Introduction

We have developed an Artificial Intelligence model which is one of the best applications of AI (Artificial Intelligence) for pharmaceutical companies. This model take drug names as inputs and according to chemical and biological features and examining different types of drugs like enzymes, chemical etc. It also predicts side effects which are associated with new drugs which can be made from two or more drugs. So, let’s see some insight about this model. How this model is working and what are the features and concepts are used to make this model work in an efficient way.

Data set used for AI model

This model is built using deep neural networks which is multi-perceptron model. Here by every neuron we mean some kind of function and activation function for that layer. These neurons are inspired by human brain and learns new things in the same way.

For training our model of neural nets data of 832 Medicines is used where each medicine have 40260 features. We have used two hidden layers in our program which 2200 and 202 neurons in each layer consecutively.

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