Regression Analysis

Williams, Leslie Lea; Quave, Kylie

doi:10.1016/b978-0-12-812775-9.00009-8

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…As justified by Williams, "great accuracy in the weights is not necessary." 53 In practice, if a practitioner knows that a transformation is error-prone in their protocol, they should reduce that transformation's presence in the design. This is achieved by decreasing the weight of such a transformation.…”

Section: Considerations When Planning Perturbation Mapsmentioning

confidence: 99%

To Design Scalable Free Energy Perturbation Networks, Optimal Is Not Enough

Pitman

Hahn

Tresadern

et al. 2023

J. Chem. Inf. Model.

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Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the simulation experiment, using graphs where nodes represent ligands and graph edges represent alchemical transformations between ligands. Recent work demonstrated that optimizing the statistical architecture of these perturbation graphs improves the accuracy of the predicted changes in the free energy of ligand binding. Therefore, to improve the success rate of computational drug discovery, we present the open-source software package High Information Mapper (HiMap)�a new take on its predecessor, Lead Optimization Mapper (LOMAP). HiMap removes heuristics decisions from design selection and instead finds statistically optimal graphs over ligands clustered with machine learning. Beyond optimal design generation, we present theoretical insights for designing alchemical perturbation maps. Some of these results include that for n number of nodes, the precision of perturbation maps is stable at n•ln(n) edges. This result indicates that even an "optimal" graph can result in unexpectedly high errors if a plan includes too few alchemical transformations for the given number of ligands and edges. And, as a study compares more ligands, the performance of even optimal graphs will deteriorate with linear scaling of the edge count. In this sense, ensuring an A-or D-optimal topology is not enough to produce robust errors. We additionally find that optimal designs will converge more rapidly than radial and LOMAP designs. Moreover, we derive bounds for how clustering reduces cost for designs with a constant expected relative error per cluster, invariant of the size of the design. These results inform how to best design perturbation maps for computational drug discovery and have broader implications for experimental design.

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Section: Considerations When Planning Perturbation Mapsmentioning

confidence: 99%

To Design Scalable Free Energy Perturbation Networks, Optimal Is Not Enough

Pitman

Hahn

Tresadern

et al. 2023

J. Chem. Inf. Model.

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“…In this case, the completeness of the study can provide not only the elucidation of the properties and nature of its constituent elements, but also the establishment of relationships and interactions between these components and the organism. Such opportunities are provided by regression analysis, which is a rather informative means of finding interdependencies in establishing relationships between the quantitative parameters that characterize an organism's state [1,7,16,17].…”

Section: Introductionmentioning

confidence: 99%

Potentiated Alimentary Iodine Deficiency: Features of Relationships Between the Follicular Thyrocytes’ Energy Capability Profile Ultrastructures When Corrected by Different Doses of Organic Iodine

Ryabukha¹

2022

Gìg. nasel. mìscʹ

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The complexity and diversity of the structure and activity of the body cause the involvement of mathematical apparatus for its study. For the first time, be means of the regression analysis, it was investigated the possibility of using the interdependence of weight of thyroid gland, adrenal glands, liver, and body weight indicators as clinical features of hypothyroidism and hyperthyroidism. The study was conducted on white rats under model conditions of alimentary hypothyroidism and thyreoidinum-induced hyperthyroidism. Such a clinical marker of hypothyroidism has been found out to be a simultaneous increase in thyroid weight and a decrease in adrenal weight, which occur against the background of weight gain. A decrease in thyroid weight along with an increase in adrenals and liver weights is a clinical marker of hyperthyroidism. The studies have confirmed the fundamental view of body weight as an informative indicator of the general state of the organism, as well as our earlier assumptions about the affiliation of the thyroid gland, adrenal glands, and liver to a single functional module.

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Regression Analysis

Cited by 2 publications

References 2 publications

To Design Scalable Free Energy Perturbation Networks, Optimal Is Not Enough

To Design Scalable Free Energy Perturbation Networks, Optimal Is Not Enough

Potentiated Alimentary Iodine Deficiency: Features of Relationships Between the Follicular Thyrocytes’ Energy Capability Profile Ultrastructures When Corrected by Different Doses of Organic Iodine

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