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Biomedical subjects

Robert C Glen

Publications and source records attributed to Robert C Glen.

15 recordsLinked to original sources
PubMed2005

Screening for dihydrofolate reductase inhibitors using MOLPRINT 2D, a fast fragment-based method employing the naïve Bayesian classifier: limitations of the descriptor and the importance of balanced chemistry in training and test sets.

A fragment-based similarity searching method, MOLPRINT 2D, was employed for virtual screening of Escherichia coli dihydrofolate reductase inhibitors. Using the original training set of 50,000 compounds, only marginal enrichment factors (between 1 and 3) could be achieved on the test library. The active structures contained in the training and test libraries represented different types of "chemistry", that is, different substructural features associated with activity. Training and test sets were pooled in a 2nd step and randomly split into training and test of equal size, with the objective of smoothing out the different chemical characteristics of both libraries. In a 10-fold cross-validation study on the new training and test sets, typically 10-fold enrichment could be found in the first 96 positions, 4-fold enrichment in the first 384 positions, and 3-fold enrichment in the first 1536 positions, corresponding to 6, 10, and 28 hits, respectively (out of a total of 307; activity defined as average residual activity of less than 80%). The conclusions are 2-fold. On one hand, the exact fragment-matching similarity searching method employed here is not capable of finding completely novel hit structures. On the other hand, this study emphasizes the requirement for a comparable distribution of chemical features of the training and test sets. MOLPRINT 2D is freely downloadable from http://www.cheminformatics.org.

Bayes Theorem↗
PubMed2004

Molecular surface point environments for virtual screening and the elucidation of binding patterns (MOLPRINT 3D).

A novel method (MOLPRINT 3D) for virtual screening and the elucidation of ligand-receptor binding patterns is introduced that is based on environments of molecular surface points. The descriptor uses points relative to the molecular coordinates, thus it is translationally and rotationally invariant. Due to its local nature, conformational variations cause only minor changes in the descriptor. If surface point environments are combined with the Tanimoto coefficient and applied to virtual screening, they achieve retrieval rates comparable to that of two-dimensional (2D) fingerprints. The identification of active structures with minimal 2D similarity ("scaffold hopping") is facilitated. In combination with information-gain-based feature selection and a naive Bayesian classifier, information from multiple molecules can be combined and classification performance can be improved. Selected features are consistent with experimentally determined binding patterns. Examples are given for angiotensin-converting enzyme inhibitors, 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors, and thromboxane A2 antagonists.

Angiotensin-Converting Enzyme Inhibitors↗
PubMed2004

Molecular similarity: a key technique in molecular informatics.

Molecular Informatics utilises many ideas and concepts to find relationships between molecules. The concept of similarity, where molecules may be grouped according to their biological effects or physicochemical properties has found extensive use in drug discovery. Some areas of particular interest have been in lead discovery and compound optimisation. For example, in designing libraries of compounds for lead generation, one approach is to design sets of compounds "similar" to known active compounds in the hope that alternative molecular structures are found that maintain the properties required while enhancing e.g. patentability, medicinal chemistry opportunities or even in achieving optimised pharmacokinetic profiles. Thus the practical importance of the concept of molecular similarity has grown dramatically in recent years. The predominant users are pharmaceutical companies, employing similarity methods in a wide range of applications e.g. virtual screening, estimation of absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) and prediction of physicochemical properties (solubility, partitioning etc.). In this perspective, we discuss the representation of molecular structure (descriptors), methods of comparing structures and how these relate to measured properties. This leads to the concept of molecular similarity, its various definitions and uses and how these have evolved in recent years. Here, we wish to evaluate and in some cases challenge accepted views and uses of molecular similarity. Molecular similarity, as a paradigm, contains many implicit and explicit assumptions in particular with respect to the prediction of the binding and efficacy of molecules at biological receptors. The fundamental observation is that molecular similarity has a context which both defines and limits its use. The key issues of solvation effects, heterogeneity of binding sites and the fundamental problem of the form of similarity measure to use are addressed.

Algorithms↗
PubMed2004

Predicting protein-ligand binding affinities: a low scoring game?

We have investigated the performance of five well known scoring functions in predicting the binding affinities of a diverse set of 205 protein-ligand complexes with known experimental binding constants, and also on subsets of mutually similar complexes. We have found that the overall performance of the scoring functions on the diverse set is disappointing, with none of the functions achieving r(2) values above 0.32 on the whole dataset. Performance on the subsets was mixed, with four of the five functions predicting fairly well the binding affinities of 35 proteinases, but none of the functions producing any useful correlation on a set of 38 aspartic proteinases. We consider two algorithms for producing consensus scoring functions, one based on a linear combination of scores from the five individual functions and the other on averaging the rankings produced by the five functions. We find that both algorithms produce consensus functions that generally perform slightly better than the best individual scoring function on a given dataset.

Algorithms↗
PubMed2004

Water network dynamics at the critical moment of a peptide's beta-turn formation: a molecular dynamics study.

All-atom molecular dynamics simulations for a single molecule of Leu-Enkephalin in aqueous solution have been used to study the role of the water network during the formation of beta-turns. We give a detailed account of the intramolecular hydrogen bonding, the water-peptide hydrogen bonding, and the orientation and residence times of water molecules focusing on the short critical periods of transition to the stable beta-turns. These studies suggest that, when intramolecular hydrogen bonding between the first and fourth residue of the beta-turn is not present, the disruption of the water network and the establishment of water bridges constitute decisive factors in the formation and stability of the beta-turn. Finally, we provide possible explanations and mechanisms for the formations of different kinds of beta-turns.

Computer Simulation↗
PubMed2004

Synthesis and serotonergic activity of variously substituted (3-amido)phenylpiperazine derivatives and benzothiophene-4-piperazine derivatives: novel antagonists for the vascular 5-HT1B receptor.

The synthesis and vascular 5-HT(1B) receptor activity of a novel series of substituted 3-amido phenylpiperazine and 4-(4-methyl-1-piperazinyl)-1-benzo[b]thiophene derivatives is described. Modifications to the amido linked sidechains of the 3-amidophenyl-piperazine derivatives and to the 2-sidechain of the 1-benzo[b]thiophene derivatives have been explored. Several compounds were identified which exhibited affinity at the vascular 5-HT(1B) receptor of pK(B) > 7.0. From the 3-amidophenyl-piperazine series, N-(4-(4-chlorophenyl)thiazol-2-yl-3-(4-methyl-1-piperazinyl)benzamide (30) and from the benzo[b]thiophene-4-piperazine series N-(2-ethylphenyl)-4-(4-methyl-1- piperazinyl)-1-benzo[b]thiophene-2-carboxamide (38) were identified as a highly potent, silent (as judged by the inability of angiotensin II to unmask 5-HT(1B) receptor mediated agonist activity in the rabbit femoral artery) and competitive vascular 5-HT(1B) receptor antagonist. The affinity of compounds from these two series of compounds for the vascular 5-HT(1B) receptor is discussed as well as a proposed mode of binding to the receptor pharmacophore.

Amides↗
PubMedDate not supplied

Novel methods for the prediction of logP, pK(a), and logD.

Novel methods for predicting logP, pK(a), and logD values have been developed using data sets (592 molecules for logP and 1029 for pK(a)) containing a wide range of molecular structures. An equation with three molecular properties (polarizability and partial atomic charges on nitrogen and oxygen) correlates highly with logP (r2 = 0.89). The pK(a)s are estimated for both acids and bases using a novel tree structured fingerprint describing the ionizing centers. The new models have been compared with existing models and also experimental measurements on test sets of common organic compounds and pharmaceutical molecules.

Algorithms↗
PubMedDate not supplied

Predicting pK(a) by molecular tree structured fingerprints and PLS.

This is the second phase of the pK(a) predictor published earlier (J. Chem. Inf. Comput. Sci. 2002, 42, 796-805). The algorithm has been extended by treating specific chemical classes separately and generating tree-structured molecular descriptors tailored to each individual class. A training set consisting of 625 acids and 412 bases covers the major areas of chemical space involved in protonation and deprotonation. The models obtained demonstrate excellent statistics (SE = 0.41 for acids and 0.30 for bases) and yielded accurate predictions on an external test set. The quality and statistical performance of pK(a) prediction has been improved considerably over the initial implementation of the method.

Algorithms↗
PubMedDate not supplied

Molecular similarity searching using atom environments, information-based feature selection, and a naïve Bayesian classifier.

A novel technique for similarity searching is introduced. Molecules are represented by atom environments, which are fed into an information-gain-based feature selection. A naïve Bayesian classifier is then employed for compound classification. The new method is tested by its ability to retrieve five sets of active molecules seeded in the MDL Drug Data Report (MDDR). In comparison experiments, the algorithm outperforms all current retrieval methods assessed here using two- and three-dimensional descriptors and offers insight into the significance of structural components for binding.

Journal Article↗
PubMedDate not supplied

Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance.

A molecular similarity searching technique based on atom environments, information-gain-based feature selection, and the naive Bayesian classifier has been applied to a series of diverse datasets and its performance compared to those of alternative searching methods. Atom environments are count vectors of heavy atoms present at a topological distance from each heavy atom of a molecular structure. In this application, using a recently published dataset of more than 100000 molecules from the MDL Drug Data Report database, the atom environment approach appears to outperform fusion of ranking scores as well as binary kernel discrimination, which are both used in combination with Unity fingerprints. Overall retrieval rates among the top 5% of the sorted library are nearly 10% better (more than 14% better in relative numbers) than those of the second best method, Unity fingerprints and binary kernel discrimination. In 10 out of 11 sets of active compounds the combination of atom environments and the naive Bayesian classifier appears to be the superior method, while in the remaining dataset, data fusion and binary kernel discrimination in combination with Unity fingerprints is the method of choice. Binary kernel discrimination in combination with Unity fingerprints generally comes second in performance overall. The difference in performance can largely be attributed to the different molecular descriptors used. Atom environments outperform Unity fingerprints by a large margin if the combination of these descriptors with the Tanimoto coefficient is compared. The naive Bayesian classifier in combination with information-gain-based feature selection and selection of a sensible number of features performs about as well as binary kernel discrimination in experiments where these classification methods are compared. When used on a monoaminooxidase dataset, atom environments and the naive Bayesian classifier perform as well as binary kernel discrimination in the case of a 50/50 split of training and test compounds. In the case of sparse training data, binary kernel discrimination is found to be superior on this particular dataset. On a third dataset, the atom environment descriptor shows higher retrieval rates than other 2D fingerprints tested here when used in combination with the Tanimoto similarity coefficient. Feature selection is shown to be a crucial step in determining the performance of the algorithm. The representation of molecules by atom environments is found to be more effective than Unity fingerprints for the type of biological receptor similarity calculations examined here. Combining information prior to scoring and including information about inactive compounds, as in the Bayesian classifier and binary kernel discrimination, is found to be superior to posterior data fusion (in the datasets tested here).

Journal Article↗
PubMedDate not supplied

General melting point prediction based on a diverse compound data set and artificial neural networks.

We report the development of a robust and general model for the prediction of melting points. It is based on a diverse data set of 4173 compounds and employs a large number of 2D and 3D descriptors to capture molecular physicochemical and other graph-based properties. Dimensionality reduction is performed by principal component analysis, while a fully connected feed-forward back-propagation artificial neural network is employed for model generation. The melting point is a fundamental physicochemical property of a molecule that is controlled by both single-molecule properties and intermolecular interactions due to packing in the solid state. Thus, it is difficult to predict, and previously only melting point models for clearly defined and smaller compound sets have been developed. Here we derive the first general model that covers a comparatively large and relevant part of organic chemical space. The final model is based on 2D descriptors, which are found to contain more relevant information than the 3D descriptors calculated. Internal random validation of the model achieves a correlation coefficient of R(2) = 0.661 with an average absolute error of 37.6 degrees C. The model is internally consistent with a correlation coefficient of the test set of Q(2) = 0.658 (average absolute error 38.2 degrees C) and a correlation coefficient of the internal validation set of Q(2) = 0.645 (average absolute error 39.8 degrees C). Additional validation was performed on an external drug data set consisting of 277 compounds. On this external data set a correlation coefficient of Q(2) = 0.662 (average absolute error 32.6 degrees C) was achieved, showing ability of the model to generalize. Compared to an earlier model for the prediction of melting points of druglike compounds our model exhibits slightly improved performance, despite the much larger chemical space covered. The remaining model error is due to molecular properties that are not captured using single-molecule based descriptors, namely both inter- and intramolecular interactions and crystal packing, for which examples of and reasons for outliers are given.

Journal Article↗
PubMedDate not supplied

A discussion of measures of enrichment in virtual screening: comparing the information content of descriptors with increasing levels of sophistication.

We have performed virtual screening using some very simple features, by employing the number of atoms per element as molecular descriptors but without regard to any structural information whatsoever. Surprisingly, these atom counts are able to outperform virtual-affinity-based fingerprints and Unity fingerprints in some activity classes. Although molecular weight and other biases were known in target-based virtual screening settings (docking), we report the effect of using very simple descriptors for ligand-based virtual screening, by using clearly defined biological targets and employing a large data set (>100,000 compounds) containing multiple (11) activity classes. Structure-unaware atom count vectors as descriptors in combination with the Euclidean distance measure are able to achieve "enrichment factors" over random selection of around 4 (depending on the particular class of active compounds), putting the enrichment factors reported for more sophisticated virtual screening methods in a different light. They are also able to retrieve active compounds with novel scaffolds instead of merely the expected structural analogues. The added value of many currently used virtual screening methods (calculated as enrichment factors) drops down to a factor of between 1 and 2, instead of often reported double-digit figures. The observed effect is much less profound for simple descriptors such as molecular weight and is only present in cases of atypical (larger) ligands. The current state of virtual screening is not as sophisticated as might be expected, which is due to descriptors still not being able to capture structural properties relevant to binding. This fact can partly be explained by highly nonlinear structure-activity relationships, which represent a severe limitation of the "similar property principle" in the context of bioactivity.

Computer Simulation↗
PubMedDate not supplied

Analysis of activity space by fragment fingerprints, 2D descriptors, and multitarget dependent transformation of 2D descriptors.

The effect of multitarget dependent descriptor transformation on classification performance is explored in this work. To this end decision trees as well as neural net QSAR in combination with PLS were applied to predict the activity class of 5HT3 ligands, angiotensin converting enzyme inhibitors, 3-hydroxyl-3-methyl glutaryl coenzyme A reductase inhibitors, platelet activating factor antagonists, and thromboxane A2 antagonists. Physicochemical descriptors calculated by MOE and fragment-based descriptors (MOLPRINT 2D) were employed to generate descriptor vectors. In a subsequent step the physicochemical descriptor vectors were transformed to a lower dimensional space using multitarget dependent descriptor transformation. Cross-validation of the original physicochemical descriptors in combination with decision trees and neural net QSAR as well as cross-validation of PLS multitarget transformed descriptors with neural net QSAR were performed. For comparison this was repeated using fragment-based descriptors in combination with decision trees.

Angiotensin-Converting Enzyme Inhibitors↗
PubMedDate not supplied

Characterizing bitterness: identification of key structural features and development of a classification model.

This work describes the first approach in the development of a comprehensive classification method for bitterness of small molecules. The data set comprises 649 bitter and 13 530 randomly selected molecules from the MDL Drug Data Repository (MDDR) which are analyzed by circular fingerprints (MOLPRINT 2D) and information-gain feature selection. The feature selection proposes substructural features which are statistically correlated to bitterness. Classification is performed on the selected features via a naïve Bayes classifier. The substructural features upon which the classification is based are able to discriminate between bitter and random compounds, and thus we propose they are also functionally responsible for causing the bitter taste. Such substructures include various sugar moieties as well as highly branched carbon scaffolds. Cynaropicrine contains a number of the substructural features found to be statistically associated with bitterness and thus was correctly predicted to be bitter by our model. Alternatively, both promethazine and saccharin contain fewer of these substructural features, and thus the bitterness in these compounds was not identified. Two different classes of bitter compounds were identified, namely those which are larger and contain mainly oxygen and carbon and often sugar moieties, and those which are rather smaller and contain additional nitrogen and/or sulfur fragments. The classifier is able to predict 72.1% of the bitter compounds. Feature selection reduces the number of false-positives while also increasing the number of false negatives to 69.5% of bitter compounds correctly predicted. Overall, the method presented here presents both one of the largest databases of bitter compounds presently available as well as a relatively reliable classification method.

Algorithms↗