17,534 research outputs found
Computational structureābased drug design: Predicting target flexibility
The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in threeādimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant
from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft
Adaptive learning program for developing employability skills
The paper aims to demonstrate the benefits of adaptive learning technologies as a viable alternative to time consuming tutor led individual support. It proposes to reveal how adaptive learning interventions can be effective in enriching student learning while targeting precise areas of development. This review will compile evidence on the nature and extent of Adaptive Learning tools used to develop employability skills among Higher Education institutions. This will be specifically for students undergoing studies at the graduate level. Given the short time available, a scoping study framework will be used to examine the scope of carrying out a full systematic review or identifying gaps in existing literature (Arksey and OāMalley, 2005). This design follows the general principles of a systematic review by following preāspecified methods to reduce the risk of bias by selecting favourable studies, and extracting and analysing data that backs a particular hypothesis. That is, the methods are determined a priori, and are transparent and replicable
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks
In de novo drug design, computational strategies are used to generate novel
molecules with good affinity to the desired biological target. In this work, we
show that recurrent neural networks can be trained as generative models for
molecular structures, similar to statistical language models in natural
language processing. We demonstrate that the properties of the generated
molecules correlate very well with the properties of the molecules used to
train the model. In order to enrich libraries with molecules active towards a
given biological target, we propose to fine-tune the model with small sets of
molecules, which are known to be active against that target.
Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test
molecules that medicinal chemists designed, whereas against Plasmodium
falciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled
with a scoring function, our model can perform the complete de novo drug design
cycle to generate large sets of novel molecules for drug discovery.Comment: 17 pages, 17 figure
Kern-basierte Lernverfahren fĆ¼r das virtuelle Screening
We investigate the utility of modern kernel-based machine learning methods for ligand-based virtual screening. In particular, we introduce a new graph kernel based on iterative graph similarity and optimal assignments, apply kernel principle component analysis to projection error-based novelty detection, and discover a new selective agonist of the peroxisome proliferator-activated receptor gamma using Gaussian process regression. Virtual screening, the computational ranking of compounds with respect to a predicted property, is a cheminformatics problem relevant to the hit generation phase of drug development. Its ligand-based variant relies on the similarity principle, which states that (structurally) similar compounds tend to have similar properties. We describe the kernel-based machine learning approach to ligand-based virtual screening; in this, we stress the role of molecular representations, including the (dis)similarity measures defined on them, investigate effects in high-dimensional chemical descriptor spaces and their consequences for similarity-based approaches, review literature recommendations on retrospective virtual screening, and present an example workflow. Graph kernels are formal similarity measures that are defined directly on graphs, such as the annotated molecular structure graph, and correspond to inner products. We review graph kernels, in particular those based on random walks, subgraphs, and optimal vertex assignments. Combining the latter with an iterative graph similarity scheme, we develop the iterative similarity optimal assignment graph kernel, give an iterative algorithm for its computation, prove convergence of the algorithm and the uniqueness of the solution, and provide an upper bound on the number of iterations necessary to achieve a desired precision. In a retrospective virtual screening study, our kernel consistently improved performance over chemical descriptors as well as other optimal assignment graph kernels. Chemical data sets often lie on manifolds of lower dimensionality than the embedding chemical descriptor space. Dimensionality reduction methods try to identify these manifolds, effectively providing descriptive models of the data. For spectral methods based on kernel principle component analysis, the projection error is a quantitative measure of how well new samples are described by such models. This can be used for the identification of compounds structurally dissimilar to the training samples, leading to projection error-based novelty detection for virtual screening using only positive samples. We provide proof of principle by using principle component analysis to learn the concept of fatty acids. The peroxisome proliferator-activated receptor (PPAR) is a nuclear transcription factor that regulates lipid and glucose metabolism, playing a crucial role in the development of type 2 diabetes and dyslipidemia. We establish a Gaussian process regression model for PPAR gamma agonists using a combination of chemical descriptors and the iterative similarity optimal assignment kernel via multiple kernel learning. Screening of a vendor library and subsequent testing of 15 selected compounds in a cell-based transactivation assay resulted in 4 active compounds. One compound, a natural product with cyclobutane scaffold, is a full selective PPAR gamma agonist (EC50 = 10 +/- 0.2 muM, inactive on PPAR alpha and PPAR beta/delta at 10 muM). The study delivered a novel PPAR gamma agonist, de-orphanized a natural bioactive product, and, hints at the natural product origins of pharmacophore patterns in synthetic ligands.Wir untersuchen moderne Kern-basierte maschinelle Lernverfahren fĆ¼r das Liganden-basierte virtuelle Screening. Insbesondere entwickeln wir einen neuen Graphkern auf Basis iterativer GraphƤhnlichkeit und optimaler Knotenzuordnungen, setzen die Kernhauptkomponentenanalyse fĆ¼r Projektionsfehler-basiertes Novelty Detection ein, und beschreiben die Entdeckung eines neuen selektiven Agonisten des Peroxisom-Proliferator-aktivierten Rezeptors gamma mit Hilfe von GauĆ-Prozess-Regression. Virtuelles Screening ist die rechnergestĆ¼tzte Priorisierung von MolekĆ¼len bezĆ¼glich einer vorhergesagten Eigenschaft. Es handelt sich um ein Problem der Chemieinformatik, das in der Trefferfindungsphase der Medikamentenentwicklung auftritt. Seine Liganden-basierte Variante beruht auf dem Ćhnlichkeitsprinzip, nach dem (strukturell) Ƥhnliche MolekĆ¼le tendenziell Ƥhnliche Eigenschaften haben. In unserer Beschreibung des Lƶsungsansatzes mit Kern-basierten Lernverfahren betonen wir die Bedeutung molekularer ReprƤsentationen, einschlieĆlich der auf ihnen definierten (Un)ƤhnlichkeitsmaĆe. Wir untersuchen Effekte in hochdimensionalen chemischen DeskriptorrƤumen, ihre Auswirkungen auf Ćhnlichkeits-basierte Verfahren und geben einen LiteraturĆ¼berblick zu Empfehlungen zur retrospektiven Validierung, einschlieĆlich eines Beispiel-Workflows. Graphkerne sind formale ĆhnlichkeitsmaĆe, die inneren Produkten entsprechen und direkt auf Graphen, z.B. annotierten molekularen Strukturgraphen, definiert werden. Wir geben einen LiteraturĆ¼berblick Ć¼ber Graphkerne, insbesondere solche, die auf zufƤlligen Irrfahrten, Subgraphen und optimalen Knotenzuordnungen beruhen. Indem wir letztere mit einem Ansatz zur iterativen GraphƤhnlichkeit kombinieren, entwickeln wir den iterative similarity optimal assignment Graphkern. Wir beschreiben einen iterativen Algorithmus, zeigen dessen Konvergenz sowie die Eindeutigkeit der Lƶsung, und geben eine obere Schranke fĆ¼r die Anzahl der benƶtigten Iterationen an. In einer retrospektiven Studie zeigte unser Graphkern konsistent bessere Ergebnisse als chemische Deskriptoren und andere, auf optimalen Knotenzuordnungen basierende Graphkerne. Chemische DatensƤtze liegen oft auf Mannigfaltigkeiten niedrigerer DimensionalitƤt als der umgebende chemische Deskriptorraum. Dimensionsreduktionsmethoden erlauben die Identifikation dieser Mannigfaltigkeiten und stellen dadurch deskriptive Modelle der Daten zur VerfĆ¼gung. FĆ¼r spektrale Methoden auf Basis der Kern-Hauptkomponentenanalyse ist der Projektionsfehler ein quantitatives MaĆ dafĆ¼r, wie gut neue Daten von solchen Modellen beschrieben werden. Dies kann zur Identifikation von MolekĆ¼len verwendet werden, die strukturell unƤhnlich zu den Trainingsdaten sind, und erlaubt so Projektionsfehler-basiertes Novelty Detection fĆ¼r virtuelles Screening mit ausschlieĆlich positiven Beispielen. Wir fĆ¼hren eine Machbarkeitsstudie zur Lernbarkeit des Konzepts von FettsƤuren durch die Hauptkomponentenanalyse durch. Der Peroxisom-Proliferator-aktivierte Rezeptor (PPAR) ist ein im Zellkern vorkommender Rezeptor, der den Fett- und Zuckerstoffwechsel reguliert. Er spielt eine wichtige Rolle in der Entwicklung von Krankheiten wie Typ-2-Diabetes und DyslipidƤmie. Wir etablieren ein GauĆ-Prozess-Regressionsmodell fĆ¼r PPAR gamma-Agonisten mit chemischen Deskriptoren und unserem Graphkern durch gleichzeitiges Lernen mehrerer Kerne. Das Screening einer kommerziellen Substanzbibliothek und die anschlieĆende Testung 15 ausgewƤhlter Substanzen in einem Zell-basierten Transaktivierungsassay ergab vier aktive Substanzen. Eine davon, ein Naturstoff mit Cyclobutan-GrundgerĆ¼st, ist ein voller selektiver PPAR gamma-Agonist (EC50 = 10 +/- 0,2 muM, inaktiv auf PPAR alpha und PPAR beta/delta bei 10 muM). Unsere Studie liefert einen neuen PPAR gamma-Agonisten, legt den Wirkmechanismus eines bioaktiven Naturstoffs offen, und erlaubt RĆ¼ckschlĆ¼sse auf die NaturstoffursprĆ¼nge von Pharmakophormustern in synthetischen Liganden
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