Three-Dimensional Compound Comparison Methods and Their Application in Drug Discovery

Shin, Woong‐Hee; Zhu, Xiaolei; Bures, Mark G.; Kihara, Daisuke

doi:10.3390/molecules200712841

Cited by 60 publications

(61 citation statements)

References 62 publications

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“…[4] Other measures include 2D or 3D descriptors such as MOLPRINT 2D, [5] shape fingerprints, [6] pharmacophore, [7] and 3D structure comparison. [8,9] Using information from existing ligands makes this approach difficult to discover new classes of ligands for the receptor.…”

Section: Introductionmentioning

confidence: 99%

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

2016

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Structure-based virtual screening usually involves docking of a library of chemical compounds onto the functional pocket of the target receptor so as to discover novel classes of ligands. However, the overall success rate remains low and screening a large library is computationally intensive. An alternative to this "ab initio" approach is virtual screening by binding homology search. In this approach, potential ligands are predicted based on similar interaction pairs (similarity in receptors and ligands). SPOT-Ligand is an approach that integrates ligand similarity by Tanimoto coefficient and receptor similarity by protein structure alignment program SPalign. The method was found to yield a consistent performance in DUD and DUD-E docking benchmarks even if model structures were employed. It improves over docking methods (DOCK6 and AUTODOCK Vina) and has a performance comparable to or better than other binding-homology methods (FINDsite and PoLi) with higher computational efficiency. The server is available at http://sparks-lab.org. © 2016 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

2016

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“…Examples include a vector of measured or predicted physical properties (5-8), a vector enumerating the presence or absence of known functional groups on the ligand (9, 10), a vectorial representation of connectivities in the molecular graph (11,12) (known also as molecular fingerprints), and simply the 3D shape of the ligand (13)(14)(15)(16). Existing approaches then take the descriptor associated with each ligand and compare ligands with each other, for example through the Tanimoto coefficient (17,18).…”

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confidence: 99%

Predicting protein–ligand affinity with a random matrix framework

Lee

Brenner

Colwell

2016

Proc. Natl. Acad. Sci. U.S.A.

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Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal "signals" of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robust predictor of ligand-target affinity, performing as well or better than any algorithm in the published literature. We interpret our algorithm as deriving a model for the binding energy between a target receptor and the set of known ligands, where the underlying binding energy model is related to the classic Ising model in statistical physics.drug discovery | random matrix theory | protein-ligand affinity | computational pharmacology | statistical physics F inding new ligands that bind to a given target is both a crucial step and a major stumbling block in modern drug discovery. Numerous attempts have been made to develop computational algorithms to predict the binding affinity of a ligand to a given receptor, which would allow potential compounds to be screened in silico, reducing costs and saving time. In particular, in response to the wealth of experimental data that exists both within pharmaceutical companies, and also in freely accessible online databases such as ChEMBL (1), approaches that attempt to "learn" from these data are increasingly gaining attention (2).An intuitive data-driven approach builds on the hypothesis that chemical commonalities among the known ligand set reveal salient features of the binding site. A corollary is that ligands with similar chemical functionality are expected to share similar binding affinity toward a particular receptor (3,4). This suggests that the known ligand set of a given target can be used to learn criteria that predict whether a novel ligand will bind to the target. This ligand-based approach is a powerful paradigm that does not require structural information about the receptor, which is potentially arduous to obtain, unlike other more atomistic methods such as docking or molecular dynamics.Any ligand-based method requires a way to quantify the chemical functionalities of a ligand, and various chemical descriptors have been proposed. Examples include a vector of measured or predicted physical properties (5-8), a vector enumerating the presence or absence of known functional groups on the ligand (9, 10), a vectorial representation of connectivities in the molecular graph (11, 12) (known also as molecular fingerprints), and simply the 3D shape of the ligand (13-16). Existing approaches then take the descriptor associated with each ligand and compare ligands with each other, for example through the Tanimoto coefficient (17, 18).Nonetheless, regardless of how ligand chemical func...

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“…The five compound distances measure different aspects of compounds, and thus their distances are not necessarily consistent. Zernike (3D Zernike descriptors) compares global surface shape of molecules 28, 31, 36 . SIMCOMP evaluates 2D graph similarity of molecules 35 .…”

Section: Resultsmentioning

confidence: 99%

Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein–Ligand Binding Prediction in CSAR 2013 and 2014

Zhu

Shin

Kim

2015

J. Chem. Inf. Model.

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The Community Structure-Activity Resource (CSAR) benchmark exercise provides a unique opportunity for researchers to objectively evaluate the performance of protein-ligand docking methods. Patch-Surfer and PL-PatchSurfer, molecular surface-based methods for predicting binding ligands of proteins developed in our group, were tested on both CSAR 2013 and 2014 benchmark exercises in combination with an empirical scoring function-based method, AutoDock, while we only participated in CSAR 2013 using Patch-Surfer. The prediction results for Phase 1 task in CSAR 2013 showed that Patch-Surfer was able to rank all the four designed binding proteins within top ranks, outperforming AutoDock Vina. In Phase 2 of 2013, PL-PatchSurfer correctly selected the correct ligand pose for two target proteins. PL-PatchSurfer performed reasonably well in ranking ligands according to their binding affinity and in selecting near-native ligand poses in 2013 Phase 3 and 2014 Phase 1, respectively, although AutoDock Vina showed better performance. Lastly, in the 2014 Phase 2 exercise, the PL-PatchSurfer scores computed for ligands to target protein pairs correlated well with their pIC50 values, which was better or comparable to results by other participants. Overall, our methods showed fairly good performance in CSAR 2013 and 2014. Unique characteristics of the methods are discussed in comparison with AutoDock.

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Three-Dimensional Compound Comparison Methods and Their Application in Drug Discovery

Cited by 60 publications

References 62 publications

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

SPOT‐Ligand: Fast and effective structure‐based virtual screening by binding homology search according to ligand and receptor similarity

Predicting protein–ligand affinity with a random matrix framework

Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein–Ligand Binding Prediction in CSAR 2013 and 2014

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