Janderson Ferreira

Purpose: Molecular docking prediction plays a pivotal role in intelligent drug design, offering significant advantages in the development of antivirus medications and vaccines. By accurately evaluating the interactions between drug molecules and target proteins, researchers can effectively expedite the discovery and development of vital pharmaceutical solutions, aiding in the mitigation of epidemics, pandemics, and the urgent need for improved vaccines. Methods: We propose an intelligent hybrid architecture for estimating molecular docking between two proteins using deep networks based on pseudo-convolutions and Random Forests. As input, we Evaluation of molecular docking by deep learning and Random Forests used the sequences of characters representing the bases of each protein. Through two layers of pseudo-convolutions, these sequences are reorganized and, finally, represented as co-occurrence matrices, concatenated into a feature vector. To validate our proposal, we used a combination of the public datasets Affinity Benchmark 3 and Negatome 2. Results: The experimental results revealed that the hybrid architecture , comprising two layers of pseudo-convolution followed by a decision layer with an ensemble of 25 Random Forests, yielded average accuracy of 94%, AUC of 93%, sensitivity of 94%, and specificity of 78%. This demonstrates the feasibility of achieving robust estimates of molecular docking without relying on 3D molecule modeling. Conclusion: These findings the potential of rapidly estimating protein affinity, providing valuable insights into drug interactions, molecular dynamics, and facilitating the intelligent design of pharmaceuticals, encompassing vaccines and antiviral drugs. These advancements play a critical role in cost reduction related to laboratory analysis and expediting the timely delivery of solutions to both the market and society at large, particularly in the context of epidemic outbreaks, pandemics, and the urgent demand for supplementary vaccines.

Cracks are pathologies whose appearance in ceramic tiles can cause various damages due to the coating system losing water tightness and impermeability functions. Besides, the detachment of a ceramic plate, exposing the building structure, can still reach people who move around the building. Manual inspection is the most common method for addressing this problem. However, it depends on the knowledge and experience of those who perform the analysis and demands a long time and a high cost to map the entire area. This work focuses on automated optical inspection to find faults in ceramic tiles performing the segmentation of cracks in ceramic images using deep learning to segment these defects. We propose an architecture for segmenting cracks in facades with Deep Learning that includes an image pre-processing step. We also propose the Ceramic Crack Database, a set of images to segment defects in ceramic tiles. The proposed model can adequately identify the crack even when it is close to or within the grout.

The proliferation of the SARS-Cov-2 virus to the whole world caused more than 250,000 deaths worldwide and over 4 million confirmed cases. The severity of Covid-19, the exponential rate at which the virus proliferates, and the rapid exhaustion of the public health resources are critical factors. The RT-PCR with virus DNA identification is still the benchmark Covid-19 diagnosis method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach, and represented by co-occurrence matrices. This technique analyzes the DNA sequences obtained by the RT-PCR method, eliminating sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-Cov-2. Experiments with all 24 virus families and SARS-Cov-2 (multi-class scenario) resulted 0.822222 ± 0.05613 for sensitivity and 0.99974 ± 0.00001 for specificity using Random Forests with 100 trees and 30% overlap. When we compared SARS-Cov-2 with similar-symptoms virus families, we got 0.97059 ± 0.03387 for sensitivity, and 0.99187 ± 0.00046 for specificity with MLP classifier and 30% overlap. In the real test scenario, in which SARS-Cov-2 is compared to Coronaviridae and healthy human DNA sequences, we got 0.98824 ± 001198 for sensitivity and 0.99860 ± 0.00020 for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify SARS-Cov-2 DNA sequences faster with higher specificity and sensitivity.