Anti-Drug Response and Drug Side Effect Prediction Methods: A Review

Abstract

Predicting medication (drug) adverse reactions is a crucial aspect of drug development. Using simulation techniques to customize medication response predictions to identify the optimal medication holds enormous potential for improving a patient’s probability of successful treatment. Unfortunately, the statistical effort of forecasting medication reaction is extremely difficult, partly due to dataset limitations and also due to algorithms flaws. Medications are organic molecules that are ingested by humans as well as cause a response in the body through engaging to target proteins. These medications can reduce positive or negative effects inside the organisms. The undesirable modifications in medications cause adverse reactions inside the human organism generally referred to as medication side effects. Such adverse effects might vary from mild occurrences like a migraine to more significant ones involving heart failure, malignancy, or perhaps even mortality. The medications are put through a series of tests in the laboratories to see if they have any negative health effects. Such tests, unfortunately, are both expensive and time-consuming. Numerical techniques can be used as a replacement for experimental research. Numerous computational strategies for detecting drug adverse reactions have recently been published. In this paper, several existing techniques of drug response prediction are surveyed. The Pearson correlation coefficient value of existing techniques is compared graphically and find that SRMF technique had attained the best results. The existing methods are examined with the help of various databases. The most essential databases for drug adverse reactions prediction are BIO-SNAP, SIDER, DART, etc. The sets of data associated with drug adverse reactions, as well as the parameters used to evaluate drug adverse events prediction techniques, have been described. For the evaluation of drug adverse reactions prediction techniques PCC, precision, accuracy, etc., parameters are used. For the prediction of drug side effects, several methods are used such as docking-based methods, network-based methods, machine learning-based methods, and various miscellaneous methods.