Designing novel small molecules with desirable properties and feasible synthesis continues to pose a significant challenge in drug discovery, particularly in the realm of natural products. Reaction-based gradient-free methods are promising approaches for designing new molecules as they ensure synthetic feasibility and provide potential synthesis paths. However, it is important to note that the novelty and diversity of the generated molecules highly depend on the availability of comprehensive reaction templates. To address this challenge, we introduce ReactEA, a new open-source evolutionary framework for computer-aided drug discovery that solely utilizes biochemical reaction rules. ReactEA optimizes molecular properties using a comprehensive set of 22,949 reaction rules, ensuring chemical validity and synthetic feasibility. ReactEA is versatile, as it can virtually optimize any objective function and track potential synthetic routes during the optimization process. To demonstrate its effectiveness, we apply ReactEA to various case studies, including the design of novel drug-like molecules and the optimization of pre-existing ligands. The results show that ReactEA consistently generates novel molecules with improved properties and reasonable synthetic routes, even for complex tasks such as improving binding affinity against the PARP1 enzyme when compared to existing inhibitors.

Recent developments in Generative Deep Learning have fostered new engineering methods for protein design. Although deep generative models trained on protein sequence can learn biologically meaningful representations, the design of proteins with optimised properties remains a challenge. We combined deep learning architectures with evolutionary computation to steer the protein generative process towards specific sets of properties to address this problem. The latent space of a Variational Autoencoder is explored by evolutionary algorithms to find the best candidates. A set of single-objective and multi-objective problems were conceived to evaluate the algorithms' capacity to optimise proteins. The optimisation tasks consider the average proteins' hydrophobicity, their solubility and the probability of being generated by a defined functional Hidden Markov Model profile. The results show that Evolutionary Algorithms can achieve good results while allowing for more variability in the design of the experiment, thus resulting in a much greater set of possibly functional novel proteins.

The non-caloric sweeteners market is catching up with the market of conventionally used sugars due to the benefits of preventing obesity, tooth decay and other health problems. Developing strategies for designing easier-to-produce novel molecules with a sweet taste and less toxicity are up-to-date motivations for the food industry. In this sense, Machine Learning (ML) approaches have been reported as cutting-edge technologies to guide the design of new molecules towards specific objectives, including sweet taste. The largest known dataset of sweet molecules is here provided. The dataset contains fully integrated 9541 sweeteners and 1141 bitterants from FooDB, FlavorDB and literature. This robust dataset allowed the development of standard Machine and Deep Learning pipelines towards conceiving Structure-Activity Relationships (SAR) between molecules and sweetness. In this work, we showcase that Textual Convolutional Neural Networks (TextCNN), Graph Convolutional Networks (GCN), and Deep Neural Networks (DNNs) outperformed most of traditional “shallow” learning approaches. These Deep Learning (DL) models produced platforms to guide the design of new sweeteners and repurposing existing compounds. Sixty million compounds from PubChem were evaluated using these models. Herein, we deliver a dataset of 67724 compounds that present high probabilities of being sweet. Quick searches in literature allowed us to find 13 molecules reported as potent sweetening agents, revealing that our approach is suitable for finding new sweeteners, valuable to expand food chemistry databases, repurposing existing chemicals and designing novel molecules with a sweet taste.

Exploiting the natural metabolic abilities of microorganisms for the production of bioactive compounds has been a research problem of great interest. The economical and environmental costs associated with petrochemical-derived industries have promoted the emergence of biochemical processes from renewable carbon sources. However, optimally rewiring microbial metabolism in a competitive and sustainable manner is still a challenge. Recently, some retrobiosynthesis tools for the design of de novo biosynthetic pathways have been proposed. These tools generate a large number of intermediate compounds that are beyond experimental feasibility. Thus, effective methods to reduce the number of compounds by selecting the most promising ones are still needed. Here, we propose the use of classification and regression deep learning models, such as fully-connected neural networks and 1D convolutional neural networks, to predict the number of biochemical transformations needed to produce a compound. The data to train and evaluate the models was generated using a set of 13055 reaction rules and 673 compounds from Escherichia coli metabolism as starting compounds. The data was generated up to 5 steps resulting in a dataset of over 2.6 million compounds. This approach can be effectively used in biochemical applications, including retrobiosyntesis, to prioritize compounds that can be produced using fewer biochemical transformations.

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.

IEEE Segment Routing (SR) is a new fertile ground for Traffic Engineering (TE). By decomposing forwarding paths into segments, which specify a list of intermediate delivery points that a packet must visit on its way to the final destination, SR improves TE tasks and enables new solutions for the optimization of network resource utilization. This work proposes an Evolutionary Computation approach that enables Path Computation Element (PCE), or Software-defined Network (SDN) controllers, to optimize SR configurations for improved traffic distribution. Furthermore, we present a robust semi-oblivious method to address the variability of traffic requirements as well as alternative approaches to ensure a good network performance after link failures. In all cases, the optimization of network resource utilization is achieved using at the most three segments to configure each SR path. Moreover, all proposed optimization methods are made publicly available in a optimization framework developed by the authors.