Machine Learning for in Silico Protein Research

Abstract

In silico protein research has been a focus for a long time, while its recent combination with machine learning gives great contributions to related areas. This review mainly focuses on four major fields of in silico protein research that combine with machine learning, which are molecular dynamics, structure prediction, property prediction and molecule design. Molecular dynamics depends on force field parameters, which is necessary for accurate results. Machine learning can help to construct satisfied force field parameters. In molecular dynamics, machine learning can also help to do the free energy calculation by a relatively low cost. Structure prediction is usually predicting the structure of given sequence. Structure prediction is of high complexity and data amount, which is what machine learning good at. By the help of machine learning, scientists have gained great achievements in 3D structure prediction of proteins. On the other hand, predicting proteins’ properties based on their known information is also important to protein research. The most challenging, however, is molecule design. Though drug-like small molecule design and protein design have achieved much in recent years, this field is still remain to be explored. This review focuses on the above three areas and make an outlook to in silico protein research with machine learning.