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Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology.

Ion channels are membrane proteins involved in almost all physiological processes, including neurotransmission, muscle contraction, pace-making activity, secretion, electrolyte and water balance, immune response, and cell proliferation. Due to their broad distribution in human body and physiological roles, ion channels are attractive targets for drug discovery and safety pharmacology. Over the years ion channels have been associated to many genetic diseases ("channelopathies"). For most of these diseases the therapy is mainly empirical and symptomatic, often limited by lack of efficacy and tolerability for a number of patients. The search for the development of new and more specific therapeutic approaches is therefore strongly pursued. At the same time acquired channelopathies or dangerous side effects (such as proarrhythmic risk) can develop as a consequence of drugs unexpectedly targeting ion channels. Several noncardiovascular drugs are known to block cardiac ion channels, leading to potentially fatal delayed ventricular repolarization. Thus, the search of reliable preclinical cardiac safety testing in early stage of drug discovery is mandatory. To fulfill these needs, both ion channels drug discovery and toxicology strategies are evolving toward comprehensive research approaches integrating ad hoc designed in silico predictions and experimental studies for a more reliable and quick translation of results to the clinic side.Here we discuss two examples of how the combination of in silico methods and patch clamp experiments can help addressing drug discovery and safety issues regarding ion channels.

Predictive toxicology is a new emerging multifaceted research field aimed at protecting human health and environment from risks posed by chemicals. Such issue is of extreme public relevance and requires a multidisciplinary approach where the experience in medicinal chemistry is of utmost importance. Herein, we will survey some basic recommendations to gather good data and then will review three recent case studies to show how strategies of ligand- and structure-based molecular design, widely applied in medicinal chemistry, can be adapted to meet the more restrictive scientific and regulatory goals of predictive toxicology. In particular, we will report: Docking-based classification models to predict the estrogenic potentials of chemicals. Predicting the bioconcentration factor using biokinetics descriptors. Modeling oral sub-chronic toxicity using a customized k-nearest neighbors (k-NN) approach.