Using RUBI to partition agents in air traffic problems with hard constraints and reward shaping

Air traffic flow management over the U.S. airpsace is a difficult problem. Current management approaches lead to hundreds of thousands of hours of delay, costing billions of dollars annually. Weather and airport conditions may instigate this delay, but routing decisions balancing delay with congestion contribute significantly to the propagation of delays throughout the US airspace. The task of managing delay may be seen as a multiagent congestion problem. In this problem there are many tightly coupled agents whose actions collectively impact the system, making it difficult for agents to learn how they individually affect the system. Reward shaping is effective at improving agent learning for soft constraint problems by reducing noise caused by interactions with other agents, so we extend those results to hard constraints that cannot be easily learned, and must be enforced algorithmically. Additionally, congestion must be removed from the system in order to ensure a safe environment for all aircraft. This can be done through the use of a greedy scheduler, enforcing a ground delay on each plane to remove congestion from the system, at the cost of delay. Reward shaping reduces noise caused by agent interactions, and a greedy scheduler removes congestion from the system, but these two approaches cannot be simply combined without a large increase in computational complexity. Agent partitioning can be used to alleviate this complexity by treating each partition of agents as an independent set of agents from other partitions, thus simplifying the computation during each learning step. Our approach is based on the combination of three different methods to perform hard constraint optimization: a greedy scheduler, reward shaping and agent partitioning. We present two agent partitioning algorithms in conjunction with this approach to simplify the learning domain. The first partitioning algorithm uses system features to compute similarities between agents. This is then used to partition agents into small subsets. To remove the assumption of having domain knowledge, we introduce the Reward/Utility Based Impact (RUBI) algorithm. This algorithm develops an effective similarity matrix while requiring no prior domain knowledge. This similarity matrix can then be used in any similarity based partitioning algorithm. Our results show that autonomous partitioning of the agents using system features leads to up to 450x speed over simple hard constraint enforcement, as well as up to a 37% improvement in performance over a greedy scheduling solution, while using RUBI to partition agents led to a better partitioning of agents, as well as a 510x speed up. Both results correspond to hundreds of hours of delay saved in a single day

Conformational search, structural analysis, vibrational properties, reactivity study and affinity towards DNA of the novel insecticide flonicamid

This work deals with the study of structural properties, vibrational analysis, and reactivity of the novel insecticide flonicamid. Eight different minimum energy conformers were found during a conformational search and subsequent geometry optimizations at different levels of theory. The analysis of the PES leads to eight zones of minimum energy due to the scan of selected dihedral angles, which agrees with the structure of the eight most stable conformers found in the conformational search. The vibrational analysis and a complete assignment of the vibrational modes were performed, evidencing a good correlation with the experimental reported data. Furthermore, to predict the reactivity of flonicamid, different descriptors were analyzed. Finally, results obtained from docking analysis suggested that due to the molecular interaction with a sequence of DNA, the possible carcinogenic effects of flonicamid and three of its most important metabolites must be evaluated experimentally.Fil: Corregidor, Pablo Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones para la Industria Química. Universidad Nacional de Salta. Facultad de Ingeniería. Instituto de Investigaciones para la Industria Química; ArgentinaFil: Zígolo, María Antonela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones para la Industria Química. Universidad Nacional de Salta. Facultad de Ingeniería. Instituto de Investigaciones para la Industria Química; ArgentinaFil: Ottavianelli, Emilce Ethel. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones para la Industria Química. Universidad Nacional de Salta. Facultad de Ingeniería. Instituto de Investigaciones para la Industria Química; Argentin

Heme-iron complexing biphenyl and naphthalene derivatives as CYP17 inhibitorsfor the treatment of prostate cancer : design, synthesis and evaluation

Insufficient efficacy and unsafe treatment options are the current state of the art of prostate cancer therapies. This motivated us to develop antiandrogenic drugs with reduced side effects, aiming at effective tumor growth prevention and regression for prostatic adenocarcinomas. The key enzyme in androgen synthesis CYP17 is to date the most promising target for an overall, mild treatment of prostate carcinomas. Thus, it was our aim to develop nonsteroidal CYP17 inhibitors. In the present work, the synthesis, biological evaluation and molecular modeling studies of 69 compounds divided in three main classes are presented. These were published in three different international scientific magazines. A further manuscript is already prepared. Other related compounds, not essential for this work, are already published or to be published soon in four other co-authored works.Ungenügend wirksame und risikoreiche Behandlungen sind der heutige Stand der Prostatakrebstherapien. Dies motivierte uns zur Entwicklung antiandrogener nebenwirkungs-armer Wirkstoffe mit dem Ziel eine effektive Tumorwachstumprävention und -regression prostatischer Adenokarzinome zu erreichen. Das Schlüsselenzym der Androgensynthese CYP17 ist bis dato das vielversprechendste Target für eine Behandlung des Prostatakarzinoms. Dementsprechend war es unser Ziel nichtsteroidale CYP17 Inhibitoren zu entwickeln. In der vorliegenden Arbeit wurde die Synthese, biologische Evaluierung und Molecular Modeling Studien von 69 Verbindungen in drei Substanzklassen vorgestellt. Diese wurden in drei verschiedenen internationalen Zeitschriften publiziert. Ein weiteres Manuskript ist fertiggestellt. Andere verwandte, für diese Arbeit nicht essentielle Verbindungen, sind in vier weiteren Publikationen niedergelegt

AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets

MOTIVATION: Sampling the conformational space is a fundamental step for both ligand- and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of methods such as Monte Carlo, genetic, and reconstruction algorithms. Although some software already implements clustering procedures, at present, a universally accepted protocol is still missing. RESULTS: We integrated hierarchical agglomerative cluster analysis with a clusterability assessment method and a user independent cutting rule, to form a global protocol that we implemented in a MATLAB metalanguage program (AClAP). We tested it on the conformational space of a quite diverse set of drugs generated via Metropolis Monte Carlo simulation, and on the poses we obtained by reiterated docking runs performed by four widespread programs. In our tests, AClAP proved to remarkably reduce the dimensionality of the original datasets at a negligible computational cost. Moreover, when applied to the outcomes of many docking programs together, it was able to point to the crystallographic pose

A powerful combination of computational methods on the road toward potent non-steroidal inhibitors of steroidogenic enzymes involved in hormone-dependent diseases

Different computational methods have been applied to the development of non-steroidal inhibitors of steroidogenic enzymes for the treatment of hormone-sensitive diseases, like breast and prostate cancer and hypertension. A high-quality homology model of CYP17 was created and used for docking studies. Three binding modes were identified and SAR for different CYP17-inhibitor classes, substantiated by ab initio calculations, could be derived and used in further drug design leading to improved potency. Docking studies and ligand cluster analysis for a series of CYP19 inhibitors resulted in a binding mode, well explaining their different inhibitory potencies. The derived SAR were used in ongoing drug design, resulting so far in highly potent CYP19 inhibitors. The kinetic cycle of 17β-HSD1 was hypothesized based on biochemical data, analysis of the crystal structures and a multi-trajectory MD approach and provided insights in protein motion. These were translated into the drug design process. An ensemble docking study was performed for bis(hydroxyphenyl)-arenes, potent 17β-HSD1 inhibitors, and two conformation-dependent binding modes were identified. MD simulations and quantum-chemical calculations identified one of them as the more plausible, suggesting this class of compounds to dysfunction the enzyme dynamics. Two pharmacophore models were derived from CYP11B2 inhibitors and combined into a ligand- and structure-based approach, which led to a new class of potent CYP11B2 inhibitors.Verschiedene computergestützte Methoden wurden in der Entwicklung von nicht-steroidalen Hemmstoffen steroidogener Enzyme angewandt. Diese Inhibitoren sollen zur Behandlung hormon-abhängiger Krankheiten eingesetzt werden. Ein qualitativ-hochwertiges CYP17 Homologiemodell wurde erstellt und in Docking Studien verwendet. Drei Bindungsmodi konnten identifiziert werden und Struktur-Wirkungsbeziehungen wurden für verschiedene CYP17 Hemmstoffklassen abgeleitet und erfolgreich in eine weitere Hemmstoffentwicklung eingebaut. Dockingstudien und Clusteranalysen einer Reihe von CYP19 Inhibitoren ergaben einen plausiblen Bindungsmodus und die daraus gewonnenen Erkenntnisse führten in laufenden Projekten zu höchst potenten Hemmstoffen. Der kinetische Zyklus von 17β-HSD1 wurde postuliert basierend auf biochemischen Literaturdaten, Kristallstrukturenanalyse und einem multiplen MD-Ansatz, welcher wichtige Einblicke in die Dynamik des Enzyms lieferte. Diese wurden als ensemble docking Ansatz in die Entwicklung einer Klasse hochpotenter 17β-HSD1 Inhibitoren eingebaut und ergaben zwei Enzymkonformations-abhängige Bindungsmodi. MD-Simulationen und quantenchemische Methoden identifizierten einen davon als plausibler. Dabei scheinen Substanzen dieser Klasse die Enzymdynamik zu stören. Zwei Pharmakophormodelle wurden basierend auf CYP11B2-Hemmstoffen erstellt und in einen Ligand- und Struktur-basierten Ansatz eingebaut. Dieser führte zu einer neuen Klasse von potenten und selektiven CYP11B2-Inhibitoren

AClAP, Autonomous hierarchical agglomerative Cluster Analysis based protocol to partition conformational datasets

MOTIVATION: Sampling the conformational space is a fundamental step for both ligand- and structure-based drug design. However, the rational organization of different molecular conformations still remains a challenge. In fact, for drug design applications, the sampling process provides a redundant conformation set whose thorough analysis can be intensive, or even prohibitive. We propose a statistical approach based on cluster analysis aimed at rationalizing the output of methods such as Monte Carlo, genetic, and reconstruction algorithms. Although some software already implements clustering procedures, at present, a universally accepted protocol is still missing. RESULTS: We integrated hierarchical agglomerative cluster analysis with a clusterability assessment method and a user independent cutting rule, to form a global protocol that we implemented in a MATLAB metalanguage program (AClAP). We tested it on the conformational space of a quite diverse set of drugs generated via Metropolis Monte Carlo simulation, and on the poses we obtained by reiterated docking runs performed by four widespread programs. In our tests, AClAP proved to remarkably reduce the dimensionality of the original datasets at a negligible computational cost. Moreover, when applied to the outcomes of many docking programs together, it was able to point to the crystallographic pose

Applications of metadynamics to drug design-related issues

Applications of metadynamics to drug design-related issues Andrea Cavalli 1Department of Pharmaceutical Sciences, University of Bologna, Via Belmeloro 6, I-40126 Bologna, Italy The methadynamics method1 is a relatively new molecular dynamics technique aimed at both enhancing the sampling of regions separated in the phase space, and mapping out the free energy landscape as a function of a small number of parameters commonly referred to as collective variables. The metadynamics algorithm uses repulsive potentials that allow the system to escape from local minima over the lowest transition state, and discourage the system from revisiting the same points in the configurational space. In such a way, the method not only accelerates the simulation of rare events, but also maps out the free energy landscape as a function of chemically relevant collective variables. This lecture reports on applications of metadynamics to drug design-related issues. To illustrate the potential of metadynamics as docking tool, the dynamics of the tetramethylamonium (TMA) penetration of human acetylcholinesterase gorge will be discussed.2 Then, applications of metadynamics to docking experiments will be presented. In particular, we will show the use of metadynamics as a post-docking tool to discriminate among different minima identified by means of conventional docking software (GOLD, AutoDock, etc.) and cluster analysis performed with the algorithm AClAP.3 Applications of metadynamics as a post-docking tool to the GSK3 and FabI enzyme-inhibitor complexes will be discussed. [1] Laio A, Parrinello M. Escaping free-energy minima. Proc. Natl. Acad. Sci. USA 2002, 99, 12562-12566. [2] Branduardi, D.; Gervasio, F.L.; Cavalli, A.; Recanatini, M.; Parrinello, M. The role of the peripheral anionic site and cation-π interactions in the ligand penetration of the human AChE gorge. J. Am. Chem. Soc. 2005, 127, 9147-9155. [3] Bottegoni, G.; Rocchia, W.; Recanatini, M.; Cavalli, A. AClAP, Autonomous hierarchical agglomerative Cluster Analysis based Protocol to partition conformational datasets. Bioinformatics 2006, 22, e58-e65