Multi-Objective Genetic Algorithm (MOGA) As a Feature Selecting Strategy in the Development of Ionic Liquids’ Quantitative Toxicity–Toxicity Relationship Models

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

Spatial quantification method of grassland utilization intensity on the Qinghai-Tibetan Plateau: A case study on the Selinco basin

Existing methods for spatial quantification of grassland utilization intensity cannot meet the demand for accurate detection of the spatial distribution of grassland utilization intensity in the Qinghai-Tibetan Plateau with high spatial resolution. In this paper, a method based on remote-sensing observations and simulations of grassland growth dynamics is proposed. The grassland enhanced vegetation index (EVI) time-series curve during the growing season characterizes the growth of grassland in the corresponding pixel; The deviation between the observed and potential EVI curves indicates the disturbance on grassland growth imposed by human activities, and it can characterize the grassland utilization intensity during the growing season. Based on the main idea described above, absolute and relative disturbances are calculated and used as quantitative indicators of grassland utilization intensity defined from different perspectives. Livestock amount at the pixel scale is obtained by pixel-by-pixel calculations based on the function relationship at the township scale between absolute disturbance and livestock density, which is specific quantitative indicator that considers the mode of grassland utilization. In simulating the potential EVI of grassland, the lag and accumulation effects of meteorological factors are investigated at the daily scale using a multi-objective genetic algorithm. Further, the nonlinear functions between multiple environmental factors (e.g., grassland type, topography, soil, meteorology) and the grassland EVI are established using an error back-propagation feedforward artificial neural network (ANN-BP) with parameter optimization. Finally, the potential EVIs of all grassland pixels are simulated on the basis of this model. The method is applied to the Selinco basin on the Qinghai-Tibetan Plateau and validated by examining the spatial consistency of the results with township-scale livestock density and grazing pressure. The final results indicate that the proposed method can accurately detect the spatial distribution of grassland utilization intensity which is appliable in the similar regions.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.

AweGNN: Auto-parametrized weighted element-specific graph neural networks for molecules

While automated feature extraction has had tremendous success in many deep learning algorithms for image analysis and natural language processing, it does not work well for data involving complex internal structures, such as molecules. Data representations via advanced mathematics, including algebraic topology, differential geometry, and graph theory, have demonstrated superiority in a variety of biomolecular applications, however, their performance is often dependent on manual parametrization. This work introduces the auto-parametrized weighted element-specific graph neural network, dubbed AweGNN, to overcome the obstacle of this tedious parametrization process while also being a suitable technique for automated feature extraction on these internally complex biomolecular data sets. The AweGNN is a neural network model based on geometric-graph features of element-pair interactions, with its graph parameters being updated throughout the training, which results in what we call a network-enabled automatic representation (NEAR). To enhance the predictions with small data sets, we construct multi-task (MT) AweGNN models in addition to single-task (ST) AweGNN models. The proposed methods are applied to various benchmark data sets, including four data sets for quantitative toxicity analysis and another data set for solvation prediction. Extensive numerical tests show that AweGNN models can achieve state-of-the-art performance in molecular property predictions.

Ionic Liquids Toxicity-Benefits and Threats

Ionic liquids (ILs) are solvents with salt structures. Typically, they contain organic cations (ammonium, imidazolium, pyridinium, piperidinium or pyrrolidinium), and halogen, fluorinated or organic anions. While ILs are considered to be environmentally-friendly compounds, only a few reasons support this claim. This is because of high thermal stability, and negligible pressure at room temperature which makes them non-volatile, therefore preventing the release of ILs into the atmosphere. The expansion of the range of applications of ILs in many chemical industry fields has led to a growing threat of contamination of the aquatic and terrestrial environments by these compounds. As the possibility of the release of ILs into the environment s grow systematically, there is an increasing and urgent obligation to determine their toxic and antimicrobial influence on the environment. Many bioassays were carried out to evaluate the (eco)toxicity and biodegradability of ILs. Most of them have questioned their "green" features as ILs turned out to be toxic towards organisms from varied trophic levels. Therefore, there is a need for a new biodegradable, less toxic "greener" ILs. This review presents the potential risks to the environment linked to the application of ILs. These are the following: cytotoxicity evaluated by the use of human cells, toxicity manifesting in aqueous and terrestrial environments. The studies proving the relation between structures versus toxicity for ILs with special emphasis on directions suitable for designing safer ILs synthesized from renewable sources are also presented. The representants of a new generation of easily biodegradable ILs derivatives of amino acids, sugars, choline, and bicyclic monoterpene moiety are collected. Some benefits of using ILs in medicine, agriculture, and the bio-processing industry are also presented.

MaNGA: a novel multi-niche multi-objective genetic algorithm for QSAR modelling

SUMMARY

Quantitative structure-activity relationship (QSAR) modelling is currently used in multiple fields to relate structural properties of compounds to their biological activities. This technique is also used for drug design purposes with the aim of predicting parameters that determine drug behaviour. To this end, a sophisticated process, involving various analytical steps concatenated in series, is employed to identify and fine-tune the optimal set of predictors from a large dataset of molecular descriptors (MDs). The search of the optimal model requires to optimize multiple objectives at the same time, as the aim is to obtain the minimal set of features that maximizes the goodness of fit and the applicability domain (AD). Hence, a multi-objective optimization strategy, improving multiple parameters in parallel, can be applied. Here we propose a new multi-niche multi-objective genetic algorithm that simultaneously enables stable feature selection as well as obtaining robust and validated regression models with maximized AD. We benchmarked our method on two simulated datasets. Moreover, we analyzed an aquatic acute toxicity dataset and compared the performances of single- and multi-objective fitness functions on different regression models. Our results show that our multi-objective algorithm is a valid alternative to classical QSAR modelling strategy, for continuous response values, since it automatically finds the model with the best compromise between statistical robustness, predictive performance, widest AD, and the smallest number of MDs.

AVAILABILITY AND IMPLEMENTATION

The python implementation of MaNGA is available at https://github.com/Greco-Lab/MaNGA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Overview on the Toxicological Properties of Ionic Liquids toward Microorganisms

Ionic liquids (ILs), a class of materials with unique physicochemical properties, have been used extensively in the fields of chemical engineering, biotechnology, material sciences, pharmaceutics, and many others. Because ILs are very polar by nature, they can migrate into the environment with the possibility of inclusion in the food chain and bioaccumulation in living organisms. However, the chemical natures of ILs are not quintessentially biocompatible. Therefore, the practical uses of ILs must be preceded by suitable toxicological assessments. Among different methods, the use of microorganisms to evaluate IL toxicity provides many advantages including short generation time, rapid growth, and environmental and industrial relevance. This article reviews the recent research progress on the toxicological properties of ILs toward microorganisms and highlights the computational prediction of various toxicity models.

All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration IC50s for 8558 Novartis Assays

Profile-quantitative structure-activity relationship (pQSAR) is a massively multitask, two-step machine learning method with unprecedented scope, accuracy, and applicability domain. In step one, a "profile" of conventional single-assay random forest regression models are trained on a very large number of biochemical and cellular pIC₅₀ assays using Morgan 2 substructural fingerprints as compound descriptors. In step two, a panel of partial least squares (PLS) models are built using the profile of pIC₅₀ predictions from those random forest regression models as compound descriptors (hence the name). Previously described for a panel of 728 biochemical and cellular kinase assays, we have now built an enormous pQSAR from 11 805 diverse Novartis (NVS) IC₅₀ and EC₅₀ assays. This large number of assays, and hence of compound descriptors for PLS, dictated reducing the profile by only including random forest regression models whose predictions correlate with the assay being modeled. The random forest regression and pQSAR models were evaluated with our "realistically novel" held-out test set, whose median average similarity to the nearest training set member across the 11 805 assays was only 0.34, comparable to the novelty of compounds actually selected from virtual screens. For the 11 805 single-assay random forest regression models, the median correlation of prediction with the experiment was only r_ext² = 0.05, virtually random, and only 8% of the models achieved our standard success threshold of r_ext² = 0.30. For pQSAR, the median correlation was r_ext² = 0.53, comparable to four-concentration experimental IC₅₀s, and 72% of the models met our r_ext² > 0.30 standard, totaling 8558 successful models. The successful models included assays from all of the 51 annotated target subclasses, as well as 4196 phenotypic assays, indicating that pQSAR can be applied to virtually any disease area. Every month, all models are updated to include new measurements, and predictions are made for 5.5 million NVS compounds, totaling 50 billion predictions. Common uses have included virtual screening, selectivity design, toxicity and promiscuity prediction, mechanism-of-action prediction, and others. Several such actual applications are described.