LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening
LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening is a scholarly work by Viet-Khoa Tran-Nguyen and Didier Rognan, published in 2020 in ''Journal of Chemical Information and Modeling''. The main subjects of the publication include drug design, artificial intelligence, data mining, PubChem, virtual screening, protein structure prediction, set, machine learning, similarity, computer science, materials informatics industry, and base rate fallacy. The authors herewith present a novel data set (LIT-PCBA) specifically designed for virtual screening and machine learning.