Spirochromone-chalcone conjugates as antitubercular agents: synthesis, bio evaluation and molecular modeling studies - Mujahid, Mohammad; Perumal, Yogeeswari; Sriram, D.; Basavanag, U. M. V.; Dıaz-Cervantes, Erik; Cordoba-Bahena, Luis; Robles, Juvencio; Gonnade, Rajesh Ghanshyam; Karthikeyan, Muthukumarasamy; Vyas, Renu; Krishnan, M. Muthu (RSC Advances, 2015)
A new series of spirochromone annulated chalcone conjugates were synthesized and evaluated for their antitubercular activity against Mycobacterium tuberculosis H37Rv strain. These compounds were subjected to molecular modeling studies using docking and chemoinformatics based approaches. The docking simulations were performed against a range of known receptors for chalcone derived compounds to reveal MTB phosphotyrosine phosphatase B [MtbPtpB] protein as the most probable target based on the high binding affinity scores. Five compounds exhibit significant inhibition, showing minimum inhibitory concentration values i.e. MIC values ranging from 3.13–12.5 μg mL−1. Further analysis of the synthesized compounds with known and in-house developed chemoinformatics tools unequivocally established their potential as anti-tubercular compounds. QSAR modeling revealed a quantitative relationship between biological activities and frontier molecular orbital energies of synthesized compounds. The predictive model can be employed further for virtual screening of new compounds in this series.

Study of applications of machine learning based classification methods for virtual screening of lead molecules - Vyas, Renu; Bapat, Sanket; Jain, Esha; Tambe, Sanjeev S.; Karthikeyan, Muthukumarasamy; Kulkarni, Bhaskar D. (Combinatorial Chemistry & High Throughput Screening, 2015)
The ligand-based virtual screening of combinatorial libraries employs a number of statistical modeling and machine learning methods. A comprehensive analysis of the application of these methods for the diversity oriented virtual screening of biological targets/drug classes is presented here. A number of classification models have been built using three types of inputs namely structure based descriptors, molecular fingerprints and therapeutic category for performing virtual screening. The activity and affinity descriptors of a set of inhibitors of four target classes DHFR, COX, LOX and NMDA have been utilized to train a total of six classifiers viz. Artificial Neural Network (ANN), k nearest neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree - (DT) and Random Forest - (RF). Among these classifiers, the ANN was found as the best classifier with an AUC of 0.9 irrespective of the target. New molecular fingerprints based on pharmacophore, toxicophore and chemophore (PTC), were used to build the ANN models for each dataset. A good accuracy of 87.27% was obtained using 296 chemophoric binary fingerprints for the COX-LOX inhibitors compared to pharmacophoric (67.82 %) and toxicophoric (70.64 %). The methodology was validated on the classical Ames mutagenecity dataset of 4337 molecules. To evaluate it further, selectivity and promiscuity of molecules from five drug classes viz. anti-anginal, anti-convulsant, anti-depressant, anti-arrhythmic and anti-diabetic were studied. The TPC fingerprints computed for each category were able to capture the drug-class specific features using the k-NN classifier. These models can be useful for selecting optimal molecules for drug design.
Keywords: Anti-anginal; anti-arrythmic; anti-bacterial; anti-convulsant; anti-depressant anti-diabetic; binary QSAR; chemophore; machine learning; pharmacophore; toxicophore

Synthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-Chromone fused alpha-aminophosphonates as src kinase inhibitors - Bapat, S.; Viswanadh, N.; Mujahid, M.; Shirazi, A. N.; Tiwari, R. K.; Parang, K.; Karthikeyan, M.; Muthukrishnan, M.; Vyas, Renu (Journal of Scientific & Industrial Research, 2019)
A series of novel chromone/aza-chromone fused alpha-aminophosphonate derivatives were synthesized in good yields using silica chloride as the catalyst. All the synthesized compounds were tested for their c-Src kinase inhibitory activity. Aza-chromone compound showed Src kinase inhibition with an IC50 value of 15.8 mu M. The compounds were subjected to molecular docking and dynamics simulations to study the atomic level interactions with an unphosphorylated proto-oncogenic tyrosine protein kinase Src (PDB code 1Y57) as well as phosphorylated tyrosine protein kinase Src (PDB code 2H8H). Docking and molecular dynamic results revealed phosphorylated Src tyrosine kinase protein better results than unphosphorylated tyrosine Src kinase protein. Chemoinformatics study revealed the compounds had lead like properties. Machine learning (SVR) models were built to study the structure activity correlations. A CC of 0.835 was obtained when the SVR model was applied to the 17 synthesized compounds. It is envisaged that the work will provide guidelines for future drug design efforts for Src kinase inhibitors.

Synthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-chromone fused α-aminophosphonates as Src kinase inhibitors - Bapat, Sanket; Viswanadh, N.; Mujahid, M.; Shirazi, Amir Nasrolahi; Tiwari, Rakesh; Parang, Keykavous; Karthikeyan, Muthukumarasamy; Muthukrishnan, M.; Vyas, Renu (Journal of Scientific and Industrial Research, 2019)
A series of novel chromone/aza-chromone fused α-aminophosphonate derivatives were synthesized in good yields using silica chloride as the catalyst. All the synthesized compounds were tested for their c-Src kinase inhibitory activity. Aza-chromone compound showed Src kinase inhibition with an IC50 value of 15.8 µM. The compounds were subjected to molecular docking and dynamics simulations to study the atomic level interactions with an unphosphorylated proto-oncogenic tyrosine protein kinase Src (PDB code 1Y57) as well as phosphorylated tyrosine protein kinase Src (PDB code 2H8H). Docking and molecular dynamic results revealed phosphorylated Src tyrosine kinase protein better results than unphosphorylated tyrosine Src kinase protein. Chemoinformatics study revealed the compounds had lead like properties. Machine learning (SVR) models were built to study the structure activity correlations. A CC of 0.835 was obtained when the SVR model was applied to the 17 synthesized compounds. It is envisaged that the work will provide guidelines for future drug design efforts for Src kinase inhibitors.