Identification of drug candidates and repurposing opportunities through compound-target interaction networks

Introduction: System-wide identification of both on- and off-targets of chemical probes provides improved understanding of their therapeutic potential and possible adverse effects, thereby accelerating and de-risking drug discovery process. Given the high costs of experimental profiling of the complete target space of drug-like compounds, computational models offer systematic means for guiding these mapping efforts. These models suggest the most potent interactions for further experimental or pre-clinical evaluation both in cell line models and in patient-derived material.Areas covered: The authors focus here on network-based machine learning models and their use in the prediction of novel compound-target interactions both in target-based and phenotype-based drug discovery applications. While currently being used mainly in complementing the experimentally mapped compound-target networks for drug repurposing applications, such as extending the target space of already approved drugs, these network pharmacology approaches may also suggest completely unexpected and novel investigational probes for drug development.Expert opinion: Although the studies reviewed here have already demonstrated that network-centric modeling approaches have the potential to identify candidate compounds and selective targets in disease networks, many challenges still remain. In particular, these challenges include how to incorporate the cellular context and genetic background into the disease networks to enable more stratified and selective target predictions, as well as how to make the prediction models more realistic for the practical drug discovery and therapeutic applications.Peer reviewe

An Efficient Method for Large Margin Parameter

We consider structured prediction problems with a parametrized linear prediction function, and the associated parameter optimization problems in large margin type of discriminative training. We propose a dual optimization approach which uses the restricted simplicial decomposition method to optimize a reparametrized dual problem. Our reparametrization reduces the dimension of the space of the dual function to one that is linear in the number of parameters and training examples, and hence independent of the dimensionality of the prediction outputs. This in conjunction with simplicial decomposition makes our approach efficient. We discuss the connections of our approach with related earlier works, and we show its advantages

Value parameters rationalization of supply chains material flows of the building industry enterprises

Стаття присвячена розгляду математичної моделі управління матеріальними запасами із узагальненими номенклатурними випадками, що дозволяє оптимізувати витрати підприємства на етапі переміщення та зберігання продукції від постачальників у необхідну точку попиту (будівельний майданчик, склад будівельної організації, філію тощо), враховує багатономенклат урність поставки продукції із суттєво розширеною параметричною базою, зокрема, площу складування, обмеження щодо мінімального розміру і вартості доставленої продук ції, витрати на зберігання, можливі обсяги замовлення, втрачену вигоду і т.д.The article is devoted to the mathematical inventory control models with generalized nomenclature cases, to optimize the cost of the enterprise at the stage of moving and storage products from suppliers in the desired point of demand (construction site, a warehouse of a const ruction company, subsidiaries, etc.), takes into account a large range of products significantly enhanced parametric basis, in particular, storage area, restrictions on minimum size and cost of the delivered products, storage costs, possible volumes of orders, loss of profit, etc

Polttokennojen ominaisuudet ja sähkötekninen mallintaminen

Uusiutuvan energiantuotannon tarpeen ja uusiutuvan vedyn tuotannon kasvaessa kiinnostus polttokennoihin on lisääntynyt. Tämä näkyy myös meriliikenteessä, jossa polttokennoja voidaan jo käyttää joko pääenergiantuotannossa tai lisätehona. Polttokennot eivät ole monien muiden uusiutuvien energiantuotantotapojen tapaan riippuvaisia säästä tai vuorokaudenajasta. Niillä voidaan siis tuottaa energiaa teoriassa niin kauan kuin polttoainetta syötetään. Polttokennoteknologioita on useita ja niiden toimintalämpötilat vaihtelevat suuresti. Niiden tuottamaa lämpöä voidaan myös hyödyntää monin eri tavoin käyttökohteesta riippuen. Polttokennot yhdistetään sähköverkkoon yleensä DC/DC-muuntimella ja vaihtosuuntaajalla. Tasajännitteisessä verkossa vaihtosuuntaajaa ei tarvita. Diplomityö tehtiin Yaskawa Environmental Energy/The Switchille. Työn tavoitteena oli tehdä laaja kirjallisuuskatsaus eri polttokennoteknologioista ja polttokennojärjestelmistä. Tämän lisäksi tavoitteena oli selvittää polttokennojärjestelmien niin yleisiä kuin myös sähköisiä ominaisuuksia, polttokennojen käyttöä meriliikenteessä ja tutkia DC/DC-muuntimia ja virtasäröä polttokennojärjestelmässä. Kirjallisuuskatsauksen lisäksi työssä mallinnettiin erään ammoniakilla toimivan kiinteäoksidipolttokennopinon dynaamista toimintaa polttoaineensyötön ja kuormituksen muutoksissa. Työn tuloksena saatiin selville nykymarkkinoiden potentiaalisimmat polttokennoteknologiat ja käsiteltiin syvällisemmin nopeammin reagoivia matalan lämpötilan protoninvaihtopolttokennoja ja paremman hyötysuhteen omaavia korkean lämpötilan kiinteäoksidipolttokennoja. Lisäksi tarkasteltiin lyhyesti myös protoninvaihtopolttokennoista johdettuja teknologioita. Polttokennojärjestelmät todettiin osittain kilpailukykyisiksi muille markkinoilla oleville energiantuotantotavoille ja energiavarastoille muun muassa suorituskyvyn ja hyötysuhteen osalta. Polttokennoilla on kuitenkin yleisesti erittäin vaihteleva jännitealue ja ne ovat hitaita käynnistymään sekä reagoimaan tehontarpeen muutoksiin. Tästä syystä esimerkiksi laivoissa käytetäänkin niiden rinnalla akustoja vastaamaan nopeisiin tehomuutoksiin. DC/DC-muuntimen tutkimisessa perehdyttiin sen eri toteutustapoihin ja polttokennokäytössä tarvittaviin ominaisuuksiin. Virtasärön tutkimisessa selvisi, että polttokennot ovat herkkiä pääasiassa vaihtosuuntaajan aiheuttamalle matalataajuiselle (alle kilohertsin) virtasärölle. DC/DC-muunnin tuottaa pääasiassa korkeataajuista (useiden kilohertsien) virtasäröä, joka on matalataajuista virtasäröä harmittomampaa. Kiinteäoksidipolttokennopinon dynaamisen toiminnan mallinnuksessa todettiin, että viiveettömät muutokset aiheuttavat jännite- ja virtapiikkejä, mutta normaalissa toiminnassa ilmenevillä muutosnopeuksilla ei synny merkittäviä jännite- tai virtapiikkejä.As the need for renewable energy production and the production of renewable hydrogen increases, the interest towards fuel cells is also increasing. This is also seen in maritime transport, where fuel cells can already be used for either main energy production or as additional power. Fuel cells, like many other renewable energy production methods, are not dependent on weather or time of the day. They can thus theoretically produce energy as long as fuel is supplied. There are several fuel cell technologies and their operating temperatures vary greatly. The heat they generate can also be utilized in many different ways depending on the application. Fuel cells are connected to electrical network usually with a DC/DC converter and an inverter. In a DC network, an inverter is not required. This thesis was done for Yaskawa Environmental Energy/The Switch. The aim of the work was to make an extensive literature review of different fuel cell technologies and systems. Also, the aim was to investigate both the general and electrical properties of fuel cell systems, the use of fuel cells in maritime transport, and to study DC/DC converters and current ripple in a fuel cell system. In addition to the literature review, the dynamic operation of an ammonia fed solid oxide fuel cell stack in changes of both fuel supply and load was modeled. As a result of the work, the most potential fuel cell technologies on the present market were identified, and faster-reacting low-temperature proton exchange fuel cells and higher-efficiency high-temperature solid oxide fuel cells were discussed in more depth. In addition to the discussion, technologies derived from proton exchange fuel cells were also briefly reviewed. Fuel cell systems were found to be partially competitive with other energy production methods and energy storages on the market, inter alia, in terms of performance and efficiency. However, fuel cells generally have a very variable voltage range and are slow to start and respond to changes in power demand. For this reason, in maritime applications, for example, batteries are used alongside them to meet rapid power needs. The study of the DC/DC converter looked at its different topologies and properties required in fuel cell applications. Examination of current ripple revealed that fuel cells are sensitive to low frequency (less than a kilohertz) current ripple caused mainly by the inverter. The DC/DC converter mainly produces high frequency (several kilohertz) current ripple, which is less harmful than low frequency current ripple. In the modeling of dynamic operation of solid oxide fuel cell stack, it was found that instant changes cause voltage and current spikes, but the rates of change occurring in normal operation do not cause significant voltage or current spikes

Towards data-driven mass spectrometry in atmospheric science

Aerosols found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Vast amounts of data are collected in each measurement of current time-of-flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains as a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data-driven compound identification approaches could alleviate the problem, yet remain rare to non-existent in atmospheric science. In this perspective, we review the current state of data-driven compound identification with mass spectrometry in atmospheric science, and discuss current challenges and possible future steps towards a digital mass spectrometry era in atmospheric science

Metabolite identification and molecular fingerprint prediction through machine learning

Motivation: Metabolite identification from tandem mass spectra is an important problem in metabolomics, underpinning subsequent metabolic modelling and network analysis. Yet, currently this task requires matching the observed spectrum against a database of reference spectra originating from similar equipment and closely matching operating parameters, a condition that is rarely satisfied in public repositories. Furthermore, the computational support for identification of molecules not present in reference databases is lacking. Recent efforts in assembling large public mass spectral databases such as MassBank have opened the door for the development of a new genre of metabolite identification methods. Results: We introduce a novel framework for prediction of molecular characteristics and identification of metabolites from tandem mass spectra using machine learning with the support vector machine. Our approach is to first predict a large set of molecular properties of the unknown metabolite from salient tandem mass spectral signals, and in the second step to use the predicted properties for matching against large molecule databases, such as PubChem. We demonstrate that several molecular properties can be predicted to high accuracy and that they are useful in de novo metabolite identification, where the reference database does not contain any spectra of the same molecule. Availability: An Matlab/Python package of the FingerID tool is freely available on the web at http://www.sourceforge.net/p/fingerid. Contact: [email protected]