Efficient Discovery Research Articles

SMolBoxes: Dataflow Model for Molecular Dynamics Exploration.

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

Development of a single-step fluorogenic sirtuin assay and its applications for high-throughput screening.

Sirtuins (SIRTs) are a class of nicotinamide adenine dinucleotide (NAD+)-dependent histone deacetylases. Since SIRTs have different subcellular locations and different preferences for deacylation activity, SIRTs are not only highly gaining significance in biological functions but also implications in human diseases. Therefore, it is valuable to establish a high-throughput screening method for the rapid and accurate discovery of SIRT modulators. In this study, we designed and synthesized small molecules 4a-d as fluorogenic probes based on the different lysine substrates of SIRTs, which can be recognized and catalyzed by SIRTs and then spontaneous intramolecular transesterification can give the fluorescence. We have undertaken a comprehensive study of these fluorogenic probes with different SIRTs for assay optimization, validation, kinetics, parameters, and applications of high-throughput screening formats. We envision that these probes will provide useful and powerful tools for the highly efficient discovery of more SIRT inhibitors.

Efficient Discovery of Room Temperature Magnetic Transition Metal Monolayers Assisted by Artificial Neural Network

Efficient Discovery of Room Temperature Magnetic Transition Metal Monolayers Assisted by Artificial Neural Network

Relating Conformational Equilibria to Conformer-Specific Lipophilicities: New Opportunities in Drug Discovery.

Efficient drug discovery is based on a concerted effort in optimizing bioactivity and compound properties such as lipophilicity, and is guided by efficiency metrics that reflect both aspects. While conformation–activity relationships and ligand conformational control are known strategies to improve bioactivity, the use of conformer‐specific lipophilicities (logp) is much less explored. Here we show how conformer‐specific logp values can be obtained from knowledge of the macroscopic logP value, and of the equilibrium constants between the individual species in water and in octanol. This is illustrated with fluorinated amide rotamers, with integration of rotamer 19F NMR signals as a facile, direct method to obtain logp values. The difference between logp and logP optimization is highlighted, giving rise to a novel avenue for lipophilicity control in drug discovery.

A mathematical nutrition model adequately predicts beef and dairy cow intake and biological efficiency.

The beef cow-calf sector accounts for 70% of feed consumed and greenhouse gases emitted for the beef industry, but there is no straightforward method to measure biological efficiency in grazing conditions. The objective of this study was to evaluate a mathematical nutrition model to estimate the feed intake and biological efficiency of mature beef cows. Data from dams (N = 160) and their second and third progeny (312 pairs) were collected from 1953 through 1980. Individual feed intake was measured at 28-d intervals year-round for dams and during 240-d lactation for progeny. Body weights of progeny were measured at 28-d intervals from birth to weaning, and of dams at parturition and weaning each production cycle. The milk yield of dams was measured at 14-d intervals. Dam metabolizable energy intake (DMEI) and milk energy yield (MEL) of each cow were predicted using the Cattle Value Discovery System beef cow (CVDSbc) model for each parity. Biological efficiency (Mcal/kg) was computed as the ratio of observed or predicted DMEI to observed calf weaning weight (PWW). Pearson correlation coefficients were computed using corr.test function and model evaluation was performed using the epiR function in R software. Average (SD) dam weight, PWW, DMEI, and observed MEL were 527 (86) kg, 291 (47) kg, 9584 (2701) Mcal/production cycle, and 1029 (529) Mcal, respectively. Observed and predicted DMEI (r = 0.93 and 0.91), and observed and predicted MEL (r = 0.58 and 0.59) were positively correlated for progeny 2 and 3, respectively. The CVDSbc model under-predicted DMEI (mean bias [MB] = 1,120±76 Mcal, 11.7% of observed value) and MEL (MB = 30±25 Mcal, 2.9% of observed value). Observed and predicted progeny feed intake were not correlated (r = 0.01, P-value = 0.79). Observed and predicted biological efficiency were positively correlated (r = 0.80 and 0.80, P-value ≤ 0.05) for parity 2 and 3, respectively, and the CVDSbc model under-predicted biological efficiency by 11% (MB = 3.59±0.25 Mcal/kg). The CVDSbc provides reasonable predictions of feed intake and biological efficiency of mature beef cows, but further refinement of the relationship between calf feed intake and milk yield is recommended to improve predictions. Mathematical nutrition models can assist in the discovery of the biological efficiency of mature beef cows.

Rational Design of a Two‐Photon Fluorescent Probe for Human Cytochrome P450 3A and the Visualization of Mechanism‐Based Inactivation

AbstractMechanism‐based inactivation (MBI) can mediate adverse reactions and hepatotoxicity from drugs, which is a result of their conversion into highly reactive metabolites catalyzed by enzymes such as cytochrome P450 3A (CYP3A). In the present research, we optimized the key interaction domain of the fluorophore with the target protein to develop a two‐photon fluorescent probe for CYP3A that is involved in the metabolism of more than half of all clinical drugs. The developed BN‐1 probe exhibited appropriate selectivity and sensitivity for the semi‐quantitative detection and imaging of endogenous CYP3A activity in various living systems, thereby providing a high‐throughput screening system enabling evaluation of MBI‐associated hepatotoxicity by CYP3A. Using BN‐1 as a fluorescent molecular tool facilitates the efficient discovery and characterization of CYP3A‐induced MBI in natural systems.

Rational Design of a Two-Photon Fluorescent Probe for Human Cytochrome P450 3A and the Visualization of Mechanism-Based Inactivation.

Mechanism-based inactivation (MBI) can mediate adverse reactions and hepatotoxicity from drugs, which is a result of their conversion into highly reactive metabolites catalyzed by enzymes such as cytochrome P450 3A (CYP3A). In the present research, we optimized the key interaction domain of the fluorophore with the target protein to develop a two-photon fluorescent probe for CYP3A that is involved in the metabolism of more than half of all clinical drugs. The developed BN-1 probe exhibited appropriate selectivity and sensitivity for the semi-quantitative detection and imaging of endogenous CYP3A activity in various living systems, thereby providing a high-throughput screening system enabling evaluation of MBI-associated hepatotoxicity by CYP3A. Using BN-1 as a fluorescent molecular tool facilitates the efficient discovery and characterization of CYP3A-induced MBI in natural systems.

Efficient discovery of potential inhibitors for SARS-CoV-2 3C-like protease from herbal extracts using a native MS-based affinity-selection method

The 3C-like protease (3CLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is essential to the virus life cycle and is supposed to be a potential target for the treatment of coronaviral infection. Traditional Chinese medicines (TCMs) have played an impressive role in the treatment of COVID-19 in China. The effectiveness of TCM formulations prompts scientists to take continuous effort on searching for bioactive small molecules from the ancient resources. Herein, we developed a native mass spectrometry-based affinity-selection method for rapid screening of active small molecules from crude herbal extracts applied for COVID-19 therapy. Six common herbs named Lonicera japonica, Scutellaria baicalensis, Forsythia suspensa, Glycyrrhiza uralensis, Cirsium japonicum, and Andrographis paniculata were investigated. After preliminary separation of the crude extracts, the fractions were incubated with 3CLpro. A native MS-based affinity screening assay was then conducted to search for the protein-ligand complexes. A UHPLC-Q/TOF-MS with UNIFI data acquisition and data processing software was applied to identify the hit compounds. Standard compounds were used to verify the outcomes. Among the 16 hits, three flavonoids, baicalein, scutellarein and ganhuangenin, were identified as potential noncovalent inhibitors against 3CLpro with IC50 values of 0.94, 3.02, and 0.84 μM, respectively. Their binding affinities were further characterized by native MS, with Kd values being 1.43, 3.85, and 1.09 μM, respectively. Overall, we established an efficient native MS-based strategy for discovering 3CLpro ligands from crude mixtures, which supplies a potential strategy of small molecule lead discovery from TCMs.

An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs

Intelligent Transportation system are becoming an interesting research area, after Internet of Things (IoT)-based sensors have been effectively incorporated in vehicular ad hoc networks (VANETs). The optimal route discovery in a VANET plays a vital role in establishing reliable communication in uplink and downlink direction. Thus, efficient optimal path discovery without a loop-free route makes network communication more efficient. Therefore, this challenge is addressed by nature-inspired optimization algorithms because of their simplicity and flexibility for solving different kinds of optimization problems. NIOAs are copied from natural phenomena and fall under the category of metaheuristic search algorithms. Optimization problems in route discovery are intriguing because the primary objective is to find an optimal arrangement, ordering, or selection process. Therefore, many researchers have proposed different kinds of optimization algorithm to maintain the balance between intensification and diversification. To tackle this problem, we proposed a novel Java macaque algorithm based on the genetic and social behavior of Java macaque monkeys. The behavior model mimicked from the Java macaque monkey maintains well-balanced exploration and exploitation in the search process. The experimentation outcome depicts the efficiency of the proposed Java macaque algorithm compared to existing algorithms such as discrete cuckoo search optimization (DCSO) algorithm, grey wolf optimizer (GWO), particle swarm optimization (PSO), and genetic algorithm (GA).

Genetic Variation and the Distribution of Variant Types in the Horse.

Genetic variation is a key contributor to health and disease. Understanding the link between an individual’s genotype and the corresponding phenotype is a major goal of medical genetics. Whole genome sequencing (WGS) within and across populations enables highly efficient variant discovery and elucidation of the molecular nature of virtually all genetic variation. Here, we report the largest catalog of genetic variation for the horse, a species of importance as a model for human athletic and performance related traits, using WGS of 534 horses. We show the extent of agreement between two commonly used variant callers. In data from ten target breeds that represent major breed clusters in the domestic horse, we demonstrate the distribution of variants, their allele frequencies across breeds, and identify variants that are unique to a single breed. We investigate variants with no homozygotes that may be potential embryonic lethal variants, as well as variants present in all individuals that likely represent regions of the genome with errors, poor annotation or where the reference genome carries a variant. Finally, we show regions of the genome that have higher or lower levels of genetic variation compared to the genome average. This catalog can be used for variant prioritization for important equine diseases and traits, and to provide key information about regions of the genome where the assembly and/or annotation need to be improved.

OP279 Data Protection In The European Union Post-General Data Protection Regulation (GDPR): A Barrier Or An Enabler Of Pharmaceutical Innovation?

IntroductionThe expansion of health data offers exciting opportunities to support better and more efficient drug discovery, development and implementation. Data protection and governance provide the legal framework to balance safeguarding patients’ privacy with the benefits to society of medical research. Our aim is to highlight current legal barriers to the better use of health data and propose ways to address them.MethodsAnalysis of the relevant legislative texts was supplemented by interviews with external experts in data protection, health research, informatics and cyber security and a workshop with pharmaceutical industry members. We investigated the legal issues arising for six key activities along the pharmaceutical lifecycle, from identifying unmet need through to health technology assessment and pharmacovigilance.ResultsThe General Data Protection Regulation (GDPR) was introduced in May 2018 to Harmonise data protection across Europe. However, considerable ambiguity remains, particularly around the appropriate legal bases for data processing in the absence of consent: scientific research, public interest, or provision of health or social care. Other key themes included data subject rights, anonymization, compatibility of primary and secondary (re-)use of data, heterogeneity arising from divergent interpretation, the need for guidance on digital health, and the importance of trust.ConclusionsWe speculate which legal bases are most appropriate for the six pharmaceutical activities studied, but clear guidance and consensus is required. The GDPR was not designed to hamper scientific research, and the issues identified arose from uncertainties rather than barriers per se. Industry and academic researchers should therefore deal proactively with the prevailing uncertainties, share good practice, and engender trust by co-creating a code of conduct and outlining principles of responsible use. Engagement with patients will be critical in encouraging a shared understanding of the value to society of health data for research.

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.

Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principle Simulations.

Artificial intelligence (AI) based self-learning or self-improving material discovery system will enable next-generation material discovery. Herein, we demonstrate how to combine accurate prediction of material performance via first-principle calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PSs). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean absolute error of 0.090 eV for singlet-triplet spitting) and high-performance PS search ability, realizing efficient discovery of PSs. From a molecular space with more than 7 million molecules, 5357 potential high-performance PSs were discovered. Four PSs were further synthesized to show performance comparable with or superior to commercial ones. This work highlights the potential of active learning in first-principle-based materials design, and the discovered structures could boost the development of photosensitization related applications.

Accelerating Causal Inference and Feature Selection Methods through G-Test Computation Reuse.

This article presents a novel and remarkably efficient method of computing the statistical G-test made possible by exploiting a connection with the fundamental elements of information theory: by writing the G statistic as a sum of joint entropy terms, its computation is decomposed into easily reusable partial results with no change in the resulting value. This method greatly improves the efficiency of applications that perform a series of G-tests on permutations of the same features, such as feature selection and causal inference applications because this decomposition allows for an intensive reuse of these partial results. The efficiency of this method is demonstrated by implementing it as part of an experiment involving IPC–MB, an efficient Markov blanket discovery algorithm, applicable both as a feature selection algorithm and as a causal inference method. The results show outstanding efficiency gains for IPC–MB when the G-test is computed with the proposed method, compared to the unoptimized G-test, but also when compared to IPC–MB++, a variant of IPC–MB which is enhanced with an AD–tree, both static and dynamic. Even if this proposed method of computing the G-test is presented here in the context of IPC–MB, it is in fact bound neither to IPC–MB in particular, nor to feature selection or causal inference applications in general, because this method targets the information-theoretic concept that underlies the G-test, namely conditional mutual information. This aspect grants it wide applicability in data sciences.

A Metaphoric Design of Electronic Medical Record (EMR) for Periodic Health Examination Reports

The exponential growth of big data demands an efficient knowledge discovery. The electronic medical records of patients on medical data Clouds contain implicit medical information. Although the periodic health examination (PHE) reports describing a set of screening tests for healthy individuals performed periodically, common individuals require the assistance of an expert to interpret the results for a medical opinion. This research study proposes a metaphoric design of Electronic Medical Record (EMR) for PHE reports of patients. The outcomes of this study glimpses useful findings for the common people in the self-interpretation of their medical reports. Besides, among a variety of solutions, the study uses the metaphoric representation to convert the numerical data and medical terminology to familiar graphic representations from real life. The study identifies the detailed requirements to propose a conceptual architecture for metaphoric EMR reports. The future work will result in a prototype design, evaluation, and refinement of metaphors based on stakeholders' feedback.

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints.

The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.

A semantic web services discovery approach integrating multiple similarity measures and k-means clustering

Web service (WS) discovery is an essential task for implementing complex applications in a service oriented architecture (SOA), such as selecting, composing, and providing services. This task is limited semantically in the incorporation of the customer’s request and the web services. Furthermore, applying suitable similarity methods for the increasing number of WSs is more relevant for efficient web service discovery. To overcome these limitations, we propose a new approach for web service discovery integrating multiple similarity measures and k-means clustering. The approach enables more accurate services appropriate to the customer's request by calculating different similarity scores between the customer's request and the web services. The global semantic similarity is determined by applying k-means clustering using the obtained similarity scores. The experimental results demonstrated that the proposed semantic web service discovery approach outperforms the state-of-the approaches in terms of precision (98%), recall (95%), and F-measure (96%). The proposed approach is efficiently designed to support and facilitate the selection and composition of web services phases in complex applications.

Temporal state change Bayesian networks for modeling of evolving multivariate state sequences: model, structure discovery and parameter estimation

Many systems can be expressed as multivariate state sequences (MSS) in terms of entities and their states with evolving dependencies over time. In order to interpret the temporal dynamics in such data, it is essential to capture relationships between entities and their changes in state and dependence over time under uncertainty. Existing probabilistic models do not explicitly model the evolution of causality between dependent state sequences and mostly result in complex structures when representing complete causal dependencies between random variables. To solve this, Temporal State Change Bayesian Networks (TSCBN) are introduced to effectively model interval relations of MSSs under evolving uncertainty. Our model outperforms competing approaches in terms of parameter complexity and expressiveness. Further, an efficient structure discovery method for TSCBNs is presented, that improves classical approaches by exploiting temporal knowledge and multiple parameter estimation approaches for TSCBNs are introduced. Those are expectation maximization, variational inference and a sampling based maximum likelihood estimation that allow to learn parameters from partially observed MSSs. Lastly, we demonstrate how TSCBNs allow to interpret and infer patterns of captured sequences for specification mining in automotive.

MEdge-Chain: Leveraging Edge Computing and Blockchain for Efficient Medical Data Exchange

Medical data exchange between diverse e-health entities can lead to a better healthcare quality, improving the response time in emergency conditions, and a more accurate control of critical medical events (e.g., national health threats or epidemics). However, exchanging large amount of information between different e-health entities is challenging in terms of security, privacy, and network loads, especially for large-scale healthcare systems. Indeed, recent solutions suffer from poor scalability, computational cost, and slow response. Thus, this article proposes medical-edge-blockchain (MEdge-Chain), a holistic framework that exploits the integration of edge computing and blockchain-based technologies to process large amounts of medical data. Specifically, the proposed framework describes a healthcare system that aims to aggregate diverse health entities in a unique national healthcare system by enabling swift, secure exchange, and storage of medical data. Moreover, we design an automated patients monitoring scheme, at the edge, which enables the remote monitoring and efficient discovery of critical medical events. Then, we integrate this scheme with a blockchain architecture to optimize medical data exchanging between diverse entities. Furthermore, we develop a blockchain-based optimization model that aims to optimize the latency and computational cost of medical data exchange between different health entities, hence providing effective and secure healthcare services. Finally, we show the effectiveness of our system in adapting to different critical events, while highlighting the benefits of the proposed intelligent health system.

Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer.

Simple SummaryDiscovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.