Identification of Novel Antibacterials Using Machine Learning Techniques.

ABSTRACT: Many pharmaceutical companies are avoiding the development of novel antibacterials due to a range of rational reasons and the high risk of failure. However, there is an urgent need for novel antibiotics especially against resistant bacterial strains. Available in silico models suffer from many drawbacks and, therefore, are not applicable for scoring novel molecules with high structural diversity by their antibacterial potency. Considering this, the overall aim of this study was to develop an efficient in silico model able to find compounds that have plenty of chances to exhibit antibacterial activity. Based on a proprietary screening campaign, we have accumulated a representative dataset of more than 140,000 molecules with antibacterial activity against Escherichia coli

SUBMITTER: Ivanenkov YA 

PROVIDER: S-EPMC6719509 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature