Data analysis in chemistry and bio-medical sciences

EditorialMinisterio de Economía y Competitividad; CTQ2013-41229-PMinisterio de Economía y Competitividad; CTQ2013-41229-P/BQUMinisterio de Economía y Competitividad; CTQ2016-74881-PPaís Vasco. Gobierno; IT1045-1

Thermal mapping as a valuable tool for road weather forecast and winter road maintenance: an example from the Italian Alps

During the winter period ice is likely to form on roads, making pavement surfaces slippery and increasing accident risk. Road surface temperature (RST) is one of the most important parameters in ice formation. The LIFE+ “CLEANROADS” project aims to forecast RSTs in advance in order to support road maintenance services in the timely and effective preparation of preventive anti-icing measures. This support is provided through a novel MDSS (Maintenance Decision Support System). The final goal of the project is to quantitatively demonstrate that the implemented MDSS is capable to minimize the consumption of chemical anti-icing reagents (e.g. sodium chloride) and the associated environmental (water and air) impact while maintaining the current high levels of road safety. In the CLEAN-ROADS system RSTs have been forecast by applying the numerical model METRo (Model of the Environment and Temperature of Roads) to a network of RWIS (Road Weather Information System) stations installed on a test route in the Adige Valley (Italy). This forecast is however local and does not take into account typical peculiarities along road network, such as the presence of road sections that are particularly prone to ice formation. Thermal mapping, i.e. the acquisition of mobile RST measurements through infrared thermometry, permits to (i) identify and map those sections, and (ii) extend the forecast from a RWIS station to adjacent areas. The processing of thermal mapping signals is however challenging because of random variations in the road surface emissivity. To overcome this we have acquired several thermal mapping traces along the test route during winter seasons 2014-2015 and 2015-2016. We have then defined a “characteristic” thermal fingerprint as a function of all its historical thermal mapping signals, and used it to spatialize local METRo forecasts. Preliminary results suggest the high potential of such a technique for winter road applications

A New Similarity/Diversity Measure for the Characterization of DNA Sequences

In this paper, a new similarity/diversity measure is proposed as a new approach to the analysis of sequential data, where useful information can be also obtained by the ordering relationships between the sequence elements. This methodology has been applied to characterize DNA sequences, evaluating their similarity/diversity. The new proposed distance (weighted standardized Hasse distance) is evaluated between pairs of Hasse matrices derived from the classical partial ordering rules. It can be naturally standardized, thus allowing the interpretation of these distances as absolute values (e.g. percentage) and deriving simple similarity and correlation indices. DNA sequences taken from the first exons of the beta-globins for eight different species have been analyzed. Sensitivity analysis has been also performed, showing the high capability of this measure to take into account small modifications of the DNA sequences. Finally, a comparison with results obtained from literature is given

Publisher Correction: Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity

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A QSTR-based expert system to predict sweetness of molecules

This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.Instituto de Investigaciones Fisicoquímicas Teóricas y AplicadasFacultad de Ciencias Exacta

A QSTR-based expert system to predict sweetness of molecules

This work describes a novel approach based on advanced molecular similarity to predict the sweetness of chemicals. The proposed Quantitative Structure-Taste Relationship (QSTR) model is an expert system developed keeping in mind the five principles defined by the Organization for Economic Co-operation and Development (OECD) for the validation of (Q)SARs. The 649 sweet and non-sweet molecules were described by both conformation-independent extended-connectivity fingerprints (ECFPs) and molecular descriptors. In particular, the molecular similarity in the ECFPs space showed a clear association with molecular taste and it was exploited for model development. Molecules laying in the subspaces where the taste assignation was more difficult were modeled trough a consensus between linear and local approaches (Partial Least Squares-Discriminant Analysis and N-nearest-neighbor classifier). The expert system, which was thoroughly validated through a Monte Carlo procedure and an external set, gave satisfactory results in comparison with the state-of-the-art models. Moreover, the QSTR model can be leveraged into a greater understanding of the relationship between molecular structure and sweetness, and into the design of novel sweeteners.Instituto de Investigaciones Fisicoquímicas Teóricas y AplicadasFacultad de Ciencias Exacta

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu

QSAR Modeling: Where Have You Been? Where Are You Going To?

Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495