OpenZika: An IBM World Community Grid Project to Accelerate Zika Virus Drug Discovery

<div><p>The Zika virus outbreak in the Americas has caused global concern. To help accelerate this fight against Zika, we launched the OpenZika project. OpenZika is an IBM World Community Grid Project that uses distributed computing on millions of computers and Android devices to run docking experiments, in order to dock tens of millions of drug-like compounds against crystal structures and homology models of Zika proteins (and other related flavivirus targets). This will enable the identification of new candidates that can then be tested in vitro, to advance the discovery and development of new antiviral drugs against the Zika virus. The docking data is being made openly accessible so that all members of the global research community can use it to further advance drug discovery studies against Zika and other related flaviviruses.</p></div

MEDICINAL CHEMISTRY PERSPECTIVES FOR THE 21ST CENTURY: CHALLENGES AND OPPROTUNITIES

<p></p><p>In the 21st century, medicinal chemists will face many challenges to improve the quality of life of populations. The challenges consist of emerging infectious (ex. bacterial, viral and parasite infections) and non-communicable diseases (ex. autoimmune, Alzheimer disease, Parkinson’s disease) that will require innovative technologies (ex. microfluidics, nanotechnology, biotechnology) to be fully understood and combated. In this work, we indicate trends, perspectives and opportunities related to drug discovery as well as highlight the tools and strategies that could be used in drug discovery of the 21st century.</p><p></p

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

DataSheet_1_Whole genome sequencing identifies novel mutations in malaria parasites resistant to artesunate (ATN) and to ATN + mefloquine combination.pdf

IntroductionThe global evolution of resistance to Artemisinin-based Combination Therapies (ACTs) by malaria parasites, will severely undermine our ability to control this devastating disease.MethodsHere, we have used whole genome sequencing to characterize the genetic variation in the experimentally evolved Plasmodium chabaudi parasite clone AS-ATNMF1, which is resistant to artesunate + mefloquine.Results and discussionFive novel single nucleotide polymorphisms (SNPs) were identified, one of which was a previously undescribed E738K mutation in a 26S proteasome subunit that was selected for under artesunate pressure (in AS-ATN) and retained in AS-ATNMF1. The wild type and mutated three-dimensional (3D) structure models and molecular dynamics simulations of the P. falciparum 26S proteasome subunit Rpn2 suggested that the E738K mutation could change the toroidal proteasome/cyclosome domain organization and change the recognition of ubiquitinated proteins. The mutation in the 26S proteasome subunit may therefore contribute to altering oxidation-dependent ubiquitination of the MDR-1 and/or K13 proteins and/or other targets, resulting in changes in protein turnover. In light of the alarming increase in resistance to artemisin derivatives and ACT partner drugs in natural parasite populations, our results shed new light on the biology of resistance and provide information on novel molecular markers of resistance that may be tested (and potentially validated) in the field.</p

Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals

Chemically induced skin sensitization is a complex immunological disease with a profound impact on quality of life and working ability. Despite some progress in developing alternative methods for assessing the skin sensitization potential of chemical substances, there is no in vitro test that correlates well with human data. Computational QSAR models provide a rapid screening approach and contribute valuable information for the assessment of chemical toxicity. We describe the development of a freely accessible web-based and mobile application for the identification of potential skin sensitizers. The application is based on previously developed binary QSAR models of skin sensitization potential from human (109 compounds) and murine local lymph node assay (LLNA, 515 compounds) data with good external correct classification rate (0.70–0.81 and 0.72–0.84, respectively). We also included a multiclass skin sensitization potency model based on LLNA data (accuracy ranging between 0.73 and 0.76). When a user evaluates a compound in the web app, the outputs are (i) binary predictions of human and murine skin sensitization potential; (ii) multiclass prediction of murine skin sensitization; and (iii) probability maps illustrating the predicted contribution of chemical fragments. The app is the first tool available that incorporates quantitative structure–activity relationship (QSAR) models based on human data as well as multiclass models for LLNA. The Pred-Skin web app version 1.0 is freely available for the web, iOS, and Android (in development) at the LabMol web portal (http://labmol.com.br/predskin/), in the Apple Store, and on Google Play, respectively. We will continuously update the app as new skin sensitization data and respective models become available

Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure–Activity Relationship Models

Multiple approaches to quantitative structure–activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal (https://chembench.mml.unc.edu/mudra<i>)</i>

Cheminformatics-driven discovery of hit compounds against Paracoccidioides spp.

suppl material</p

Naive Bayes Skin Sensitization Model v. 1.1

KNIME workflow for prediction of human skin sensitization using a Naive Bayes model developed with human, animal, and non-animal data sources.<div><br></div><div>If you use this workflow in your research, please cite our paper:</div><div><br></div><div><div>Vinicius M. Alves, Stephen J. Capuzzi, Rodolpho C. Braga, Joyce V. B. Borba, Arthur C. Silva, Thomas Luechtefeld, Thomas Hartung, Carolina Horta Andrade, Eugene N. Muratov, and Alexander Tropsha. <i>ACS Sustainable Chemistry & Engineering </i><b>2018 </b><i>6</i> (3), 2845-2859. DOI: 10.1021/acssuschemeng.7b04220</div></div