Structural Protein–Ligand Interaction Fingerprints (SPLIF) for Structure-Based Virtual Screening: Method and Benchmark Study

By C. Da, D. Kireev. 2014

151 citations recorded by the source corpus.

Accurate and affordable assessment of ligand–protein affinity for structure-based virtual screening (SB-VS) is a standing challenge. Hence, empirical postdocking filters making use of various types of structure–activity information may prove useful. Here, we introduce one such filter based upon three-dimensional structural protein–ligand interaction fingerprints (SPLIF). SPLIF permits quantitative assessment of whether a docking pose interacts with the protein target similarly to a known ligand and rescues active compounds penalized by poor initial docking scores. An extensive benchmark study on 10 diverse data sets selected from the DUD-E database has been performed in order to evaluate the absolute and relative efficiency of this method. SPLIF demonstrated an overall better performance than relevant standard methods.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

In Pharmacology

Citations in the corpus

AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, background): “Basic structural features can vary from a simple enumeration of atom types to more complex protein-ligand descriptors such as SPLIF [31], SIFt [32], or APIF [33].”
Machine-learning methods for ligand-protein molecular docking. (2021, background): “The second category is based on interactions ﬁngerprinting, the most iconic of which are Structural Interaction Fingerprint (SIFt) [52] and Structural Protein-Ligand Inter-action Fingerprint (SPLIF) [53].”