cardioToxCSM: A Web Server for Predicting Cardiotoxicity of Small Molecules.

The design of novel, safe, and effective drugs to treat human diseases is a challenging venture, with toxicity being one of the main sources of attrition at later stages of development. Failure due to toxicity incurs a significant increase in costs and time to market, with multiple drugs being withdrawn from the market due to their adverse effects. Cardiotoxicity, for instance, was responsible for the failure of drugs such as fenspiride, propoxyphene, and valdecoxib. While significant effort has been dedicated to mitigate this issue by developing computational approaches that aim to identify molecules likely to be toxic, including quantitative structure-activity relationship models and machine learning methods, current approaches present limited performance and interpretability. To overcome these, we propose a new web-based computational method, cardioToxCSM, which can predict six types of cardiac toxicity outcomes, including arrhythmia, cardiac failure, heart block, hERG toxicity, hypertension, and myocardial infarction, efficiently and accurately. cardioToxCSM was developed using the concept of graph-based signatures, molecular descriptors, toxicophore matchings, and molecular fingerprints, leveraging explainable machine learning, and was validated internally via different cross validation schemes and externally via low-redundancy blind sets. The models presented robust performances with areas under ROC curves of up to 0.898 on 5-fold cross-validation, consistent with metrics on blind tests. Additionally, our models provide interpretation of the predictions by identifying whether substructures that are commonly enriched in toxic compounds were present. We believe cardioToxCSM will provide valuable insight into the potential cardiotoxicity of small molecules early on drug screening efforts. The method is made freely available as a web server at https://biosig.lab.uq.edu.au/cardiotoxcsm.

Exploring Novel 1-Hydroxynaphthalene-2-Carboxanilides Based Inhibitors Against C-Jun N-Terminal Kinases Through Molecular Dynamic Simulation and WaterSwap Analysis.

Cancer is a disease of mutation and lifestyle modifications. A large number of normal genes can transform normal cells to cancer cells due to their deregulations including overexpression and loss of expression. Signal transduction is a complex signaling process that involves multiple interactions and different functions. C-Jun N-terminal kinases (JNKs) is an important protein involved in signaling process. JNK mediated pathways can detect, integrate, and amplify various external signals that may cause alterations in gene expression, enzyme activities, and different cellular functions that affect cellular behavior like metabolism, proliferation, differentiation, and cell survival. In this study, we performed molecular docking protocol (MOE) to predict the binding interactions of some known anticancer 1-hydroxynaphthalene-2-carboxanilides candidates. A set of 10 active compounds was retrieved after initial screening on the basis of docking scores, binding energies, and number of interactions and was re-docked in the active site of JNK protein. The results were further validated through molecular dynamics simulation and MMPB/GBSA calculations. The active compounds 4p and 5 k were ranked on top. After computationally exploring interactions of 1-hydroxynaphthalene-2-carboxanilides with JNK protein, we believe compounds 4p and 5 k can serve as potential inhibitors of JNK protein. It is believed that the results of current research would help to develop novel and structurally diverse anticancer compounds that will be useful not only treat cancer but also for the medication for the other diseases caused by protein deregulation.