Due to the constant increase in cancer rates, the disease has become a leading cause of death worldwide, enhancing the need for its detection and treatment. In the era of personalized medicine, the main goal is to incorporate individual variability in order to choose more precisely which therapy and prevention strategies suit each person. However, predicting the sensitivity of tumors to anticancer treatments remains a challenge. In this work, we propose two deep neural network models to predict the impact of anticancer drugs in tumors through the half-maximal inhibitory concentration (IC50). These models join biological and chemical data to apprehend relevant features of the genetic profile and the drug compounds, respectively. In order to predict the drug response in cancer cell lines, this study employed different DL methods, resorting to Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). In the first stage, two autoencoders were pre-trained with high-dimensional gene expression and mutation data of tumors. Afterward, this genetic background is transferred to the prediction models that return the IC50 value that portrays the potency of a substance in inhibiting a cancer cell line. When comparing RSEM Expected counts and TPM as methods for displaying gene expression data, RSEM has been shown to perform better in deep models and CNNs model can obtain better insight in these types of data. Moreover, the obtained results reflect the effectiveness of the extracted deep representations in the prediction of the IC50 value that portrays the potency of a substance in inhibiting a tumor, achieving a performance of a mean squared error of 1.06 and surpassing previous state-of-the-art models.

The drug discovery process can be significantly improved by applying deep reinforcement learning (RL) methods that learn to generate compounds with desired pharmacological properties. Nevertheless, RL-based methods typically condense the evaluation of sampled compounds into a single scalar value, making it difficult for the generative agent to learn the optimal policy. This work combines self-attention mechanisms and RL to generate promising molecules. The idea is to evaluate the relative significance of each atom and functional group in their interaction with the target, and to utilize this information for optimizing the Generator. Therefore, the framework for de novo drug design is composed of a Generator that samples new compounds combined with a Transformer-encoder and a biological affinity Predictor that evaluate the generated structures. Moreover, it takes the advantage of the knowledge encapsulated in the Transformer's attention weights to evaluate each token individually. We compared the performance of two output prediction strategies for the Transformer: standard and masked language model (MLM). The results show that the MLM Transformer is more effective in optimizing the Generator compared with the state-of-the-art works. Additionally, the evaluation models identified the most important regions of each molecule for the biological interaction with the target. As a case study, we generated synthesizable hit compounds that can be putative inhibitors of the enzyme ubiquitin-specific protein 7 (USP7).

Cancer is among the deadliest diseases, enhancing the need for its detection and treatment. In the era of precision medicine, the main goal is to take into account individual vari-ability in order to choose more accurately which treatment and prevention strategies suit each person. However, drug response prediction for cancer therapy remains a challenge. In this work, we propose a deep neural network model to predict the effect of anticancer drugs in tumors through the half-maximal inhibitory concentration (IC50). The model can be seen as two-fold: first, we pre-trained two autoencoders with high-dimensional gene expression and mutation data to capture the crucial features from tumors; then, this genetic background is translated to cancer cell lines to predict the impact of the genetic variants on a given drug. Moreover, SMILES structures were introduced so that the model can apprehend relevant features regarding the drug compound. Finally, we use drug sensitivity data correlated to the genomic and drugs data to identify features that predict the IC50 value for each pair of drug-cell line. The obtained results demonstrate the effectiveness of the extracted deep representations in the prediction of drug-target interactions, achieving a performance of a mean squared error of 1.07 and surpassing previous state-of-the-art models.

Algorand is a public proof-of-stake (PoS) blockchain with a throughput of 750 MB of transactions per hour, 125 times more than Bitcoin. While the throughput of Algorand depends on the participation of most of its nodes, rational nodes may behave selfishly and not cooperate with others. To encourage nodes to participate in the consensus protocol, Algorand rewards nodes in each round. However, currently Algorand does not pay transaction fees to participating nodes, rather storing it for future use. In this paper, we show that this current approach of Algorand motivates selfish block proposers to increase their profits by creating empty blocks. Such selfish behavior reduces the throughput of Algorand. Therefore, the price of Algo will decrease in the long run. Because of this price reduction, nodes will leave Algorand, compromising its security. Moreover, lack of an appropriate mechanism to pay fees to participants causes additional issues, such as lack of transparency, centralization, and inability of nodes to prioritize transactions. To overcome this challenge, we design a perfectly competitive market and propose an algorithm for computing optimal transaction fees and block size in Algorand We also propose an algorithm that reduces the cost of Algorand, without compromising its security. We further simulate the Algorand network and show how the optimal transaction fee and block size can be calculated in practice.

The generation of candidate hit molecules with the potential to be used in cancer treatment is a challenging task. In this context, computational methods based on deep learning have been employed to improve in silico drug design methodologies. Nonetheless, the applied strategies have focused solely on the chemical aspect of the generation of compounds, disregarding the likely biological consequences for the organism's dynamics. Herein, we propose a method to implement targeted molecular generation that employs biological information, namely, disease-associated gene expression data, to conduct the process of identifying interesting hits. When applied to the generation of USP7 putative inhibitors, the framework managed to generate promising compounds, with more than 90% of them containing drug-like properties and essential active groups for the interaction with the target. Hence, this work provides a novel and reliable method for generating new promising compounds focused on the biological context of the disease.

BACKGROUND: Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug-target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing explanations to the decision-making process of deep learning architectures. In this research study, we explore the reliability of convolutional neural networks (CNNs) at identifying relevant regions for binding, specifically binding sites and motifs, and the significance of the deep representations extracted by providing explanations to the model's decisions based on the identification of the input regions that contributed the most to the prediction. We make use of an end-to-end deep learning architecture to predict binding affinity, where CNNs are exploited in their capacity to automatically identify and extract discriminating deep representations from 1D sequential and structural data. RESULTS: The results demonstrate the effectiveness of the deep representations extracted from CNNs in the prediction of drug-target interactions. CNNs were found to identify and extract features from regions relevant for the interaction, where the weight associated with these spots was in the range of those with the highest positive influence given by the CNNs in the prediction. The end-to-end deep learning model achieved the highest performance both in the prediction of the binding affinity and on the ability to correctly distinguish the interaction strength rank order when compared to baseline approaches. CONCLUSIONS: This research study validates the potential applicability of an end-to-end deep learning architecture in the context of drug discovery beyond the confined space of proteins and ligands with determined 3D structure. Furthermore, it shows the reliability of the deep representations extracted from the CNNs by providing explainability to the decision-making process.

In drug discovery, Deep Learning algorithms are emerging as a potential method to generate novel chemical structures since they can speed up the traditional process and decrease expenditure. Recurrent architectures are amongst the most promising methods for computational de novo drug design. One current challenge consists in finding the optimal architecture and parameters for the recurrent network that assures the generation of valid molecules that span the chemical space. In this work we perform an evaluation on Recurrent Neural Networks which can learn the syntax of molecular representation in terms of SMILES notation. We optimize the computational framework based on the recurrent architecture and its hyper-parameters. Moreover, we evaluate the performance of two types of encoding and spatial arrangement of molecules: Embedding and One-hot Encoding, and datasets with and without stereo-chemical information, respectively. The proposed model showed improved performance when compared to the current literature, both in terms of percentage of valid generated SMILES and diversity with 98.7% and 0.88, for the ChEMBL dataset, respectively. Even when considering the ZINC biogenic library, with stereochemical information, the values were 94.5% and 0.90. The obtained results reveal the potential of the recurrent architectures in learning the SMILES syntax and adding novelty to generate promising compounds.

Machine learning has been increasingly applied with success in generating synthetically reasonable molecules.However, a complete system capable of both producing valid molecules and optimizing multiple traits has remained elusive.This paper employs multiobjective reinforcement learning to draw a framework to design compounds.Different multiobjective techniques have been evaluated, such as weighted sum and Chebyshev.The results show that the implemented model can be effectively optimized towards different and competing molecular properties.Nonetheless, the model implemented with the weighted sum scalarization technique with a weight of 0.55 for biological affinity is the one with the most appropriate trade-off for the different evaluated properties.

This paper provides a generative network framework that can replicate the molecular space distribution to satisfy a set of desirable features.The approach incorporates two effective machine learning techniques: an Encoder-Decoder architecture that converts the string notations of molecules into latent space and a generative adversarial network to learn the data distribution and generate new compounds.We train this joint model on a dataset that includes stereo-chemical information.The results show an improvement in the Encoder-Decoder performance, reaching 89% of correctly reconstructed molecules.The framework can generate a wide variety of compounds biased towards specific molecular properties using Transfer Learning.

MOTIVATION: The process of placing new drugs into the market is time-consuming, expensive and complex. The application of computational methods for designing molecules with bespoke properties can contribute to saving resources throughout this process. However, the fundamental properties to be optimized are often not considered or conflicting with each other. In this work, we propose a novel approach to consider both the biological property and the bioavailability of compounds through a deep reinforcement learning framework for the targeted generation of compounds. We aim to obtain a promising set of selective compounds for the adenosine A2A receptor and, simultaneously, that have the necessary properties in terms of solubility and permeability across the blood-brain barrier to reach the site of action. The cornerstone of the framework is based on a recurrent neural network architecture, the Generator. It seeks to learn the building rules of valid molecules to sample new compounds further. Also, two Predictors are trained to estimate the properties of interest of the new molecules. Finally, the fine-tuning of the Generator was performed with reinforcement learning, integrated with multi-objective optimization and exploratory techniques to ensure that the Generator is adequately biased. RESULTS: The biased Generator can generate an interesting set of molecules, with approximately 85% having the two fundamental properties biased as desired. Thus, this approach has transformed a general molecule generator into a model focused on optimizing specific objectives. Furthermore, the molecules' synthesizability and drug-likeness demonstrate the potential applicability of the de novo drug design in medicinal chemistry. AVAILABILITY AND IMPLEMENTATION: All code is publicly available in the https://github.com/larngroup/De-Novo-Drug-Design. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Parallel share-nothing architectures are currently used to handle large amounts of data arriving in real-time for processing. The continuous increase on data volume and organization, introduce several limitations to scalability and quality of service (QoS) due to processing requirements and joins. Parallelism may improve query performance, however some business require timely results (results not faster or slower than specified) which, even with additional parallelism and significant upgrade costs (both monetary and due to disturbance of normal operations), cannot be guaranteed. We propose a timely-aware execution architecture, Cloudy, which balances data and queries processing among an elastic set of non-dedicated and heterogeneous nodes in order to provide scale-out performance and timely results, nor faster or slower, using both Complex Event Processing (CEP) and database (DB). Data is distributed by nodes accordingly with their hardware characteristics, then a set of layered mechanisms rearrange queries in order to provide in timely results. We present experimental evaluation of Cloudy and demonstrate its ability to provide timely results.

MOTIVATION: In this article, we consider the bicriteria pairwise sequence alignment problem and propose extensions of dynamic programming algorithms for several problem variants with a novel pruning technique that efficiently reduces the number of states to be processed. Moreover, we present a method for the construction of phylogenetic trees based on this bicriteria framework. Two exemplary cases are discussed. RESULTS: Numerical results on a real dataset show that this approach is very fast in practice. The pruning technique saves up to 90% in memory usage and 80% in CPU time. Based on this method, phylogenetic trees are constructed from real-life data. In addition of providing complementary information, some of these trees match those obtained by the Maximum Likelihood method. AVAILABILITY AND IMPLEMENTATION: Source code is freely available for download at URL http://eden.dei.uc.pt/paquete/MOSAL, implemented in C and supported on Linux, MAC OS and MS Windows.