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

• Published in: Bioorganic & medicinal chemistry letters Vol. 38; p. 127852
• Main Authors: Gaur, Neeraj K., Goyal, Venuka Durani, Kulkarni, Kiran, Makde, Ravindra D.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.04.2021
• Subjects: Drug design; Ligands; Machine Learning; Mini-proteins; Protein therapeutics; Proteins - antagonists & inhibitors; Small Molecule Libraries - chemical synthesis; Small Molecule Libraries - chemistry; Small Molecule Libraries - pharmacology; Virtual screening; Virtual screening; Mini-proteins; Protein therapeutics; Drug design; Machine learning
• ISSN: 0960-894X; 1464-3405
• DOI: 10.1016/j.bmcl.2021.127852

[Display omitted] De novo design of mini-proteins (4–12 kDa) has recently been shown to produce new candidates for protein therapeutics. They are temperature stable molecules that bind to the drug target with high affinity for inhibiting its interactions. The development of mini-protein binders requires laboratory screening of tens of thousands of molecules for effective target binding. 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.