ITS college of Pharmacy, Muradnagar, Ghaziabad-201206, Uttar Pradesh, India
The world needs pharmacological medication therapy for the diagnosis and treatment of numerous ailments. Time constraints make it necessary to find novel chemical compounds for the same, nevertheless, current methods of screening medicinal molecules and their target proteins cannot be completed as quickly as with AI. Assigning the right target during drug molecule development is crucial for effective treatment. A disease involves several proteins. Designing any medication molecule for its specific target over disease is considerably aided by predicting the structure or makeup of the targeted protein. AI can help in the creation of structure-based drugs by foreseeing the effect of a molecule on the target as well as safety concerns by anticipating a target protein's 3D structure in line with its chemical environment. Using the AI tool Alpha-Fold, which is based on DNNs, the 3D protein structure was predicted by examining the distances between neighbouring amino acids and the corresponding angles of the peptide bonds. In a study, RNN was used to predict the protein structure. A recurrent geometric network (RGN) is said to be composed of three stages: computation, geometry, and assessment. Here, the torsional angles for a particular residue and a partially formed backbone obtained from the geometric unit upstream of this served as the input and output for encoding the fundamental protein sequence. The final unit produced the 3D structure. As a result, it is cutting edge to use AI approaches to screen medications based on the study of target proteins.
Ishu Garg*, Harish, Mohit Kumar, Vishvanshi Tyagi, Nishant, Artificial Intelligence in Predicting Drug Target Proteins- A Review, Int. J. in Pharm. Sci., 2023, Vol 1, Issue 8, 72-78. https://doi.org/10.5281/zenodo.8229884