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• W2889677957 abstract "Fingerprints (FPs) are the most common small molecule representation in cheminformatics. There are a wide variety of FPs, and the Extended Connectivity Fingerprint (ECFP) is one of the best-suited for general applications. Despite the overall FP abundance, only a few FPs represent the 3D structure of the molecule, and hardly any encode protein-ligand interactions.Here, we present a Protein-Ligand Extended Connectivity (PLEC) FP that implicitly encodes protein-ligand interactions by pairing the ECFP environments from the ligand and the protein. PLEC FPs were used to construct different machine learning models tailored for predicting protein-ligand affinities (pKi∕d). Even the simplest linear model built on the PLEC FP achieved Rp = 0.817 on the Protein Databank (PDB) bind v2016 'core set', demonstrating its descriptive power.The PLEC FP has been implemented in the Open Drug Discovery Toolkit (https://github.com/oddt/oddt).Supplementary data are available at Bioinformatics online." @default.
• W2889677957 created "2018-09-27" @default.
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• W2889677957 date "2018-09-08" @default.
• W2889677957 modified "2023-10-12" @default.
• W2889677957 title "Development of a protein–ligand extended connectivity (PLEC) fingerprint and its application for binding affinity predictions" @default.
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• W2889677957 doi "https://doi.org/10.1093/bioinformatics/bty757" @default.
• W2889677957 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6477977" @default.
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