Biomedical subjects

David J Livingstone

Publications and source records attributed to David J Livingstone.

6 recordsLinked to original sources

PubMed2005

Judging the significance of multiple linear regression models.

It is common practice to calculate large numbers of molecular descriptors, apply variable selection procedures to reduce the numbers, and then construct multiple linear regression (MLR) models with biological activity. The significance of these models is judged using the usual statistical tests. Unfortunately, these tests are not appropriate under these circumstances since the MLR models suffer from "selection bias". Experiments with regression using random numbers have generated critical values (Fmax) with which to assess significance.

Antimycin A↗

PubMed2003

Theoretical property predictions.

Methods for the prediction of octanol/water partition coefficient, aqueous solubility and acid/base dissociation constants are described and discussed. The advantages and limitations of the different approaches are described and an indication of problem areas discussed. Available prediction software is described and listed and attempts are made to assess the likely reliability of the predictions. The concept of "drug-likeness" is introduced and put into context and models for the prediction of ADME properties and toxicity are briefly described and assessed. Software for ADME/toxicity prediction is listed and the impact of these techniques on current drug design efforts is described. Web references are given for both commercial and public domain software which is available for property prediction from chemical structure.

Algorithms↗

PubMed2002

Substituent effects on edge-to-face aromatic interactions.

Chemical double mutant cycles have been used to measure the magnitude of edge-to-face aromatic interactions in hydrogen-bonded zipper complexes as a function of substituents on both aromatic rings. The interaction energies vary depending on the combination of substituents from +1.0 kJ mol-1 (repulsive), to -4.9 kJ mol-1 (attractive). The results correlate with the Hammett substituent constants which indicates that electrostatic interactions are responsible for the observed differences in interaction energy. The experiments can be rationalised based on local electrostatic interactions between the protons on the edge ring and the pi-electron density on the face ring as well as global electrostatic interactions between the overall dipoles on the two aromatic groups.

Amides↗

PubMedDate not supplied

Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks.

A series of neural networks has been trained, using consensus methods, to recognize compounds that act at biological targets belonging to specific gene families. The MDDR database was used to provide compounds targeted against gene families and sets of randomly selected molecules. BCUT parameters were employed as input descriptors that encode structural properties and information relevant to ligand-receptor interactions. In each case, the networks identified over 80% of the compounds targeting a gene family. The technique was applied to purchasing compounds from external suppliers, and results from screening against one gene family demonstrated impressive abilities to predict the activity of the majority of known hit compounds.

Databases, Factual↗

PubMedDate not supplied

A consensus neural network-based technique for discriminating soluble and poorly soluble compounds.

BCUT [Burden, CAS, and University of Texas] descriptors, defined as eigenvalues of modified connectivity matrices, have traditionally been applied to drug design tasks such as defining receptor relevant subspaces to assist in compound selections. In this paper we present studies of consensus neural networks trained on BCUTs to discriminate compounds with poor aqueous solubility from those with reasonable solubility. This level was set at 0.1 mg/mL on advice from drug formulation and drug discovery scientists. By applying strict criteria to the insolubility predictions, approximately 95% of compounds are classified correctly. For compounds whose predictions have a lower level of confidence, further parameters are examined in order to flag those considered to possess unsuitable biopharmaceutical and physicochemical properties. This approach is not designed to be applied in isolation but is intended to be used as a filter in the selection of screening candidates, compound purchases, and the application of synthetic priorities to combinatorial libraries.

Journal Article↗

PubMedDate not supplied

Application of QSPR to mixtures.

In this paper we report an attempt to apply the QSPR approach for the analysis of data for mixtures. This is an extension of the conventional QSPR approach to the analysis of data for single molecules. The QSPR methodology was applied to a data set of experimental measured density of binary liquid mixtures compiled from the literature. The present study is aimed to develop models to predict the "delta" value of a mixture i.e., deviation of the experimental mixture density (MED) from the ideal, mole-weighted calculated mixture density (MCD). The QSPR was investigated in two perspectives (QMD-I and QMD-II) with respect to the creation of training and test sets. The study resulted in significant ensemble neural network and k-nearest neighbor models having statistical parameters r2, q2(10cv) greater than 0.9, and pred_r2 greater than 0.75. The developed models can be used to predict the delta and hence the density of a new mixture. The QSPR analysis shows the importance of hydrogen bond, polar, shape, and thermodynamic descriptors in determining mixture density, thus aiding in the understanding of molecular interactions important in molecular packing in the mixtures.

Ligands↗