2018
DOI: 10.1007/s10822-018-0126-x
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Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose

Abstract: We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performan… Show more

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Cited by 7 publications
(21 citation statements)
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“…Likewise, ComBind can be used with any pairwise pose similarity metric or combination thereof. ComBind's performance could potentially be improved by using more fine-grained interaction descriptors (41,42) or by using similarity metrics based on field-based methods developed for virtual screening (28,43).…”
Section: Extensibility and Future Workmentioning
confidence: 99%
“…Likewise, ComBind can be used with any pairwise pose similarity metric or combination thereof. ComBind's performance could potentially be improved by using more fine-grained interaction descriptors (41,42) or by using similarity metrics based on field-based methods developed for virtual screening (28,43).…”
Section: Extensibility and Future Workmentioning
confidence: 99%
“…The 3D QSAR study was generated by using 20 molecules from synthesis, 2-methoxyestradiol (1) and their negative logarithm of the IC 50 value (pIC 50 ) of MCF-7, performed with Surflex (Version 4.4) using leave-one-out cross validation method as previously described [41]. Twenty randomly selected molecules were calculated by Surflex-Quansa to build a 3D QSAR model (number of molecules to select for core multiple-alignment = 10) by leaving a single molecule out.…”
Section: Modelingmentioning
confidence: 99%
“…A 3D QSAR study was generated by using these 21 molecules and their pIC 50 value of MCF-7, performed with Surflex-Quansa using the leave-one-out cross validation method as previously described [41]. The molecules with low activity (pIC 50 of MCF-7 < 4) were in correlation with predicted values.…”
Section: Modelingmentioning
confidence: 99%
“…Here, we introduce a new 3D similarity method that combines the surface-based approach of Surflex-Sim and related methods [1416] with an electrostatic field comparison method that derives from the recently introduced QuanSA 3D-QSAR approach [17]. The method is called “eSim” for e lectrostatic-field and s urface- s hape sim ilarity (with the three “s” characters giving rise to the capital “S”).…”
Section: Introductionmentioning
confidence: 99%
“…Figure 1 depicts the central calculation within eSim, which is the computation of feature values at observer points, with the latter having been placed outside of a query molecule (also referred to as a “target molecule” in what follows). At each observer point, six values are computed:: The distance in Angstroms from the point to the atom surface, which corresponds to the minimum over the distances to each atom less that atom’s VdW radius.: The molecular electric field is characterized in terms of the Coulombic energy of moving a point charge of + 0.2 e to the observer point from an infinite distance (as in the QuanSA approach [17], with additional details provided in the Methods Section).: The minimum distance from the observer to any donor proton’s surface.: The angle formed by the observer point, the donor proton, and the atom attached to the donor proton.: The minimum distance from the observer to any acceptor atom’s surface.: The angle formed by the observer point, the acceptor atom, and the centroid of the atoms attached to the acceptor atom (180°, for example, when a pyridine nitrogen is oriented perfectly toward an observer).
Fig. 2Five small molecules (cyan) are shown in their optimal poses relative to the query/target 2-pyrrolidone (magenta, shown with yellow observer points, upper left), with their corresponding eSim scores
…”
Section: Introductionmentioning
confidence: 99%