Machine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics

In this study we trained machine learning classifiers which can distinguish, with 90% accuracy and 80% precision, mini-protein binders from non-binding molecules designed for a particular target; this significantly reduces the number of mini protein candidates for experimental screening. Further, on the basis of our results we propose a multi-stage protocol where a small dataset (few hundred experimentally verified target-specific mini-proteins) can be used to train classifiers for improving the efficiency of mini-protein design for any specific target.PMID:33609660 | DOI:10.1016/j.bmcl.2021.127852

Source: Bioorganic and Medicinal Chemistry Letters - February 20, 2021 Category: Chemistry Authors: Neeraj K Gaur Venuka Durani Goyal Kiran Kulkarni Ravindra D Makde Source Type: research