Recent developments in the target based cancer therapies have identified HSF1 as a novel non oncogenic drug target. The present study delineates the design and molecular docking evaluation of Rohinitib (RHT) - Cantharidin (CLA) based novel HSF1 inhibitors for target-based cancer therapy. Here, we exploited the pharmacophoric features of both the parent ligands for the design of novel hybrid HSF1 inhibitors. The RHT-CLA ligands were designed and characterized for ADME/Tox features, interaction with HSF1 DNA binding domain and their pharmacophoric features essential for interaction. From the results, amino acid residues Ala17, Phe61, His63, Asn65, Ser68, Arg71 and Gln72 were found crucial for HSF1 interaction with the Heat shock elements (HSE). The hybrid ligands had better affinity towards the HSF1 DNA binding domain, in comparison to RHT or CLA and interacted with most of the active site residues. Additionally, the HSF1-ligand complex had a reduced affinity towards HSE in comparison to native HSF1. Based on the results, ligand RC15 and RC17 were non carcinogenic, non mutagenic, completely biodegradable under aerobic conditions, had better affinity for HSF1 (1.132 and 1.129 folds increase respectively) and diminished the interaction of HSF1 with HSE (1.203 and 1.239 folds decrease respectively). The simulation analysis also suggested that the ligands formed a stable complex with HSF1, restraining the movement of active site residues. In conclusion, RHT-CLA hybrid ligands can be used as a potential inhibitor of HSF1 for non-oncogene target based cancer therapy.
We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals' use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver's operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.