FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2529291290> ?p ?o ?g. }

• W2529291290 abstract "A drug is typically a small molecule that interacts with the binding site of some target protein. Drug design involves the optimization of this interaction so that the drug effectively binds with the target protein while not binding with other proteins (an event that could produce dangerous side effects). Computational drug design involves the geometric modeling of drug molecules, with the goal of generating similar molecules that will be more effective drug candidates. It is necessary that algorithms incorporate strategies to measure molecular similarity by comparing molecular descriptors that may involve dozens to hundreds of attributes. We use kernel-based methods to define these measures of similarity. Kernels are general functions that can be used to formulate similarity comparisons. The overall goal of this thesis is to develop effective and efficient computational methods that are reliant on transparent mathematical descriptors of molecules with applications to affinity prediction, detection of multiple binding modes, and generation of new drug leads. While in this thesis we derive computational strategies for the discovery of new drug leads, our approach differs from the traditional ligandbased approach. We have developed novel procedures to calculate inverse mappings and subsequently recover the structure of a potential drug lead. The contributions of this thesis are the following: 1. We propose a vector space model molecular descriptor (VSMMD) based on a vector space model that is suitable for kernel studies in QSAR modeling. Our experiments have provided convincing comparative empirical evidence that our descriptor formulation in conjunction with kernel based regression algorithms can provide sufficient discrimination to predict various biological activities of a molecule with reasonable accuracy. iii 2. We present a new component selection algorithm KACS (Kernel Alignment Component Selection) based on kernel alignment for a QSAR study. Kernel alignment has been developed as a measure of similarity between two kernel functions. In our algorithm, we refine kernel alignment as an evaluation tool, using recursive component elimination to eventually select the most important components for classification. We have demonstrated empirically and proven theoretically that our algorithm works well for finding the most important components in different QSAR data sets. 3. We extend the VSMMD in conjunction with a kernel based clustering algorithm to the prediction of multiple binding modes, a challenging area of research that has been previously studied by means of time consuming docking simulations. The results reported in this study provide strong empirical evidence that our strategy has enough resolving power to distinguish multiple binding modes through the use of a standard k-means algorithm. 4. We develop a set of reverse engineering strategies for QSAR modeling based on our VSMMD. These strategies include: (a) The use of a kernel feature space algorithm to design or modify descriptor image points in a feature space. (b) The deployment of a pre-image algorithm to map the newly defined descriptor image points in the feature space back to the input space of the descriptors. (c) The design of a probabilistic strategy to convert new descriptors to meaningful chemical graph templates. The most important aspect of these contributions is the presentation of strateiv gies that actually generate the structure of a new drug candidate. While the training set is still used to generate a new image point in the feature space, the reverse engineering strategies just described allows us to develop a new drug candidate that is independent of issues related to probability distribution constraints placed on test set molecules." @default.
• W2529291290 created "2016-10-14" @default.
• W2529291290 creator A5058844359 @default.
• W2529291290 date "2009-05-14" @default.
• W2529291290 modified "2023-09-24" @default.
• W2529291290 title "Kernel Methods in Computer-Aided Constructive Drug Design" @default.
• W2529291290 cites W1510073064 @default.
• W2529291290 cites W1514876195 @default.
• W2529291290 cites W1526472065 @default.
• W2529291290 cites W1531524766 @default.
• W2529291290 cites W1538813063 @default.
• W2529291290 cites W1542652324 @default.
• W2529291290 cites W1545231783 @default.
• W2529291290 cites W1547036528 @default.
• W2529291290 cites W1567427264 @default.
• W2529291290 cites W1568341987 @default.
• W2529291290 cites W1576520375 @default.
• W2529291290 cites W1582707787 @default.
• W2529291290 cites W1585772109 @default.
• W2529291290 cites W1607350267 @default.
• W2529291290 cites W1656114533 @default.
• W2529291290 cites W1965216736 @default.
• W2529291290 cites W1970883384 @default.
• W2529291290 cites W1971582954 @default.
• W2529291290 cites W1976780887 @default.
• W2529291290 cites W1977405994 @default.
• W2529291290 cites W1983304433 @default.
• W2529291290 cites W1984471054 @default.
• W2529291290 cites W1988771765 @default.
• W2529291290 cites W1995852445 @default.
• W2529291290 cites W2003712181 @default.
• W2529291290 cites W2004727465 @default.
• W2529291290 cites W2007436490 @default.
• W2529291290 cites W2007698848 @default.
• W2529291290 cites W2013279548 @default.
• W2529291290 cites W2015160568 @default.
• W2529291290 cites W2016462598 @default.
• W2529291290 cites W2016703634 @default.
• W2529291290 cites W2017398555 @default.
• W2529291290 cites W2019980219 @default.
• W2529291290 cites W2022331577 @default.
• W2529291290 cites W2024587346 @default.
• W2529291290 cites W2026610312 @default.
• W2529291290 cites W2032245355 @default.
• W2529291290 cites W2033257206 @default.
• W2529291290 cites W2034038739 @default.
• W2529291290 cites W2041146183 @default.
• W2529291290 cites W2050456292 @default.
• W2529291290 cites W2053080554 @default.
• W2529291290 cites W2055526753 @default.
• W2529291290 cites W2055749903 @default.
• W2529291290 cites W2058119599 @default.
• W2529291290 cites W2061111857 @default.
• W2529291290 cites W2063060349 @default.
• W2529291290 cites W2064440950 @default.
• W2529291290 cites W2067818689 @default.
• W2529291290 cites W2071958671 @default.
• W2529291290 cites W2073503722 @default.
• W2529291290 cites W2081301924 @default.
• W2529291290 cites W2081982874 @default.
• W2529291290 cites W2088515011 @default.
• W2529291290 cites W2091823093 @default.
• W2529291290 cites W2095691435 @default.
• W2529291290 cites W2103300124 @default.
• W2529291290 cites W2105732805 @default.
• W2529291290 cites W2108710077 @default.
• W2529291290 cites W2113242816 @default.
• W2529291290 cites W2114759952 @default.
• W2529291290 cites W2114779636 @default.
• W2529291290 cites W2118578744 @default.
• W2529291290 cites W2118730797 @default.
• W2529291290 cites W2119479037 @default.
• W2529291290 cites W2124608293 @default.
• W2529291290 cites W2133396101 @default.
• W2529291290 cites W2135835957 @default.
• W2529291290 cites W2136507529 @default.
• W2529291290 cites W2136651963 @default.
• W2529291290 cites W2137262074 @default.
• W2529291290 cites W2141407058 @default.
• W2529291290 cites W2141873373 @default.
• W2529291290 cites W2142387771 @default.
• W2529291290 cites W2142602210 @default.
• W2529291290 cites W2143426320 @default.
• W2529291290 cites W2143586989 @default.
• W2529291290 cites W2148797284 @default.
• W2529291290 cites W2156909104 @default.
• W2529291290 cites W2159387294 @default.
• W2529291290 cites W2160191519 @default.
• W2529291290 cites W2160795665 @default.
• W2529291290 cites W2161104773 @default.
• W2529291290 cites W2165793886 @default.
• W2529291290 cites W2166209362 @default.
• W2529291290 cites W2200810672 @default.
• W2529291290 cites W2201106676 @default.
• W2529291290 cites W2204819850 @default.
• W2529291290 cites W2213735563 @default.
• W2529291290 cites W2215353492 @default.
• W2529291290 cites W2217436801 @default.
• W2529291290 cites W2255883267 @default.
• W2529291290 cites W2318794083 @default.

• next