Molecular docking is a powerful technique that helps uncover the structural and energetic bases of the interaction between macromolecules and substrates, endogenous and exogenous ligands, and inhibitors. Moreover, this technique plays a pivotal role in accelerating the screening of large libraries of compounds for drug development purposes. The need to promote community-driven drug development efforts, especially as far as neglected diseases are concerned, calls for user-friendly tools to allow non-expert users to exploit the full potential of molecular docking. Along this path, here is described the implementation of DockingApp, a freely available, extremely user-friendly, platform-independent application for performing docking simulations and virtual screening tasks using AutoDock Vina. DockingApp sports an intuitive graphical user interface which greatly facilitates both the input phase and the analysis of the results, which can be visualized in graphical form using the embedded JMol applet. The application comes with the DrugBank set of more than 1400 ready-to-dock, FDA-approved drugs, to facilitate virtual screening and drug repurposing initiatives. Furthermore, other databases of compounds such as ZINC, available also in AutoDock format, can be readily and easily plugged in.

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Supplementary data are available at Bioinformatics online.

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The application was developed in Java SE 6, with a Java Swing Graphic User Interface (GUI). The system can be run locally on any operating system (OS) equipped with a suitable Java Virtual Machine, and is available at the following URL: http://www.computationalbiology.it/software/ASSISTv1.zip.

Fipronil is a broad-spectrum pesticide widely used in agriculture, horticulture, and forestry. Because fipronil can cause a variety of toxic effects in animals and humans, its use is authorized as a pesticide in veterinary medicinal products for pets, but not for the treatment of livestock animals whose products are intended for consumption. Recently, however, the presence of fipronil residues has been detected in the eggs and meat of layer hens from farms located in different European countries. Given the relevance of fipronil toxicity for human health, it is important to gain information concerning its fate in the human body, including its binding mode to human serum albumin (HSA), the most abundant protein in plasma. Here, the inhibition of heme-Fe(III) binding to the fatty acid site 1 (FA1) of HSA by fipronil is reported. Docking simulations support functional data, indicating that the FA1 site is the preferential cleft for fipronil recognition by HSA. The affinity of fipronil for HSA (K = 1.9 × 10 M, at pH 7.3, and 20.0°C) may be relevant in vivo. Indeed, HSA could play a pivotal role in fipronil transport and scavenging, thus reducing the pesticide-free plasmatic levels, with consequent reduced systemic toxicity. In turn, fipronil binding to the FA1 site of HSA could impair the recognition of endogenous and exogenous molecules.

The application, developed in Java SE 7 with a Swing GUI embedding a JMol applet, can be run on any OS equipped with a suitable Java Virtual Machine (JVM), and is available at the following URL: http://www.computationalbiology.it/software/LIBRAv1.zip.

Motivation: Bringing a new drug to the market is expensive and time-consuming. To cut the costs and time, computer-aided drug design (CADD) approaches have been increasingly included in the drug discovery pipeline. However, despite traditional docking tools show a good conformational space sampling ability, they are still unable to produce accurate binding affinity predictions. This work presents a novel scoring function for molecular docking seamlessly integrated into DockingApp, a user-friendly graphical interface for AutoDock Vina. The proposed function is based on a random forest model and a selection of specific features to overcome the existing limits of Vina’s original scoring mechanism. A novel version of DockingApp, named DockingApp RF, has been developed to host the proposed scoring function and to automatize the rescoring procedure of the output of AutoDock Vina, even to nonexpert users. Results: By coupling intermolecular interaction, solvent accessible surface area features and Vina’s energy terms, DockingApp RF’s new scoring function is able to improve the binding affinity prediction of AutoDock Vina. Furthermore, comparison tests carried out on the CASF-2013 and CASF-2016 datasets demonstrate that DockingApp RF’s performance is comparable to other state-of-the-art machine-learning- and deep-learning-based scoring functions. The new scoring function thus represents a significant advancement in terms of the reliability and effectiveness of docking compared to AutoDock Vina’s scoring function. At the same time, the characteristics that made DockingApp appealing to a wide range of users are retained in this new version and have been complemented with additional features.