The Journal of Computer-Aided Molecular Design: a bibliometric note

Summarizes the articles in, and the citations to, volumes 2-24 of the Journal of Computer-Aided Molecular Design. The citations to the journal come from almost 2000 different sources that span a very wide range of academic subjects, with the most heavily cited articles being descriptions of software systems and of computational methods

Application of Support Vector Machines in Virtual Screening

Traditionally drug discovery has been a labor intensive effort, since it is difficult to identify a possible drug candidate from an extremely large small molecule library for any given target. Most of the small molecules fail to show any activity against the target because of electrochemical, structural and other incompatibilities. Virtual screening is an in-silico approach to identify drug candidates which are unlikely to show any activity against a given target, thus reducing an enormous amount of experimentation which is most likely to end up as failures. Important approaches in virtual screening have been through docking studies and using classification techniques. Support vector machines based classifiers, based on the principles of statistical learning theory have found several applications in virtual screening. In this paper, first the theory and main principles of SVM are briefly outlined. Thereafter a few successful applications of SVM in virtual screening have been discussed. It further underlines the pitfalls of the existing approaches and highlights the area which needs further contribution to improve the state of the art for application of SVM in virtual screening

An Innovative Strategy for Dual Inhibitor Design and Its Application in Dual Inhibition of Human Thymidylate Synthase and Dihydrofolate Reductase Enzymes

Due to the diligence of inherent redundancy and robustness in many biological networks and pathways, multitarget inhibitors present a new prospect in the pharmaceutical industry for treatment of complex diseases. Nevertheless, to design multitarget inhibitors is concurrently a great challenge for medicinal chemists. We have developed a novel computational approach by integrating the affinity predictions from structure-based virtual screening with dual ligand-based pharmacophore to discover potential dual inhibitors of human Thymidylate synthase (hTS) and human dihydrofolate reductase (hDHFR). These are the key enzymes in folate metabolic pathway that is necessary for the biosynthesis of RNA,DNA, and protein. Their inhibition has found clinical utility as antitumor, antimicrobial, and antiprotozoal agents. A druglike database was utilized to perform dual-target docking studies. Hits identified through docking experiments were mapped over a dual pharmacophore which was developed from experimentally known dual inhibitors of hTS and hDHFR. Pharmacophore mapping procedure helped us in eliminating the compounds which do not possess basic chemical features necessary for dual inhibition. Finally, three structurally diverse hit compounds that showed key interactions at both activesites, mapped well upon the dual pharmacophore, and exhibited lowest binding energies were regarded as possible dual inhibitors of hTS and hDHFR. Furthermore, optimization studies were performed for final dual hit compound and eight optimized dual hits demonstrating excellent binding features at target systems were also regarded as possible dual inhibitors of hTS and hDHFR. In general, the strategy used in the current study could be a promising computational approach and may be generally applicable to other dual target drug designs

Study of ligand-based virtual screening tools in computer-aided drug design

Virtual screening is a central technique in drug discovery today. Millions of molecules can be tested in silico with the aim to only select the most promising and test them experimentally. The topic of this thesis is ligand-based virtual screening tools which take existing active molecules as starting point for finding new drug candidates. One goal of this thesis was to build a model that gives the probability that two molecules are biologically similar as function of one or more chemical similarity scores. Another important goal was to evaluate how well different ligand-based virtual screening tools are able to distinguish active molecules from inactives. One more criterion set for the virtual screening tools was their applicability in scaffold-hopping, i.e. finding new active chemotypes. In the first part of the work, a link was defined between the abstract chemical similarity score given by a screening tool and the probability that the two molecules are biologically similar. These results help to decide objectively which virtual screening hits to test experimentally. The work also resulted in a new type of data fusion method when using two or more tools. In the second part, five ligand-based virtual screening tools were evaluated and their performance was found to be generally poor. Three reasons for this were proposed: false negatives in the benchmark sets, active molecules that do not share the binding mode, and activity cliffs. In the third part of the study, a novel visualization and quantification method is presented for evaluation of the scaffold-hopping ability of virtual screening tools.Siirretty Doriast

BioSuite: a comprehensive bioinformatics software package (A unique industry-academia collaboration)

This article does not have an abstract

Tailoring Toll-like Receptor 8 Ligands for Balancing Immune Response and Inflammation

Toll-like receptors (TLRs) play a central role in innate immunity by recognising invading pathogens and host-derived danger signals and initiating the inflammatory response. Aberrant TLR response is involved in the pathogenesis of cancers, infections, autoimmune disorders and allergic diseases. Therefore, TLRs represent attractive targets for novel therapeutic agents. The PhD project's main research aim is to discover novel small molecule modulators of Toll-like receptor 8 (TLR8) and understand their mechanisms of action using computational approaches. TLR8 crystal structure is solved, and several modulators are known from previous drug screens. Therefore, TLR8 is a promising target for rational computer-aided development of novel drug candidates. In the initial phase of the project, the main goal was to study relevant structural features in available crystal structures of TLR8. The focus was on the dimerisation interface because of its role in the binding of ligands and subsequent activation of the receptor. Additionally, we studied the conservation of the relevant structural features across the closely related TLRs. The second part shifts the focus to the binding of the small molecules to TLR8. We investigated interactions between the known ligands and TLR8 and used it to develop the most plausible 3D pharmacophore model. Subsequently, we employed the developed 3D pharmacophore model in virtual screening to identify novel modulators of TLR8. We identified a pyrimidine-based compound that inhibits TLR8-mediated signalling in the micromolar concentration range. The potent anti-inflammatory and dose-dependent response has been confirmed in a series of derivatives of this initial virtual hit, which allowed for a detailed elucidation of structure-activity relationships (SAR) and more precise description of the binding mode. Conclusively, we have developed a novel and promising pyrimidine-based TLR8 inhibitors in silico and confirmed their biological activity, selectivity and low cytotoxicity in vitro. Results from the study on TLR8 represent a solid basis for the future design of small molecule TLR modulators as novel therapeutic agents for modulating immune response and inflammation.Toll-like Rezeptoren (TLRs) spielen eine zentrale Rolle in angeborenen Immunsystem, indem sie eindringende Pathogene sowie endogene Gefahrensignale erkennen und Entzündungsreaktionen einleiten. TLRs sind an der Pathogenese von Krebserkrankungen, Infektionen, Autoimmunerkrankungen und allergischen Erkrankungen beteiligt. Aus diesem Grund stellen TLRs attraktive Ziele für neue, niedermolekulare Wirkstoffe dar. Das Hauptziel dieses Promotionsprojekts ist die Entdeckung neuer niedermolekularer Modulatoren des Toll-like-Rezeptors 8 (TLR8) und das Verständnis ihrer Wirkmechanismen mit Hilfe computergestützter Ansätze. Die Kristallstruktur von TLR8 ist verfügbar und mehrere Modulatoren sind aus früheren Wirkstoffscreens bekannt. Daher ist TLR8 ein vielversprechendes Ziel für die rationale computergestützte Entwicklung neuer Wirkstoffkandidaten. Am Beginn des Projekts bestand das Hauptziel darin, relevante strukturelle Merkmale in den verfügbaren Kristallstrukturen von TLR8 zu untersuchen. Der Fokus lag dabei auf dem Dimerisierungsbereich, da dieser eine wichtige Rolle bei der Bindung von Liganden und der anschließenden Aktivierung des Rezeptors spielt. Zusätzlich untersuchten wir die Konservierung der relevanten Strukturmerkmale über die eng verwandten TLRs hinweg. Der zweite Teil verlagert den Fokus auf die Bindung kleiner Moleküle an TLR8. Wir untersuchten die Interaktionen zwischen den bekannten Liganden und TLR8 und entwickelten daraus systemtisch ein 3D-Pharmakophormodell. Anschließend setzten wir das entwickelte 3D-Pharmakophormodell im virtuellen Screening ein, um neuartige Modulatoren des TLR8 zu identifizieren. Wir identifizierten ein Pyrimidin-Analogon, das die TLR8- vermittelte Signalweiterleitung im mikromolaren Konzentrationsbereich hemmt. Die potente entzündungshemmende und dosisabhängige Wirkung wurde in einer kleinen Serie von Analoga bestätigt. Schließlich optimierten wir die identifizierten Pyrimidinverbindungen weiter, was eine detailliertere Struktur-Aktivitäts-Analyse und eine genauere Aufklärung des Bindungsmodus ermöglichte. Zusammenfassend haben wir neuartige und vielversprechende TLR8-Inhibitoren auf Pyrimidinbasis in silico entwickelt und ihre in vitro biologische Aktivität, Selektivität und geringe Zytotoxizität bestätigt. Die Ergebnisse der Studie zu TLR8 helfen uns, die Prozesse zu verstehen, die für ein erfolgreiches Wirkstoffdesign auch bei anderen TLR notwendig sind und stellen eine gute Ausgangsbasis dar, um in Zukunft optimierte, niedermolekulare TLR- Modulatoren zu entwickeln und damit Entzündung und die Immunreaktion effizient zu modulieren

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding