In silico prediction of anti-malarial hit molecules based on machine learning methods. - Madhulata Kumari; Subhash Chandra (Int. J. Comput. Biol. Drug Des., 2015)

In silico prediction and evaluation of interaction between protein arginine N-methyltransferase 5 cancer target and curcumin derivatives by molecular docking and simulation. - Madhulata Kumari; Subhash Chandra (Int. J. Comput. Biol. Drug Des., 2017)

Evaluation of predictive models based on random forest, decision tree and support vector machine classifiers and virtual screening of anti-mycobacterial compounds. - Madhulata Kumari; Neeraj Tiwari; Naidu Subbarao; Subhash Chandra (Int. J. Comput. Biol. Drug Des., 2017)

Comparative analysis of machine learning based QSAR models and molecular docking studies to screen potential anti-tubercular inhibitors against InhA of mycobacterium tuberculosis. - Madhulata Kumari; Neeraj Tiwari; Subhash Chandra; Naidu Subbarao (Int. J. Comput. Biol. Drug Des., 2018)

A genetic programming-based approach and machine learning approaches to the classification of multiclass anti-malarial datasets. - Madhulata Kumari; Neeraj Tiwari; Naidu Subbarao (Int. J. Comput. Biol. Drug Des., 2018)

Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases. - Madhulata Kumari; Naidu Subbarao (Comput. Biol. Medicine, 2021)