The increasing availability of extensive collections of chemical compounds associated with experimental data provides an opportunity to build predictive quantitative structure-activity relationship (QSAR) models using machine learning (ML) algorithms. These models can promote data-driven decisions and have the potential to speed up the drug discovery process and reduce their failure rates. However, many essential aspects of data preparation and modeling are not available in any standalone program. Here, we developed an automated framework for the curation of chemogenomics data and to develop QSAR models for virtual screening using the open-source KoNstanz Information MinEr (KNIME) program. The workflow includes four modules: (i) dataset preparation and curation; (ii) chemical space analysis and structure-activity relationships (SAR) rules; (iii) modeling; and (iv) virtual screening (VS). As case studies, we applied these workflows to four datasets associated with different endpoints. The implemented protocol can efficiently curate chemical and biological data in public databases and generates robust QSAR models. We provide scientists a simple and guided cheminformatics workbench following the best practices widely accepted by the community, in which scientists can adapt to solve their research problems. The workflows are freely available for download at GitHub and LabMol web portals.

Computational tools are recognized to provide high-quality predictions for the assessment of chemical toxicity. In the recent years, mobile devices have become ubiquitous, allowing for the development of innovative and useful models implemented as chemical software applications. Here, we will briefly discuss this recent uptick in the development of web-based and mobile applications for chemical problems, focusing on best practices, development, usage and interpretation. As an example, we also describe two innovative apps (Pred-hERG and Pred-Skin) for chemical toxicity prediction developed in our laboratory. These applications are based on predictive quantitative structure-activity relationships (QSAR) models developed using the largest publicly available datasets of structurally diverse compounds. The developed tools ensure both highly accurate predictions and easy interpretation of the models, allowing users to discriminate potential toxicants and to purpose structural modifications to design safer chemicals.

Cheminformatics is an interdisciplinary field between chemistry and informatics, which has evolved considerably since its inception in the 1960s. Initially, the cheminformatics community dealt primarily with practical and technical aspects of chemical structure representation, manipulation, and processing, while modern research explores a new role: the exploration and interpretation of large chemical databases and the discovery of new compounds with desired activity and safety profiles. Despite the recent release of several hallmark reviews addressing methods and application of cheminformatics written in Portuguese, so far there are no scientific articles presenting cheminformatics research to the Brazilian scientific community yet. To address this gap, we aim to introduce the field of cheminformatics to both students and researchers in a simple and didactic way by narrating important historical facts and contextualizing information within the scope of various applications.

Os parabenos são conservantes antimicrobianos amplamente utilizados na indústria farmacêutica, cosmética e alimentícia. A cadeia alquila ligada ao grupo éster define algumas características físico-químicas destes compostos, incluindo coeficiente de partição e propriedades redox. A voltametria e estudos computacionais foram realizados a fim de avaliar o comportamento redox desses compostos fenólicos e de outros análogos. Uma forte correlação entre os potenciais redox e os efeitos indutivos dos substituintes químicos dos parabenos foi observada. Usando voltametria cíclica e eletrodo de trabalho de carbono vítreo, foi observado um único pico de natureza irreversível, em torno de 0,8 V para metilparabeno (MP), etilparabeno (EP), propilparabeno (PP), butilparabeno (BP), benzilparabeno (BzP) e análogos fenólicos p-substituídos. O efeito indutivo eletrodoador de grupos alquila foi demonstrado pela mudança do potencial de oxidação para valores menos positivos conforme o aumento do número de carbonos e, portanto, o processo de oxidação dos parabenos (e outros análogos fenólicos) às formas quinonoídicas mostrou forte dependência do padrão de substituição.

Parabens are antimicrobial preservatives widely used in pharmaceutical, cosmetic and food industries. The alkyl chain connected to the ester group defines some important physicochemical characteristics of these compounds, including the partition coefficient and redox properties. The voltammetric and computational analyses were carried out in order to evaluate the redox behavior of these compounds and other phenolic analogues. A strong correlation between chemical substituents inductive effects of parabens with redox potentials was observed. Using cyclic voltammetry and glassy carbon working electrode, only one irreversible anodic peak was observed around 0.8 V for methylparaben (MP), ethylparaben (EP), propylparaben (PP), butylparaben (BP), benzylparaben (BzP) and p-substituted phenolic analogues. The electrodonating inductive effect of alkyl groups was demonstrated by the anodic oxidation potential shift to lower values as the carbon number increases and, therefore the parabens (and other phenolic analogues) oxidation processes to the quinonoidic forms showed great dependence on the substituent pattern.