De Novo Design of Anti-COVID Drugs Using Machine Learning-Based Equivariant Diffusion Model Targeting the Spike Protein.

The drug discovery and research for an anti-COVID-19 drug has been ongoing despite repurposed drugs in the market. Over time, these drugs were discontinued due to side effects. The search for effective drugs is still under process. The role of Machine Learning (ML) is critical in the search for novel drug compounds. In the current work, using the equivariant diffusion model, we built novel compounds targeting the spike protein of SARS-CoV-2. Using the ML models, 196 de novo compounds were generated which had no hits on any major chemical databases. These novel compounds fulfilled all the criteria of ADMET properties to be lead-like and drug-like compounds. Of the 196 compounds, 15 were docked with high confidence in the target. These compounds were further subjected to molecular docking, the best compound having an IUPAC name of (4aS,4bR,8aS,8bS)-4a,8a-dimethylbiphenylene-1,4,5,8(4aH,4bH,8aH,8bH)-tetraone and a binding score of -6.930 kcal/mol. The principal compound is labeled as CoECG-M1. Density Function Theory (DFT) and Quantum optimization was carried out along with the study of ADMET properties. This suggests that the compound has potential drug-like properties. The docked complex was further subjected to MD simulations, GBSA, and metadynamics simulations to gain insights into the stability of binding. The model can be in the future modified to improve the positive docking rate.

Assessment of Shear Bond Strength and Marginal Sealing Ability of Pit and Fissure Sealants: An in vitro Study.

AIM: The aim of the present study was to compare the shear bond strength and marginal sealing ability of self-adhering flow-able composite and conventional fissure sealant. MATERIALS AND METHODS: The samples consisted of 30 healthy premolar teeth which were extracted due to orthodontic reasons and randomly divided into two groups of 15, i.e., group I (Fissurit F) and group II (Dyad Flow). Shear bond strength and marginal sealing ability of both the groups were evaluated in Statistical Package for the Social Sciences (SPSS) version 16. RESULTS: The mean shear bond strength of Dyad Flow (group II) was found to be 1.4 ± 0.87 MPa and in Fissurit F (group I), it was 1.3 ± 1.4 MPa. Differences between the groups were statistically significant. In group II, 53.3% of specimens demonstrated score 0; 33.3% showed score 1; and 13.3% showed score 2. In group I, scores 0 and 1 showed 33.3% of dye penetration respectively. Scores 2 and 3 demonstrated 26.6 and 6.6% of dye penetration respectively. But there was no significant difference between both the sealant groups. CONCLUSION: The present study concluded that self-adhering flowable composite was found to have better shear bond strength and marginal sealing ability than conventional fissure sealant. CLINICAL SIGNIFICANCE: Self-adhering flowable composite can be effectively used in pediatric patients in whom isolation is difficult and exclusion of bonding agent leads to decrease in time consumption.

Corrigendum to 'Deep learning dives: Predicting anxiety in Zebrafish through novel tank assay analysis' Physiology & Behavior (2024), 114696.

Abstract.

Deep learning dives: Predicting anxiety in zebrafish through novel tank assay analysis.

Behavior is fundamental to neuroscience research, providing insights into the mechanisms underlying thoughts, actions and responses. Various model organisms, including mice, flies, and fish, are employed to understand these mechanisms. Zebrafish, in particular, serve as a valuable model for studying anxiety-like behavior, typically measured through the novel tank diving (NTD) assay. Traditional methods for analyzing NTD assays are either manually intensive or costly when using specialized software. To address these limitations, it is useful to develop methods for the automated analysis of zebrafish NTD assays using deep-learning models. In this study, we classified zebrafish based on their anxiety levels using DeepLabCut. Subsequently, based on a training dataset of image frames, we compared deep-learning models to identify the model best suited to classify zebrafish as anxious or non anxious and found that specific architectures, such as InceptionV3, are able to effectively perform this classification task. Our findings suggest that these deep learning models hold promise for automated behavioral analysis in zebrafish, offering an efficient and cost-effective alternative to traditional methods.