02349nas a2200229   4500000000100000000000100001008004100002260001200043653001800055653001000073653002100083653001900104100001800123700001400141700001500155245008600170856008300256300000900339490001300348520174400361022001402105       9998                            d  c11/202210aChemogenomics10aDrugs10aMachine Learning10aTarget Finding1 aA. Suruliandi1 aT. Idhaya1 aS. P. Raja00aDrug Target Interaction Prediction Using Machine Learning Techniques – A Review  uhttps://www.ijimai.org/journal/sites/default/files/2022-11/ip2022_11_002_0.pdf  a1-150 vIn Press3 aDrug discovery is a key process, given the rising and ubiquitous demand for medication to stay in good shape right through the course of one’s life. Drugs are small molecules that inhibit or activate the function of a protein, offering patients a host of therapeutic benefits. Drug design is the inventive process of finding new medication, based on targets or proteins. Identifying new drugs is a process that involves time and money. This is where computer-aided drug design helps cut time and costs. Drug design needs drug targets that are a protein and a drug compound, with which the interaction between a drug and a target is established. Interaction, in this context, refers to the process of discovering protein binding sites, which are protein pockets that bind with drugs. Pockets are regions on a protein macromolecule that bind to drug molecules. Researchers have been at work trying to determine new Drug Target Interactions (DTI) that predict whether or not a given drug molecule will bind to a target. Machine learning (ML) techniques help establish the interaction between drugs and their targets, using computer-aided drug design. This paper aims to explore ML techniques better for DTI prediction and boost future research. Qualitative and quantitative analyses of ML techniques show that several have been applied to predict DTIs, employing a range of classifiers. Though DTI prediction improves with negative drug target pairs (DTP), the lack of true negative DTPs has led to the use a particular dataset of drugs and targets. Using dynamic DTPs improves DTI prediction. Little attention has so far been paid to developing a new classifier for DTI classification, and there is, unquestionably, a need for better ones.  a1989-1660