In Silico Design and Selection of New Tetrahydroisoquinoline-Based CD44 Antagonist Candidates

Graph Attention Network based mapping of knowledge relations between chemical spaces of Nuclear factor kappa B and Centella asiatica

The confounding nature of the innate immunity target Nuclear Factor kappa B (NF-κB) and its interaction with Centella asiatica (CA) molecules necessitate the intervention of advanced technologies, such as deep learning methods. The integration of chemical space concepts with deep learning technologies is a new way of knowledge mapping used to explore drug-target interactions, especially in molecular libraries derived from traditional medicine based molecular sources. The current constraint of virtual screening for mechanistic target hunting is the use of a binary classification model that includes active and inactive molecules from in vitro experiments to explore drug-target interaction. This study aims to explore the regulatory nature of the molecules from the inhibition and activation of the NF-κB bioassay data set and map this information for a knowledge-based analysis against the molecules of CA, a low-growing tropical plant. This finding has led to a new direction in the field, transitioning from the conventional active-inactive framework to a more comprehensive active-inactive-regulatory model. This approach can be thoroughly explored by leveraging a graph-based deep learning system. The study presents an innovative approach using a Graph Attention Network (GAT) to rank CA molecules in chemical space based on their similarity with NF-κB bioassay molecules, enabling the efficient analysis of complex relationships between molecules and their regulatory function. Graph Attention Network (GAT) overcomes the limitations of traditional deep learning models such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in handling non-Euclidean graph data and allows for a more precise understanding of similarity ranking by utilizing molecular graphs and attention behavior. By measuring similarity and arranging a matrix of similarity ranking based on GAT, deep neural ranking-based algorithms confirmed the regulatory behaviour of an innate immunity target NF-κB with the support of underlying inverse mapping in the surjective chemical spaces of NF-κB bioassays and CA molecular spaces. Overall, the study introduces new techniques for exploring the regulatory behaviour of complex targets like NF-κB. We then used t-SNE for clustering in chemical space and scaffold hunting for scaffold property analysis and identified nine CA molecules that exhibit regulatory behavior of NF-κB target and are recommended for further investigation.

Investigating the Lack of Translation from Cruzain Inhibition to Trypanosoma cruzi Activity with Machine Learning and Chemical Space Analyses

Chagas disease is a neglected tropical disease caused by the protozoa Trypanosoma cruzi. Cruzain, its main cysteine protease, is commonly targeted in drug discovery efforts to find new treatments for this disease. Even though the essentiality of this enzyme for the parasite has been established, many cruzain inhibitors fail as trypanocidal agents. This lack of translation from biochemical to biological assays can involve several factors, including suboptimal physicochemical properties. In this work, we aim to rationalize this phenomenon through chemical space analyses of calculated molecular descriptors. These include statistical tests, visualization of projections, scaffold analysis, and creation of machine learning models coupled with interpretability methods. Our results demonstrate a significant difference between the chemical spaces of cruzain and T. cruzi inhibitors, with compounds with more hydrogen bond donors and rotatable bonds being more likely to be good cruzain inhibitors, but less likely to be active on T. cruzi. In addition, cruzain inhibitors seem to occupy specific regions of the chemical space that cannot be easily correlated with T. cruzi activity, which means that using predictive modeling to determine whether cruzain inhibitors will be trypanocidal is not a straightforward task. We believe that the conclusions from this work might be of interest for future projects that aim to develop novel trypanocidal compounds.

Salinomycin mediated therapeutic targeting of circulating stem like cell population in oral cancer

CD44+ circulating tumor stem cells (CTSCs) have been significantly associated with aggressiveness, resistance and poor prognosis of oral cancer patients. Thus, targeted elimination of these CTSCs could be a new conceptual framework for enhancing the therapeutic outcome of patients. Docking of potential investigational molecules and simulation results identified Salinomycin as a potential lead compound that could effectively inhibit CD44 receptor. To assess the cytotoxic effect, immuno-magnetically sorted circulatory CD44+ cells were subjected to increasing concentrations of 5FU, Cisplatin and Salinomycin. Salinomycin demonstrated significant cytotoxic effect towards the CD44+ subpopulation in a dose and time dependent manner. Further the effect of these compounds was investigated on apoptosis, cell cycle, signaling pathways and gene expression profiles using Muse^TM flow cytometer and Real-Time PCR. It was observed that mRNA expression patterns of CD44v6, Nanog, AKT1, CDKN2A and β-catenin of Salinomycin treated CD44+ cells. Moreover, Salinomycin significantly induced programmed cell death by inducing G2/M cell cycle arrest and inhibiting MAPK/PI3K pathways in this chemo-resistant population. Thus, this study demonstrated the potential of Salinomycin to target the chemo-resistant circulating CD44 population by attenuating its proliferation and survival.Communicated by Ramaswamy H. Sarma.