Gaussian Processes:  A Method for Automatic QSAR Modeling of ADME Properties

Obrezanova, Olga; Csänyi, Gábor; Gola, Joelle M. R.; Segall, Matthew D.

doi:10.1021/ci7000633

Cited by 204 publications

(163 citation statements)

References 26 publications

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“…In the limit of an infinite number of hidden units, for specific transfer functions it is possible to reformulate equation (3) in the form of equation (1) with well-defined covariance functions [37][38][39] . For example, for h(q, u) = tanh(u 0 + u j q j ) the corresponding kernel is…”

Section: Potential Energy Surface Fittingmentioning

confidence: 99%

On representing chemical environments

2013

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We review some recently published methods to represent atomic neighbourhood environments, and analyse their relative merits in terms of their faithfulness and suitability for fitting potential energy surfaces. The crucial properties that such representations (sometimes called descriptors) must have are differentiability with respect to moving the atoms, and invariance to the basic symmetries of physics: rotation, reflection, translation, and permutation of atoms of the same species. We demonstrate that certain widely used descriptors that initially look quite different are specific cases of a general approach, in which a finite set of basis functions with increasing angular wave numbers are used to expand the atomic neighbourhood density function. Using the example system of small clusters, we quantitatively show that this expansion needs to be carried to higher and higher wave numbers as the number of neighbours increases in order to obtain a faithful representation, and that variants of the descriptors converge at very different rates. We also propose an altogether new approach, called Smooth Overlap of Atomic Positions (SOAP), that sidesteps these difficulties by directly defining the similarity between any two neighbourhood environments, and show that it is still closely connected to the invariant descriptors. We test the performance of the various representations by fitting models to the potential energy surface of small silicon clusters and the bulk crystal.

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Section: Potential Energy Surface Fittingmentioning

confidence: 99%

On representing chemical environments

2013

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“…[17][18][19][20] The models were built on the training sets and validated on the test sets. The best model for each data set was selected based on the performance on the test sets.…”

Section: Model Construction and Validationmentioning

confidence: 99%

Aggregation risk prediction for antibodies and its application to biotherapeutic development

Obrezanova

Arnell

Cuesta

et al. 2015

mAbs

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Aggregation is a common problem affecting biopharmaceutical development that can have a significant effect on the quality of the product, as well as the safety to patients, particularly because of the increased risk of immune reactions. Here, we describe a new high-throughput screening algorithm developed to classify antibody molecules based on their propensity to aggregate. The tool, constructed and validated on experimental aggregation data for over 500 antibodies, is able to discern molecules with a high aggregation propensity as defined by experimental criteria relevant to bioprocessing and manufacturing of these molecules. Furthermore, we show how this tool can be combined with other computational approaches during early drug development to select molecules with reduced risk of aggregation and optimal developability properties.

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“…In this procedure, the natural logarithms of g and s 2 were tuned simultaneously in a grid ranging from 0 to 10 with step size of 1 and the combination of g and s 2 that give rise to the RMSCV minimum was ultimately determined. [46] (iii) For the GP regression, we used a method proposed by Obrezanova et al [47] to assign the initial values for its hyperparameter set V consisting of 4 overall scales and m length scales (m = the number of variables used in the modeling), and these parameters were further optimized using the Polak-Ribiere conjugate gradient method to maximize its logarithmic marginal likelihood. A detailed description of this procedure can be found in our previous publications.…”

Section: Statistical Modelingmentioning

confidence: 99%

Predicting the Flexibility Profile of Ribosomal RNAs

Tian

Zhang

Xia

et al. 2010

Molecular Informatics

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Flexibility in biomolecules is an important determinant of biological functionality, which can be measured quantitatively by atomic Debye–Waller factor or B‐factor. Although numerous works have been addressed on theoretical and computational studies of the B‐factor profiles of proteins, the methods used for predicting B‐factor values of nucleic acids, especially the complicated ribosomal RNAs (rRNAs), which are very functionally similar to proteins in providing matrix structures and in catalyzing biochemical reactions, still remain unexploited. In this article, we present a quantitative structure–flexibility relationship (QSFR) study with the aim at the quantitative prediction of rRNA B‐factor based on primary sequences (sequence‐based) and advanced structures (structure‐based) by using both linear and nonlinear machine learning approaches, including partial least squares regression (PLS), least squares support vector machine (LSSVM), and Gaussian process (GP). By rigorously examining the performance and reliability of constructed statistical models and by comparing our models in detail to those developed previously for protein B‐factors, we demonstrate that (i) rRNA B‐factors could be predicted at a similar level of accuracy with that of protein, (ii) a structure‐based approach performed much better as compared to sequence‐based methods in modeling of rRNA B‐factors, and (iii) rRNA flexibility is primarily governed by the local features of nonbonding potential landscapes, such as electrostatic and van der Waals forces.

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Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties

Cited by 204 publications

References 26 publications

On representing chemical environments

On representing chemical environments

Aggregation risk prediction for antibodies and its application to biotherapeutic development

Predicting the Flexibility Profile of Ribosomal RNAs

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