学术文献库

Drug development is an expensive and time-consuming process where thousands of chemical compounds are being tested in order to find those possessing drug-like properties while being safe and effective. One of key parts of the early drug discovery process has become virtual drug screening -- a method used to narrow down search for potential drugs by running computer simulations of drug-target interactions. As these methods are known to demand huge amounts of computational power to get accurate results, prediction models based on machine learning techniques became a popular solution requiring less computational power as well as offering the ability to generate novel chemical structures for further research. Deep learning is to stay in drug discovery but has a long way to go. Only in the past few years with increases in computing power have researchers really started to embrace the potential of neural networks in various stages of the drug discovery process. While prediction methods promise great perspective in the future development of drug discovery they open new questions and challenges that still have to be solved.