243 research outputs found

    Machine Learning Small Molecule Properties in Drug Discovery

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    Machine learning (ML) is a promising approach for predicting small molecule properties in drug discovery. Here, we provide a comprehensive overview of various ML methods introduced for this purpose in recent years. We review a wide range of properties, including binding affinities, solubility, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity). We discuss existing popular datasets and molecular descriptors and embeddings, such as chemical fingerprints and graph-based neural networks. We highlight also challenges of predicting and optimizing multiple properties during hit-to-lead and lead optimization stages of drug discovery and explore briefly possible multi-objective optimization techniques that can be used to balance diverse properties while optimizing lead candidates. Finally, techniques to provide an understanding of model predictions, especially for critical decision-making in drug discovery are assessed. Overall, this review provides insights into the landscape of ML models for small molecule property predictions in drug discovery. So far, there are multiple diverse approaches, but their performances are often comparable. Neural networks, while more flexible, do not always outperform simpler models. This shows that the availability of high-quality training data remains crucial for training accurate models and there is a need for standardized benchmarks, additional performance metrics, and best practices to enable richer comparisons between the different techniques and models that can shed a better light on the differences between the many techniques.Comment: 46 pages, 1 figur

    A comprehensive study on nanoparticle drug delivery to the brain: application of machine learning techniques

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    The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to limited understanding of how nanoparticle (NP) properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by using various predictive models based on collection of large data sets of 403 data points incorporating both numerical and categorical features. Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain. Furthermore, the physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve. The analysis employed various linear models, with a particular emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional accuracy. The model was validated via the preparation and administration of two distinct NP formulations via the intranasal and intravenous routes. Among the various modeling approaches, LMEMs exhibited superior performance in capturing underlying patterns. Factors such as the release rate and molecular weight had a negative impact on brain targeting. The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate

    2022 Review of Data-Driven Plasma Science

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    Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required

    High-Performance Modelling and Simulation for Big Data Applications

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    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

    Characterization of Jet Fuel Combustion Emissions During a C-130 Aeromedical Evacuation Engines Running Onload

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    The purpose of this research was to characterize jet fuel combustion emissions (JFCE) in an occupational setting. Prior research demonstrated that aircraft emit hazardous species, especially at engine start-up and ground idle. Complaints of eye, nose, and throat irritation from occupational exposures near aircraft exist. In this study JFCE were tested during an aeromedical evacuation engines running patient onload (ERO) on a C-130 Hercules at the 179th Airlift Wing, Mansfield-Lahm Air National Guard. Ultrafine particles, VOC, formaldehyde, carbon monoxide (CO), sulfuric acid, and metals were sampled simultaneously in approximate crew and patient breathing zones. Testing methods were portable condensation particle counters (CPC), polycarbonate filters (PC) and thermophoretic samplers (TPS) for electron microscopy, MultiRae® gas monitors, EPA methods TO-17 and TO-11, and NIOSH methods N0600, N7908, N7300. Ultrafine particulate matter, VOC including EPA HAPs, formaldehyde, CO, and unburned jet fuel were detected. Particles were dominated by soot that was predominantly carbonaceous with trace oxygen, sulfur and few metals in concentrations up to 3.4E+06 particles/cc. Particle size distributions were varied with most sizes less than 100 nanometers (nm). Particle morphology was highly irregular. VOC were detected in ppb, and formaldehyde in ppm. Additive or synergistic effects are suspected and may intensify irritation. Health implications from inhaling nano-sized soot particles are inconclusive

    Dynamics of Chemical Degradation in Water Using Photocatalytic Reactions in an Ultraviolet Light Emitting Diode Reactor

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    This work examined ultraviolet (UV) light emitting diodes (LED) and hydrogen peroxide in an advanced oxidation process in support of a USAF installation net zero water initiative. A UV LED reactor was used for degradation of soluble organic chemicals. There were linear relationships between input drive current, optical output power, and first order degradation rate constants. When drive current was varied, first order degradation rates depended on chemical identities and the drive current. When molar peroxide ratios were varied, kinetic profiles revealed peroxide-limited or radical-scavenged phenomena. Molar absorptivity helped explain the complexity of chemical removal profiles. Degradation kinetics were used to compare fit of molecular descriptors from published quantitative structure property relationship (QSPR) models. A novel QSPR model was built using zero point energy and molar absorptivity as predictors. Finally, a systems architecture was used to describe a net zero water program and proposed areas for UV LED reactor integration. Facility-level wastewater treatment was found to be the most feasible near-term application

    Development of quantitative structure property relationships to support non-target LC-HRMS screening

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    Κατά την τελευταία δεκαετία, ένας μεγάλος αριθμός αναδυόμενων ρύπων έχουν ανιχνευθεί και ταυτοποιηθεί σε επιφανειακά ύδατα και λύματα, προκαλώντας ανησυχία για το υδάτινο οικοσύστημα, λόγω της πιθανής χημικής τους σταθερότητας. Η τεχνική της υγροχρωματογραφίας - φασματομετρίας μάζας υψηλής διακριτικής ικανότητας (LC-HRMS) αποτελεί μια αποτελεσματική τεχνική για την ανίχνευση αναδυόμενων ρύπων στο περιβάλλον. Η ταυτόχρονη δε ανάλυση των δειγμάτων με τις συμπληρωματικές τεχνικές της υγροχρωματογραφίας αντίστροφης φάσης (RPLC) και της υγροχρωματογραφίας υδρόφιλων αλληλεπιδράσεων (HILIC), συντελεί στην ταυτοποίηση «ύποπτων» ή και άγνωστων ρύπων με ποικίλες φυσικοχημικές ιδιότητες. Για την ταυτοποίηση τους, απαιτείται να πληρούνται συγκεκριμένα κριτήρια, τα οποία αξιολογούνται με βάση τη χρήση διαγνωστικών εργαλείων, όπως η ακριβής πρόβλεψη του χρόνου ανάσχεσης, η in silico θραυσματοποίηση και η πρόβλεψη της συμπεριφορά τους στον ιοντισμό. Στο 3ο κεφάλαιο της παρούσας διδακτορικής διατριβής περιγράφεται η ανάπτυξη μιας ολοκληρωμένης πορείας εργασίας (workflow) για τη διερεύνηση των παραμέτρων που επηρεάζουν τον χρόνο έκλουσης μεγάλου αριθμού ενώσεων που συγκαταλέγονται στους αναδυόμενους ρύπους. Για τον σκοπό αυτό, πάνω από 2.500 αναδυόμενοι ρύποι χρησιμοποιήθηκαν για την ανάπτυξη του μοντέλου πρόβλεψης χρόνου ανάσχεσης για τις 2 υγροχρωματογραφικές τεχνικές (RP- και HILIC-LC-HRMS) και για ηλεκτροψεκασμό τόσο σε θετικό όσο και σε αρνητικό ιοντισμό (+/-ESI). Στη συνέχεια, πραγματοποιήθηκε εφαρμογή του μοντέλου για την υπολογιστική πρόβλεψη του χρόνου ανάσχεσης, για την ταυτοποίηση 10 νέων προϊόντων μετασχματισμού των φαρμακευτικών ενώσεων (tramadol, furosemide και niflumic acid) ύστερα από επεξεργασία με όζον. Στο 4ο κεφάλαιο παρουσιάζεται η ανάπτυξη ενός καινοτόμου γενικευμένου χημειομετρικού μοντέλου το οποίο είναι ικανό να προβλέπει τον χρόνο έκλουσης κάθε πιθανού ρύπου, ανεξαρτήτου υγροχρωματογραφικής μεθόδου που χρησιμοποιείται, συμβάλλοντας σημαντικά στην σύγκριση αποτελεσμάτων από διαφορετικές LC-HRMS μεθόδους. Το συγκεκριμένο μοντέλο χρησιμοποιήθηκε για την ταυτοποίηση «ύποπτων» και άγνωστων ενώσεων σε διεργαστηριακές δοκιμές. Το Κεφάλαιο 5, περιέχει την περιγραφή της ανάπτυξης ενός υπολογιστικού μοντέλου πρόβλεψης τοξικότητας αναδυόμενων ρύπων που ανιχνεύονται στο υδάτινο οικοσύστημα. Το συγκεκριμένο μοντέλο αποσκοπεί στην εκτίμηση του πιθανού περιβαλλοντικού κινδύνου για νέες ενώσεις που ταυτοποιήθηκαν μέσω σάρωσης «ύποπτων» ενώσεων και μη-στοχευμένης σάρωσης, για τις οποίες δεν είναι ακόμα διαθέσιμα πειραματικά δεδομένα τοξικότητας. Τέλος, στο κεφάλαιο 6 παρουσιάζεται ένας αυτοματοποιημένος και συστηματικός τρόπος σάρωσης «ύποπτων» ενώσεων και μη-στοχευμένης σάρωσης σε δεδομένα από LC-HRMS. Η νέα αυτή αυτοματοποιημένη πορεία εργασίας, αποσκοπεί στην λιγότερο χρονοβόρα επεξεργασία των HRMS δεδομένων, και στην εφαρμογή της μη-στοχευμένης σάρωσης ώστε να είναι δυνατή η εφαρμογή τους σε καθημερινούς ελέγχους ρουτίνας ή/και για χρήση από τις κανονιστικές αρχές.Over the last decade, a high number of emerging contaminants were detected and identified in surface and waste waters that could threaten the aquatic environment due to their pseudo-persistence. As it is described in chapters 1 and 2, liquid chromatography high resolution mass spectroscopy (LC-HRMS) can be used as an efficient tool for their screening. Simultaneously screening of these samples by hydrophilic interaction liquid chromatography (HILIC) and reversed phase (RP) would help with full identification of suspects and unknown compounds. However, to confirm the identity of the most relevant suspect or unknown compounds, their chemical properties such as retention time behavior, MSn fragmentation and ionization modes should be investigated. Chapter 3 of this thesis discusses the development of a comprehensive workflow to study the retention time behavior of large groups of compounds belonging to emerging contaminants. A dataset consisted of more than 2500 compounds was used for RP/HILIC-LC-HRMS, and their retention times were derived in both Electrospray Ionization mode (+/-ESI). These in silico approaches were then applied on the identification of 10 new transformation products of tramadol, furosemide and niflumic acid (under ozonation treatment). Chapter 4 discusses about the development of a first retention time index system for LC-HRMS. Some practical applications of this RTI system in suspect and non-target screening in collaborative trials have been presented as well. Chapter 5 describes the development of in silico based toxicity models to estimate the acute toxicity of emerging pollutants in the aquatic environment. This would help link the suspect/non-target screening results to the tentative environmental risk by predicting the toxicity of newly tentatively identified compounds. Chapter 6 introduces an automatic and systematic way to perform suspect and non-target screening in LC-HRMS data. This would save time and the data analysis loads and enable the routine application of non-target screening for regulatory or monitoring purpose

    High-Performance Modelling and Simulation for Big Data Applications

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    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

    Integrated Chemical Processes in Liquid Multiphase Systems

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    The essential principles of green chemistry are the use of renewable raw materials, highly efficient catalysts and green solvents linked with energy efficiency and process optimization in real-time. Experts from different fields show, how to examine all levels from the molecular elementary steps up to the design and operation of an entire plant for developing novel and efficient production processes

    Multivariate Analysis in Management, Engineering and the Sciences

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    Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field
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