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In the drug discovery path, most drug candidates failed at the early stages due to their pharmacokinetic behavior in the system. Early prediction of pharmacokinetic properties and screening methods can reduce the time and investment for lead discoveries. Plasma protein binding is one of these properties which has a vital role in drug discovery and development. The focus of the current study is to develop a computational model for the classification of Low Plasma Protein Binding (LPPB) and High Plasma Protein Binding (HPPB) drugs using machine learning methods for early screening of molecules through WEKA. Plasma protein binding drugs data was collated from the Drug Bank database where 617 drug candidates were found to interact with plasma proteins, out of which an equal proportion of high and low plasma protein binding drugs were extracted to build a training set of ~300 drugs. The machine learning algorithms were trained with a training set and evaluated by a test set. We also compared various machine learning-based classification algorithms i.e., the Naïve Bayes algorithm, Instance-Based Learner (IBK), multilayer perceptron, and random forest to determine the best model based on accuracy. It was observed that the random forest algorithm-based model outperforms with an accuracy of 99.67% and 0.9933 kappa value on training set and on test set as compared to other classification methods and can predict drug plasma binding capacity in the given data set using the WEKA tool.

It’s challenging to understand the multifarious gene regulatory networks. Diverse biological processes, are mostly conferred by nature of cis- acting regulatory elements (CARE) located within the regulatory region which include abiotic stress responses, hormone responses and developmental processes. Thus, the identification of CARE and their organization is a crucial step for better understanding of spatiotemporal expression of the gene. The putative cis- acting element that are located within the 5' regulatory region of BADH isozymes of Hordeum vulgare , Oryza sativa , Sorghum bicolor , Leymus chinensis, Zoysia tenuifolia, Arabidopsis thaliana , Amaranthus hypochondriacus , Chrysanthemum lavandulifolium , Populus euphratica and Atriplex prostrate were identified using bioinformatics tools. Several probable cis-acting regulatory elements that are coupled with cellular development, hormonal and stress response were identified as: Me-JA, GARE, GT-1, MYB, W-box, I-box, Pyrimidine-box, ANAERO, ARF, CARE, ASF1, SORLIP, SURE, TAAAGSTKST1, GCC Box, CICADIAN, Sp1 and CGCG Box. These results shed light on probable cis- acting regulatory elements these BADH isoenzyme that confer their expression and regulation during cellular, hormonal and stress response.