Using Data Mining to Identify Drug Interactions and Adverse Effects

In the past decades, deep learning has achieved significant success in various fields of artificial intelligence research. This technology has shown better performance than other machine learning algorithms in areas such as identification, voice recognition and natural language processing, etc. The first wave of using deep learning in pharmaceutical research has emerged in recent years, and its application has gone beyond biophysical prediction and promises to address various problems in drug discovery. Examples of problems in biophysical prediction, molecular design, synthesis prediction and biological image analysis will be discussed. The high demand for discovery and analysis of big data has encouraged the use of complex machine algorithms as deep learning. It has made great achievements in the field of applications such as computer games, machine vision speech recognition and natural language processing. It is safe to say that DL is changing our daily life. The top ۱۰ technology trends selected for ۲۰۱۸ show that artificial intelligence technology is ranked first. In the past, there has been a significant increase in the amount of available synthetic activity and biomedical data due to the emergence of new experimental methods such as parallel synthesis, among others.