HToPred: A Tool for Human Topoisomerase II Inhibitor Prediction

TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors

The EGFR is a clinically important therapeutic drug target in lung cancer. The first-generation tyrosine kinase inhibitors used in clinics are effective against L858R-mutated EGFR. However, relapse of the disease due to the presence of resistant mutation (T790M) makes these inhibitors ineffective. This has necessitated the need to identify new potent EGFR inhibitors against the resistant double mutants. Therefore, various machine learning techniques ((instance-based learner (IBK), naïve Bayesian (NB), sequential minimal optimization (SMO), and random forest (RF)) were employed to develop twelve classification models on three different datasets (high, moderate, and weakly active inhibitors). The models were validated using fivefold cross-validation and independent validation datasets. It was observed that the random forest-based models showed best performance. Also, functional groups, PubChem fingerprints, and substructure of highly active inhibitors were compared to inactive to identify structural features which are important for activity. To promote open-source drug discovery, a tool has been developed, which incorporates the best performing models and allows users to predict the potential of chemical molecules as anti-TMLR inhibitor. It is expected that the machine learning classification models developed in this study will pave way for identifying novel inhibitors against the resistant EGFR double mutants.

Machine learning-based modeling to predict inhibitors of acetylcholinesterase

Acetylcholinesterase enzyme is responsible for the degradation of acetylcholine and is an important drug target for the treatment of Alzheimer's disease. When this enzyme is inhibited, more acetylcholine is available in the synaptic cleft for the use, which leads to enhanced memory and cognitive ability. The aim of the present work is to create machine learning models for distinguishing between AChE inhibitors and non-inhibitors using algorithms like support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF). The developed models were evaluated by 10-fold cross-validation and external dataset. Descriptor analysis was performed to identify most important features for the activity of molecules. Descriptors which were identified as important include maxssCH2, minHssNH, SaasC, minssCH2, bit 128 MACCS key, bit 104 MACCS key, bit 24 estate fingerprint and bit 18 estate fingerprints. The model developed using fingerprints based on random forest algorithm produced better results compared to other models. The overall accuracy of best model on test set was 85.38 percent. The developed model is available at http://14.139.57.41/achepredictor/ .

Molecular dynamics simulations of sulfone derivatives in complex with DNA topoisomerase IIα ATPase domain

Human topoisomerase II alpha (TopoIIα) is a crucial enzyme involved in maintaining genomic integrity during the process of DNA replication and mitotic division. It is a vital therapeutic target for designing novel anticancer agents in targeted cancer therapy. Sulfones, members of organosulfur compounds, have been reported to possess various biological activities such as antimicrobial, anti-inflammatory, anti-HIV, anticancer, and antimalarial properties. In the present study, a series of sulfones was selected to evaluate their inhibitory activity against TopoIIα using computational approaches. Molecular docking results revealed that several sulfone analogs bind efficiently to the ATPase domain of TopoIIα. Among them, sulfones 18a, 60a, *4 b, *8 b, *3c, and 8c exhibit higher binding affinity than the known TopoII inhibitor, salvicine. Molecular dynamics simulations and free energy calculations based on MM/PB(GB)SA method demonstrated that sulfone *8 b strongly interacts with amino acid residues in the ATP-binding pocket (E87, N91, D94, I125, I141, F142, S149, G161, and A167), driven mainly by an electrostatic attraction and a strong H-bond formation at G161 residue. Altogether, the obtained results predicted that sulfones could have a high potential to be a lead molecule for targeting TopoIIα.Communicated by Ramaswamy H. Sarma.