A5005520674- Computational Drug Discovery Methods
- Spectroscopy and Chemometric Analyses
- Pharmacogenetics and Drug Metabolism
- Water Quality Monitoring and Analysis
- Drug Transport and Resistance Mechanisms
- Spectroscopy Techniques in Biomedical and Chemical Research
- Metabolomics and Mass Spectrometry Studies
- Imbalanced Data Classification Techniques
- Bayesian Modeling and Causal Inference
- Analytical Chemistry and Chromatography
- Data Stream Mining Techniques
- Plant biochemistry and biosynthesis
- Drug-Induced Hepatotoxicity and Protection
- Advanced Chemical Sensor Technologies
- Fault Detection and Control Systems
- Machine Learning and Data Classification
- Remote-Sensing Image Classification
- Neural Networks and Applications
- Microbial Metabolic Engineering and Bioproduction
- Bayesian Methods and Mixture Models
Pfizer (United States)
2020-2023
GlaxoSmithKline (Netherlands)
2008
University of Delaware
2002-2007
Research Triangle Park Foundation
2004-2006
GlaxoSmithKline (United States)
2004
Abstract In this tutorial, traditional decision tree construction and the current state of modeling are reviewed. Emphasis is placed on techniques that make trees well suited to handle complexities chemical biochemical applications. Copyright © 2004 John Wiley & Sons, Ltd.
Accurate prediction of human pharmacokinetics (PK) remains one the key objectives drug metabolism and PK (DMPK) scientists in discovery projects. This is typically performed by using vitro-in vivo extrapolation (IVIVE) based on mechanistic models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes predict future events, has gained increased popularity application absorption, distribution, metabolism, excretion (ADME) sciences. study compares...
The transfer of partial least squares (PLS) calibration models among four near-infrared spectrometers was investigated for the quantitative analysis thermoset resin polymers. A comparative study second derivatives, multiplicative scatter correction, finite impulse response filtering, slope and bias model updating (MU), orthogonal signal correction (OSC) conducted to determine which processing methods achieved transferability. It is shown that OSC MU were superior other methods, leading very...
Abstract The wavelet transform is a relatively new method that partitions signal into components which differ in the frequency of their features. Chemical data can be thought as being composed several different components, some more analytically significant than others. Therefore, used to chemical called scales, underlying signal. These scales possess noise ratios, or alternatively contain amounts information, original data. In this manuscript, two classification problems are demonstrate how...
Abstract In this paper a novel signal‐preprocessing technique that combines the local and multiscale properties of wavelet prism with global filtering capability orthogonal signal correction (OSC) is presented for pretreatment spectroscopic data. hybrid method, referred to as OSC (WOSC), separate filter applied each frequency component generated from decomposition. The combination shown be complementary, prediction results obtained in subsequent partial least squares (PLS) calibrations using...
Organic anion transporter 2 (OAT2 or SLC22A7) plays an important role in the hepatic uptake and renal secretion of several endogenous compounds drugs. The goal this work is to understand structure activity OAT2 inhibition assess clinical drug interaction risk. A single-point assay using OAT2-transfected HEK293 cells was employed screen about 150 compounds; concentration-dependent potency (IC50) measured for identified "inhibitors". Acids represented 65% all inhibitors, frequency...
Abstract Many standard classification methods are incapable of handling missing values in a sample. Instead, these must rely on external filling order to estimate the values. The hybrid network proposed this paper is an extension classifier that robust produced by performing empirical Bayesian structure learning create retains its ability presence data both training and test cases. performance measured calculating misclassification rate when removed from dataset. These curves then compared...
Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as distribution coefficient (LogD) – miniaturized shake-flask method (SFLogD) and chromatographic (ELogD). The results from these assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, ability predict...
Lipophilicity is a fundamental structural property that influences almost every aspect of drug discovery. Within Pfizer, we have two complementary high-throughput screens for measuring lipophilicity as distribution coefficient (LogD) – miniaturized shake-flask method (SFLogD) and chromatographic (ELogD). The results from these assays are not the same (see Figure 1), with each assay being applicable or more reliable in particular chemical spaces. In addition to LogD assays, ability predict...