Privileged substructures are of potentially great importance in medicinal chemistry. These scaffolds are characterized by their ability to promiscuously bind to a multitude of receptors through a variety of favorable characteristics. This may include presentation of their substituents in a spatially defined manner and perhaps also the ability to directly bind to the receptor itself, as well as exhibiting promising characteristics to aid bioavailability of the overall molecule. It is believed that some privileged substructures achieve this through the mimicry of common protein surface elements that are responsible for binding, such as β- and gamma;-turns. As a result, these structures represent a promising means by which new lead compounds may be identified.

The combinatorial synthesis of bicyclic privileged structures or privileged substructures

To understand complex biological systems requires the integration of experimental and computational research — in other words a systems biology approach. Computational biology, through pragmatic modelling and theoretical exploration, provides a powerful foundation from which to address critical scientific questions head-on. The reviews in this Insight cover many different aspects of this energetic field, although all, in one way or another, illuminate the functioning of modular circuits, including their robustness, design and manipulation. Computational systems biology addresses questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and engineering.

http://cic.javerianacali.edu.co/wiki/lib/exe/fetch.php?id=grupos:avispa:introductory_material&cache=cache&media=grupos:avispa:resources:kitano-nature.pdf

Computational systems biology

Motivation:In silico prediction of drug–target interactions from heterogeneous biological data is critical in the search for drugs and therapeutic targets for known diseases such as cancers. There is therefore a strong incentive to develop new methods capable of detecting these potential drug–target interactions efficiently.

Results: In this article, we investigate the relationship between the chemical space, the pharmacological space and the topology of drug–target interaction networks, and show that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity. We then develop a new method to predict unknown drug–target interactions from chemical, genomic and pharmacological data on a large scale. The proposed method consists of two steps: (i) prediction of pharmacological effects from chemical structures of given compounds and (ii) inference of unknown drug–target interactions based on the pharmacological effect similarity in the framework of supervised bipartite graph inference. The originality of the proposed method lies in the prediction of potential pharmacological similarity for any drug candidate compounds and in the integration of chemical, genomic and pharmacological data in a unified framework. In the results, we make predictions for four classes of important drug–target interactions involving enzymes, ion channels, GPCRs and nuclear receptors. Our comprehensively predicted drug–target interaction networks enable us to suggest many potential drug–target interactions and to increase research productivity toward genomic drug discovery.

Supplementary information: Datasets and all prediction results are available at http://cbio.ensmp.fr/~yyamanishi/pharmaco/.

Availability: Softwares are available upon request.

Contact: yoshihiro.yamanishi@ensmp.fr

Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework

Motivation: Identifying interactions between drug compounds and target proteins has a great practical importance in the drug discovery process for known diseases. Existing databases contain very few experimentally validated drug–target interactions and formulating successful computational methods for predicting interactions remains challenging.

Results: In this study, we consider four different drug–target interaction networks from humans involving enzymes, ion channels, G-protein-coupled receptors and nuclear receptors. We then propose a novel Bayesian formulation that combines dimensionality reduction, matrix factorization and binary classification for predicting drug–target interaction networks using only chemical similarity between drug compounds and genomic similarity between target proteins. The novelty of our approach comes from the joint Bayesian formulation of projecting drug compounds and target proteins into a unified subspace using the similarities and estimating the interaction network in that subspace. We propose using a variational approximation in order to obtain an efficient inference scheme and give its detailed derivations. Finally, we demonstrate the performance of our proposed method in three different scenarios: (i) exploratory data analysis using low-dimensional projections, (ii) predicting interactions for the out-of-sample drug compounds and (iii) predicting unknown interactions of the given network.

Availability: Software and Supplementary Material are available at http://users.ics.aalto.fi/gonen/kbmf2k.

Contact: mehmet.gonen@aalto.fi

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/predicting-drug-target-interactions-from-chemical-and-2f667tvs8b.pdf

Predicting drug–target interactions from chemical and genomic kernels using Bayesian matrix factorization

Motivation: Predicting interactions between small molecules and proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. When no detailed 3D structure of the protein target is available, ligand-based virtual screening allows the construction of predictive models by learning to discriminate known ligands from non-ligands. However, the accuracy of ligand-based models quickly degrades when the number of known ligands decreases, and in particular the approach is not applicable for orphan receptors with no known ligand.

Results: We propose a systematic method to predict ligand–protein interactions, even for targets with no known 3D structure and few or no known ligands. Following the recent chemogenomics trend, we adopt a cross-target view and attempt to screen the chemical space against whole families of proteins simultaneously. The lack of known ligand for a given target can then be compensated by the availability of known ligands for similar targets. We test this strategy on three important classes of drug targets, namely enzymes, G-protein-coupled receptors (GPCR) and ion channels, and report dramatic improvements in prediction accuracy over classical ligand-based virtual screening, in particular for targets with few or no known ligands.

Availability: All data and algorithms are available as Supplementary Material.

Contact: laurent.jacob@ensmp.fr

Supplementary information:Supplementary data are available at Bioinformatics online.

/pdf/protein-ligand-interaction-prediction-wetryvxoph.pdf

Darko Butina

Papers

Predicting ADME properties in silico: methods and models.