CATALOGIC 301C model - validation and improvement

Sniffing Out the Sustainable Future: The Renewability Revolution in Fragrance Chemistry

In this review, the impact of the transition from today's resource-wasting petrochemical economy towards a 100/100 renewable and biodegradable future is discussed with respect to the fragrance families: "citrus", "green", "fruity", "floral", "floriental", "oriental", "woody", "chypre" and "fougère". After benchmark data on ingredients usage, definitions on biodegradation and sustainability are given. Celebrating the 150th anniversary of synthetic vanillin, its historic synthesis from renewable starting materials serves as introduction. In the grand scheme of things, citrus scents upcycled from the beverages industry, are already an ideal case for 100/100 with new opportunities for artificial essential oils. In the fruity domain, transparent and lactonic ingredients are available in a sustainable manner. However, in the domain of green odorants, there is a lack of green chemistry for important key materials. In the floral family, renewability is more critical than biodegradability, but cost is an issue. Thanks to Ambrox and maltol, florientals and orientals will persist, while woody notes severely lack an Iso E Super replacer. In the chypre genre, patchouli became the new moss, but more musks are increasingly in demand. With their high percentage of linalool and dihydromyrcenol, the construction of fougères could well become a precedent for other families, despite challenges in vetiver and salicylates. Still, the challenges exemplified here create immense opportunities for new perfumery materials.

Optimal selection of learning data for highly accurate QSAR prediction of chemical biodegradability: a machine learning-based approach

Prior to the manufacture of new chemicals, regulations mandate a thorough review of the chemicals under risk management. This review involves evaluating their effects on the environment and human health. To assess these effects, a review report that conforms to the OECD Test Guidelines must be submitted to the regulatory body. One of the essential components of the report is an assessment of the biodegradability of chemicals in the environment. In addition to conventional methods, quantitative structure-activity relationship (QSAR) models have been developed to predict the properties of chemicals based on their structural features. Although a greater number of chemicals in the learning set may enhance the prediction accuracy, it may also lead to a decrease in accuracy due to the mixing of different structural features and properties of the chemicals. To improve the prediction performance, it is recommended to use only the appropriate data for biodegradability prediction as a training set. In this study, we propose a novel approach for the optimal selection of training set that enables a highly accurate prediction of the biodegradability of chemicals by QSAR. Our findings indicate that the proposed method effectively reduces the root mean squared error and improves the prediction accuracy.

Emerging frontiers in microbe-mediated pesticide remediation: Unveiling role of omics and In silico approaches in engineered environment

The overuse of pesticides for augmenting agriculture productivity always comes at the cost of environment, biodiversity, and human health and has put the land, water, and environmental footprints under severe threat throughout the globe. Underpinning and maximizing the microbiome functions in pesticide-contaminated environments has become a prerequisite for a sustainable environment and resilient agriculture. It is imperative to elucidate the metabolic network of the microbial communities and environmental variables at the contaminated site to predict the best strategy for remediation and soil microbe-pesticide interactions. High throughput next-generation sequencing and in silico analysis allow us to identify and discern the members and characteristics of core microbiomes at the contaminated site. Integration of modern high throughput multi-omics investigations and informatics pipelines provide novel approaches and pathways to capitalize on the core microbiomes for enhancing environmental functioning and mitigation. The role of eco-genomics tools in visualising the microbial network, taxonomy, functional potential, and environmental variables in contaminated habitats is discussed in this review. The integrated role of the potential microbe identification as individual or consortia, mechanistic approach for pesticide degradation, identification of responsible enzymes/genes, and in silico approach is emphasized for the prospects of the area.

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.

The ever-expanding limits of enzyme catalysis and biodegradation: polyaromatic, polychlorinated, polyfluorinated, and polymeric compounds

Biodegradation is simply the metabolism of anthropogenic, or otherwise unwanted, chemicals in our environment, typically by microorganisms. The metabolism of compounds commonly found in living things is limited to several thousand metabolites whereas ∼100 million chemical substances have been devised by chemical synthesis, and ∼100 000 are used commercially. Since most of those compounds are not natively found in living things, and some are toxic or carcinogenic, the question arises as to whether there is some organism somewhere with the enzymes that can biodegrade them. Repeatedly, anthropogenic chemicals have been denoted 'non-biodegradable,' only to find they are reactive with one or more enzyme(s). Enzyme reactivity has been organized into categories of functional group transformations. The discovery of new functional group transformations has continually expanded our knowledge of enzymes and biodegradation. This expansion of new-chemical biodegradation is driven by the evolution and spread of newly evolved enzymes. This review describes the biodegradation of widespread commercial chemicals with a focus on four classes: polyaromatic, polychlorinated, polyfluorinated, and polymeric compounds. Polyaromatic hydrocarbons include some of the most carcinogenic compounds known. Polychlorinated compounds include polychlorinated biphenyls (PCBs) and many pesticides of the twentieth century. Polyfluorinated compounds are a major focus of bioremediation efforts today. Polymers are clogging landfills, killing aquatic species in the oceans and increasingly found in our bodies. All of these classes of compounds, each thought at one time to be non-biodegradable, have been shown to react with natural enzymes. The known limits of enzyme catalysis, and hence biodegradation, are continuing to expand.