QSAR: Using the Past to Study the Present.

Quantitative structure-activity relationships (QSAR) is a method for predicting the physical and biological properties of small molecules; it is in use in industry and public services. However, as any scientific method, it is challenged by more and more requests, especially considering its possible role in assessing the safety of new chemicals. To answer the question whether QSAR, by exploiting available knowledge, can build new knowledge, the chapter reviews QSAR methods in search of a QSAR epistemology.

QSAR Methods.

This chapter introduces the basis of computational chemistry and discusses how computational methods have been extended from physical to biological properties, and toxicology in particular, modeling. Since about three decades, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Animal and wet experiments, aimed at providing a standardized result about a biological property, can be mimicked by modeling methods, globally called in silico methods, all characterized by deducing properties starting from the chemical structures.

Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors.

Endocrine-disrupting chemicals (EDCs) can inadvertently interact with 12 classic nuclear receptors (NRs) that disrupt the endocrine system and cause adverse effects. There is no widely accepted understanding about what structural features make thousands of EDCs able to activate different NRs as well as how these structural features exert their functions and induce different outcomes at the cellular level. This paper applies the hierarchical characteristic fragment methodology and high-throughput screening molecular docking to comprehensively explore the structural and functional features of EDCs for the 12 NRs based on more than 7000 chemicals from curated datasets.

QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction.

Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models can be constructed from 2D molecular graphs without computing chemical descriptors. Two graph convolutional neural network-based models are presented with and without a Bayesian estimation of the prediction uncertainty.

Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor.

Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal endocrine system function, causing severe symptoms. Limited studies queried the EDC mechanisms, focusing on limited chemicals or a set of structurally similar compounds. It remained uncertain how hundreds of diverse EDCs could bind to ERα and AR and cause distinct functional consequences.

Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy.

In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals.

QSAR: What Else?

QSAR (quantitative structure-activity relationship) is a method for predicting the physical and biological properties of small molecules; it is today in large use in companies and public services. However, as any scientific method, it is nowadays challenged by more and more requests, especially considering its possible role in assessing the safety of new chemicals. Posing the question whether QSAR is a way not only to exploit available knowledge but also to build new knowledge, we shortly review QSAR history, thus searching for a QSAR epistemology.

QSAR Methods.

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods.

New clues on carcinogenicity-related substructures derived from mining two large datasets of chemical compounds.

In this study, new molecular fragments associated with genotoxic and nongenotoxic carcinogens are introduced to estimate the carcinogenic potential of compounds. Two rule-based carcinogenesis models were developed with the aid of SARpy: model R (from rodents' experimental data) and model E (from human carcinogenicity data). Structural alert extraction method of SARpy uses a completely automated and unbiased manner with statistical significance.

Processing of surface EMG through pattern recognition techniques aimed at classifying shoulder joint movements.

Artificial arms for shoulder disarticulation need a high number of degrees of freedom to be controlled. In order to control a prosthetic shoulder joint, an intention detection system based on surface electromyography (sEMG) pattern recognition methods was proposed and experimentally investigated. Signals from eight trunk muscles that are generally preserved after shoulder disarticulation were recorded from a group of eight normal subjects in nine shoulder positions.

A new in silico classification model for ready biodegradability, based on molecular fragments.

Regulations such as the European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) often require chemicals to be evaluated for ready biodegradability, to assess the potential risk for environmental and human health. Because not all chemicals can be tested, there is an increasing demand for tools for quick and inexpensive biodegradability screening, such as computer-based (in silico) theoretical models. We developed an in silico model starting from a dataset of 728 chemicals with ready biodegradability data (MITI-test Ministry of International Trade and Industry).

OCWLGI descriptors: theory and praxis.

The aim of this review is description of the logic and evolution of optimal descriptors OCWLGI calculated with the molecular graph and the demonstration of their ability as tools for the modeling of biological and physicochemical parameters of chemical compounds. The ability of optimal descriptors calculated with hydrogen suppressed graph (HSG), hydrogen filled graph (HFG) and graph of atomic orbitals (GAO) is demonstrated as a collection of quantitative structure-property relationships (QSPR) and quantitative structure-activity relationships (QSAR) for properties and endpoints available from the literature. The Monte Carlo method optimization of the correlation weights of local and global invariants (OCWLGI) of molecular graphs is used as the principle for building up descriptors which are discussed in this article.

QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet.

QSAR models for inhibitors of physiological impact of Escherichia coli that leads to diarrhea.

Quantitative structure - activity relationships (QSARs) developed to evaluate percentage of inhibition of STa-stimulated (Escherichia coli) cGMP accumulation in T84 cells are calculated by the Monte Carlo method. This endpoint represents a measure of biological activity of a substance against diarrhea. Statistical quality of the developed models is quite good.

CORAL: binary classifications (active/inactive) for Liver-Related Adverse Effects of Drugs.

Classification data related to the Liver-Related Adverse Effects of Drugs have been studied with the CORAL software (http://www.insilico.eu/coral).

CORAL: QSPR model of water solubility based on local and global SMILES attributes.

Water solubility is an important characteristic of a chemical in many aspects. However experimental definition of the endpoint for all substances is impossible. In this study quantitative structure-property relationships (QSPRs) for negative logarithm of water solubility-logS (mol L(-1)) are built up for five random splits into the sub-training set (≈55%), the calibration set (≈25%), and the test set (≈20%).

Novel application of the CORAL software to model cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli.

Convenient to apply and available on the Internet software CORAL (http://www.insilico.eu/CORAL) has been used to build up quantitative structure-activity relationships (QSAR) for prediction of cytotoxicity of metal oxide nanoparticles to bacteria Escherichia coli (minus logarithm of concentration for 50% effect pEC50).

Calculation of molecular features with apparent impact on both activity of mutagens and activity of anticancer agents.

The analysis of the influence of molecular features which can be extracted from the simplified molecular input line entry system (SMILES) and involved in the process of the building up of a series of QSAR models (with different splits into training and test sets) by means of the CORAL software for mutagenicity and anticancer activity has been performed. The presence of nitrogen (sp3) is favorable for decrease of the both endpoints; the presence of only one ring is also promotor for decrease of the both endpoints; however the presence of two or three rings is favorable for increase of mutagenicity and decrease of anticancer activity. These findings provide useful criteria for further experimental and computational studies in the search for new anticancer agents.

QSAR models for toxicity of organic substances to Daphnia magna built up by using the CORAL freeware.

CORAL (CORrelations And Logic, http://www.insilico.eu/coral/) is a freeware available on the Internet.

SMILES-based QSAR approaches for carcinogenicity and anticancer activity: comparison of correlation weights for identical SMILES attributes.

CORAL software (http://www.insilico.eu/coral/) has been used for modeling of carcinogenicity (logTD50) of 401 compounds, and anticancer activity (-logIC50) of 100 compounds, on the basis of quantitative structure activity relationships (QSAR).

The acceptance of in silico models for REACH: Requirements, barriers, and perspectives.

In silico models have prompted considerable interest and debate because of their potential value in predicting the properties of chemical substances for regulatory purposes. The European REACH legislation promotes innovation and encourages the use of alternative methods, but in practice the use of in silico models is still very limited. There are many stakeholders influencing the regulatory trajectory of quantitative structure-activity relationships (QSAR) models, including regulators, industry, model developers and consultants.

Medical robotics.

Information and communication technology (ICT) and mechatronics play a basic role in medical robotics and computer-aided therapy. In the last three decades, in fact, ICT technology has strongly entered the health-care field, bringing in new techniques to support therapy and rehabilitation. In this frame, medical robotics is an expansion of the service and professional robotics as well as other technologies, as surgical navigation has been introduced especially in minimally invasive surgery.

Simplified molecular input-line entry system and International Chemical Identifier in the QSAR analysis of styrylquinoline derivatives as HIV-1 integrase inhibitors.

The simplified molecular input-line entry system (SMILES) and IUPAC International Chemical Identifier (InChI) were examined as representations of the molecular structure for quantitative structure-activity relationships (QSAR), which can be used to predict the inhibitory activity of styrylquinoline derivatives against the human immunodeficiency virus type 1 (HIV-1). Optimal SMILES-based descriptors give a best model with n = 26, r(2) = 0.6330, q(2) = 0.

An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts.

Background: Mutagenicity is the capability of a substance to cause genetic mutations. This property is of high public concern because it has a close relationship with carcinogenicity and potentially with reproductive toxicity. Experimentally, mutagenicity can be assessed by the Ames test on Salmonella with an estimated experimental reproducibility of 85%; this intrinsic limitation of the in vitro test, along with the need for faster and cheaper alternatives, opens the road to other types of assessment methods, such as in silico structure-activity prediction models.