Various pharmacological, genetic and immunity related reasons contribute to the recorded Adverse Drug Reactions (ADR) either directly or indirectly. The genetic factors are not limited to ethnicity, age and demographics, social and economical and gender factors. Such factors are considered to be the secondary reasons, whereas the primary reasons indicate the interactions between drug-drug, drug-protein and protein-protein entities. With advanced algorithms in Machine Learning and Data Science, the prediction of pharmacovigilance has reached greater heights in the recent past. The conventional methods are time-consuming and demands huge intervention from experts and manufacturers. The models built with machine learning models have simplified the analysis and regression models have identified serious adverse drug reactions better than conventional methods. Allergies, organ failures and haemorrhages are considered for this research work, focusing on the parameters such as age, number of medications consumed, number of illnesses affecting the specific patient, dosage, type of medical institution, previous or genetic history of adverse reactions to medicines, type of consumptions and method of medication intake. From the investigative results, elderly patients affected by multiple illness are bound to multiple medicine intake and thus are subjected to serious adverse drug reactions. Owing to the fact, the monitoring period should be shortened and supervised accordingly. Primary responsibility of medical institutions lies in monitoring the previous history of adverse reactions and the new symptoms upon consumption of new medicine. The proposed approach carefully studies the relationships between various factors and computes in a binary logistic model for effective detection and prediction. The outcomes of the proposed model justify the need for additional parameters for a promising accuracy in detection and prediction of ADRs.

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The mutation is a critical element in determining the proteins' stability, becoming a core element in portraying the effects of a drug in the pharmaceutical industry. Doing wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential mutations, computational approaches that can reliably anticipate the consequences of amino acid mutations are critical. This work presents a robust methodology to analyze and identify the effects of mutation on a single protein structure. Initially, the context in a collection of words is determined using a knowledge graph for feature selection purposes. The proposed prediction is based on an easier and simpler logistic regression inferred binary classification technique. This approach can able to obtain a classification accuracy (AUC) Area Under the Curve of 87% when randomly validated against experimental energy changes. Moreover, for each cross-fold validation, the precision, recall, and F-Score are presented. These results support the validity of our strategy since it performs the vast majority of prior studies in this domain.

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