel compounds that are likely to possess desired properties or activities. De novo molecular design finds applications in different fields ranging from drug discovery and materials sciences to biotechnology. A panoply of deep generative models, including architectures as Recurrent Neural Networks, Autoencoders, and Generative Adversarial Networks, can be trained on existing data sets and provide for the generation of novel compounds. Typically, the new compounds follow the same underlying statistical distributions of properties exhibited on the training data set Additionally, different optimization strategies, including transfer learning, Bayesian optimization, reinforcement learning, and conditional generation, can direct the generation process toward desired aims, regarding their biological activities, synthesis processes or chemical features. Given the recent emergence of these technologies and their relevance, this work presents a systematic and critical review on deep generative models and related optimization methods for targeted compound design, and their applications.

Summary Metabolic Engineering aims to favour the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, which covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms. Availability and implementation MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license.

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In current network infrastructures, several management tasks often require significant human intervention and can be of high complexity, having to consider several inputs to attain efficient configurations. In this perspective, this work presents an optimization framework able to automatically provide network administrators with efficient and robust routing configurations. The proposed optimization tool resorts to techniques from the field of Evolutionary Computation, where Evolutionary Algorithms (EAs) are used as optimization engines to solve the envisaged NP-hard problems. The devised methods focus on versatile and resilient aware Traffic Engineering (TE) approaches, which are integrated into an autonomous optimization framework able to assist network administrators. Some examples of the supported TE optimization methods are presented, including preventive, reactive and multi-topology solutions, taking advantage of the EAs optimization capabilities.