tackling malaria

Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.publishersversionpublishe

QSAR models of human data can enrich or replace LLNA testing for human skin sensitization

An example of structural transformation of human skin sensitizers into various non-sensitizers based on interpretation of QSAR models

Alarms about structural alerts

Integrative approach for safety assessment of new chemicals by combining structural alerts and QSAR models

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace in vitro or in vivo assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving. Among the VS approaches, quantitative structure–activity relationship (QSAR) analysis is the most powerful method due to its high and fast throughput and good hit rate. As the first preliminary step of a QSAR model development, relevant chemogenomics data are collected from databases and the literature. Then, chemical descriptors are calculated on different levels of representation of molecular structure, ranging from 1D to nD, and then correlated with the biological property using machine learning techniques. Once developed and validated, QSAR models are applied to predict the biological property of novel compounds. Although the experimental testing of computational hits is not an inherent part of QSAR methodology, it is highly desired and should be performed as an ultimate validation of developed models. In this mini-review, we summarize and critically analyze the recent trends of QSAR-based VS in drug discovery and demonstrate successful applications in identifying perspective compounds with desired properties. Moreover, we provide some recommendations about the best practices for QSAR-based VS along with the future perspectives of this approach

QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.

Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents

QUIMIOINFORMÁTICA: UMA INTRODUÇÃO

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

Development of Web and Mobile Applications for Chemical Toxicity Prediction

<div><p>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.</p></div

Cyclic Voltammetry and Computational Chemistry Studies on the Evaluation of the Redox Behavior of Parabens and other Analogues

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

CHEMINFORMATICS: AN INTRODUCTION

<p></p><p>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.</p><p></p