Quantitative Binding Site Model Generation: Compass Applied to Multiple Chemotypes Targeting the 5-HT1A Receptor

Jain, Ajay N.; Harris, Nomi L.; Park, John Y.

doi:10.1021/jm00008a008

Cited by 54 publications

(63 citation statements)

References 8 publications

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“…This issue was approached in the same manner that the Compass algorithm approaches the problem [5][6][7]. The training algorithm iterates parameter estimation and ligand pose optimization.…”

Section: Resultsmentioning

confidence: 99%

“…The hydrophobic effect is captured by the weighted sum of a Gaussian-like function g and a sigmoid function s of pairwise surface distances as in Compass [5][6][7]:…”

Section: Hydrophobic Complementaritymentioning

confidence: 99%

“…c Ligands were docked into the binding sites using Hammerhead [11]. basis functions with tunable parameters estimated by iterative gradient descent [5][6][7]. Apart from the Compass functional approach, this work borrows most directly from B6hm [8] and from Bohacek and McMartin [9].…”

Section: Introductionmentioning

confidence: 99%

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Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities

Jain

1996

J Computer-Aided Mol Des

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570

378

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Exploitation of protein structures for potential drug leads by molecular docking is critically dependent on methods for scoring putative protein-ligand interactions. An ideal function for scoring must exhibit predictive accuracy and high computational speed, and must be tolerant of variations in the relative protein-ligand molecular alignment and conformation. This paper describes the development of an empirically derived scoring function, based on the binding affinities of protein-ligand complexes coupled with their crystallographically determined structures. The function's primary terms involve hydrophobic and polar complementarity, with additional terms for entropic and solvation effects. The issue of alignment/conformation dependence was solved by constructing a continuous differentiable nonlinear function with the requirement that maxima in ligand conformation/alignment space corresponded closely to crystallographically determined structures. The expected error in the predicted affinity based on cross-validation was 1.0 log unit. The function is sufficiently fast and accurate to serve as the objective function of a molecular-docking search engine. The function is particularly well suited to the docking problem, since it has spatially narrow maxima that are broadly accessible via gradient descent.

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Section: Resultsmentioning

confidence: 99%

“…The hydrophobic effect is captured by the weighted sum of a Gaussian-like function g and a sigmoid function s of pairwise surface distances as in Compass [5][6][7]:…”

Section: Hydrophobic Complementaritymentioning

confidence: 99%

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Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities

Jain

1996

J Computer-Aided Mol Des

Self Cite

570

378

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show abstract

“…Rather than taking the precise pose from a crystal structure, the approach is to find the nearest local optimum and define the score at that optimum as the score for the ligand. This follows the approach developed for Compass, which established the conceptual framework for this approach, termed multiple instance learning within the computational machine learning field [36][37][38][39]. The scoring function was tuned to predict the binding affinities of 34 protein/ligand complexes (overlapping significantly with the Bohm training set), with its output being represented in units of -log(K d ) [2].…”

Section: Scoring Functionmentioning

confidence: 99%

Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search

Jain

2007

J Comput Aided Mol Des

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544

502

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The Surflex flexible molecular docking method has been generalized and extended in two primary areas related to the search component of docking. First, incorporation of a small-molecule force-field extends the search into Cartesian coordinates constrained by internal ligand energetics. Whereas previous versions searched only the alignment and acyclic torsional space of the ligand, the new approach supports dynamic ring flexibility and allatom optimization of docked ligand poses. Second, knowledge of well established molecular interactions between ligand fragments and a target protein can be directly exploited to guide the search process. This offers advantages in some cases over the search strategy where ligand alignment is guided solely by a ''protomol'' (a pre-computed molecular representation of an idealized ligand). Results are presented on both docking accuracy and screening utility using multiple publicly available benchmark data sets that place Surflex's performance in the context of other molecular docking methods. In terms of docking accuracy, Surflex-Dock 2.1 performs as well as the best available methods. In the area of screening utility, Surflex's performance is extremely robust, and it is clearly superior to other methods within the set of cases for which comparative data are available, with roughly double the screening enrichment performance.

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“…For each parameter setting, it shows the pair rank correlation coefficient (PRCC) of the results relative to the default values. The PRCC [15], a measure closely related to Kendall's r [16], is defined as the probability, given two randomly chosen members of a set, that they are ranked the same way in both orderings. The table shows that, even with the polar w values increased threefold, there is an 89% probability that the ranking of two randomly chosen molecules was unchanged.…”

Section: Parameter Sensitivitymentioning

confidence: 99%

The measurement of molecular diversity: A three-dimensional approach

Chapman¹

1996

J Computer-Aided Mol Des

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This paper describes a method for selecting a small, highly diverse subset from a large pool of molecules. The method has been employed in the design of combinatorial synthetic libraries for use in high-throughput screening for pharmaceutical lead generation. It computes diversity in terms of the main factors relevant to ligand-protein binding, namely the three-dimensional arrangement of steric bulk and of polar functionalities and molecular entropy. The method was used to select a set of 20 carboxylates suitable for use as side-chain precursors in a polyamine-based library. The method depends on estimates of various physical-chemical parameters involved in ligand-protein binding; experiments examined the sensitivity of the method to these parameters. This paper compares the diversity of randomly and rationally selected side-chain sets; the results suggest that careful design of synthetic combinatorial libraries may increase their effectiveness several-fold.

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Quantitative Binding Site Model Generation: Compass Applied to Multiple Chemotypes Targeting the 5-HT1A Receptor

Cited by 54 publications

References 8 publications

Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities

Scoring noncovalent protein-ligand interactions: A continuous differentiable function tuned to compute binding affinities

Surflex-Dock 2.1: Robust performance from ligand energetic modeling, ring flexibility, and knowledge-based search

The measurement of molecular diversity: A three-dimensional approach

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