Prediction of acute aquatic toxicity in Tetrahymena pyriformis--'Eco-Derek', a knowledge-based system approach

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models’ performance.

In Silico Resources of Drug-Likeness as a Mirror: What Are We Lacking in Pesticide-Likeness?

Unfavorable bioavailability is an important aspect underlying the failure of drug candidates. Computational approaches for evaluating drug-likeness can minimize these risks. Over the past decades, computational approaches for evaluating drug-likeness have sped up the process of drug development and were also quickly derived to pesticide-likeness. As a result of many critical differences between drugs and pesticides, many kinds of methods for drug-likeness cannot be used for pesticide-likeness. Therefore, it is crucial to comprehensively compare and analyze the differences between drug-likeness and pesticide-likeness, which may provide a basis for solving the problems encountered during the evaluation of pesticide-likeness. Here, we systematically collected the recent advances of drug-likeness and pesticide-likeness and compared their characteristics. We also evaluated the current lack of studies on pesticide-likeness, the molecular descriptors and parameters adopted, the pesticide-likeness model on pesticide target organisms, and comprehensive analysis tools. This work may guide researchers to use appropriate methods for developing pesticide-likeness models. It may also aid non-specialists to understand some important concepts in drug-likeness and pesticide-likeness.