Scoring ligand similarity in structure‐based virtual screening

Abstract

Scoring to identify high-affinity compounds remains a challenge in virtual screening. On one hand, protein-ligand scoring focuses on weighting favorable and unfavorable interactions between the two molecules. Ligand-based scoring, on the other hand, focuses on how well the shape and chemistry of each ligand candidate overlay on a three-dimensional reference ligand. Our hypothesis is that a hybrid approach, using ligand-based scoring to rank dockings selected by protein-ligand scoring, can ensure that high-ranking molecules mimic the shape and chemistry of a known ligand while also complementing the binding site. Results from applying this approach to screen nearly 70 000 National Cancer Institute (NCI) compounds for thrombin inhibitors tend to support the hypothesis. EON ligand-based ranking of docked molecules yielded the majority (4/5) of newly discovered, low to mid-micromolar inhibitors from a panel of 27 assayed compounds, whereas ranking docked compounds by protein-ligand scoring alone resulted in one new inhibitor. Since the results depend on the choice of scoring function, an analysis of properties was performed on the top-scoring docked compounds according to five different protein-ligand scoring functions, plus EON scoring using three different reference compounds. The results indicate that the choice of scoring function, even among scoring functions measuring the same types of interactions, can have an unexpectedly large effect on which compounds are chosen from screening. Furthermore, there was almost no overlap between the top-scoring compounds from protein-ligand versus ligand-based scoring, indicating the two approaches provide complementary information. Matchprint analysis, a new addition to the SLIDE (Screening Ligands by Induced-fit Docking, Efficiently) screening toolset, facilitated comparison of docked molecules' interactions with those of known inhibitors. The majority of interactions conserved among top-scoring compounds for a given scoring function, and from the different scoring functions, proved to be conserved interactions in known inhibitors. This was particularly true in the S1 pocket, which was occupied by all the docked compounds.

Challenges and advances in computational docking: 2009 in review

“…Several approaches have been tested to improve enrichment in VS, including: application of interaction fingerprints for postprocessing of docking results (91,98,134), consensus (84,86) and QM/MM (79) scoring, combining docking with pharmacophore perception (90,(134)(135)(136)(137)(138) or other ligand-based methods (64,(139)(140)(141), accounting for receptor flexibility (142)(143)(144). and use of machine-learning strategies (67,145).…”

Virtual Screening Strategies in Drug Design

“…Os cálculos de docagem representam a abordagem central utilizada na triagem baseada em estrutura. [39][40][41] As técnicas de docagem, desenvolvidas para predizer a melhor orientação e conformação de uma molécula ligante no seu sítio receptor, são empregadas há algum tempo no processo de planejamento de fármacos. A ideia geral é obter um conjunto de conformações do complexo ligante-receptor e classificá-las em ordem de prioridade com base em suas estabilidades energéticas.…”

The Challenge of Affinity Prediction: Scoring Functions for Structure‐Based Virtual Screening

“…There are many ways to reduce the number of compounds for docking and scoring by prefiltering. It is often done using simple molecular or physicochemical properties [172] or including a prescreening based on ligand similarity [173,174]. Such filters are sometimes followed by the application of pharmacophore models and/or docking constraints in the binding site.…”