Chembench: A Publicly Accessible, Integrated Cheminformatics Portal

The enormous increase in the amount of publicly available chemical genomics data and the growing emphasis on data sharing and open science mandates that cheminformaticians also make their models publicly available for broad use by the scientific community. Chembench is one of the first publicly accessible, integrated cheminformatics Web portals. It has been extensively used by researchers from different fields for curation, visualization, analysis, and modeling of chemogenomics data. Since its launch in 2008, Chembench has been accessed more than 1 million times by more than 5000 users from a total of 98 countries. We report on the recent updates and improvements that increase the simplicity of use, computational efficiency, accuracy, and accessibility of a broad range of tools and services for computer-assisted drug design and computational toxicology available on Chembench. Chembench remains freely accessible at https://chembench.mml.unc.ed

ПРОГРАММА «XCHEM» - ИСПОЛЬЗОВАНИЕ ФРАГМЕНТОВ ХИМИЧЕСКОЙ СТРУКТУРЫ ДЛЯ ПОИСКА И МОДЕЛИРОВАНИЯ ХИМИЧЕСКИХ И БИОЛОГИЧЕСКИХ СВОЙСТВ // Ученые записки КФУ. Естественные науки 2009 N1

Разработан программный пакет для анализа, моделирования и визуализации больших коллекций химических соединений. Помимо стандартных алгоритмов, данный набор содержит модуль для создания произвольно большого набора структурных фрагментов, конфигурация которых контролируется пользователем, а также для последующего использования этих фрагментов в качестве дескрипторов при моделировании химических и биологических свойств. На основе таких фрагментов был реализован поиск структурного сродства по коллекциям антибиотиков (~500 соединений) и биоцидных веществ (~250 соединений), собранным из литературных источников

Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results

Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R2=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q2ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential

Prediction of human population responses to toxic compounds by a collaborative competition

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000-Genomes Project. The challenge participants developed algorithms to predict inter-individual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against a blinded experimental dataset. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson’s r<0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r<0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal

Quantitative Structure - Skin permeability Relationships

This paper reviews in silico models currently available for the prediction of skin permeability with the main focus on the quantitative structure-permeability relationship (QSPR) models. A comprehensive analysis of the main achievements in the field in the last decade is provided. In addition, the mechanistic models are discussed and comparative studies that analyse different models are discussed

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using random forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers were 71–88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the ScoreCard database of possible skin or sense organ toxicants as primary candidates for experimental validation

Predicting Adverse Drug Effects from Literature Assertions that Link Drugs to Targets and Targets to Effects

Adverse drug effects (ADEs) are a major reason for drug candidate failure in clinical trials; thus, it is critical to predict possible ADEs in the early stages of drug discovery. In this study, cheminformatics, bioinformatics, and data mining approaches were employed to integrate and analyze publicly-available pharmacological and clinical data with the goal of inferring novel associations between drugs, targets, and ADEs. A new database was created that integrated experimental drug-target binding data and known associations between drugs (7448 unique instances), targets (1280), and ADEs (4492) expressed as assertions found in the literature. Unreported associations between drugs, targets, and ADEs were inferred, and inferences were prioritized as testable hypotheses. As a proof of concept, an association was identified between paroxetine and thrombocytopenic purpura using a focused subset of ~47K top-ranked inferences published prior to the first reports confirming this association in 2013. Given the increasing costs of bringing new drug entities to market, there is a strong need for cost-effective methods of identifying potential adverse effects of a drug candidate early on in the development process. The workflow presented here, based on free-access databases and an association-based inference scheme, has provided chemical-ADE inferences that have been validated post-hoc in literature case reports. With refinement of prioritization schemes for the generated chemical-ADE inferences, this workflow may provide an effective computational method for the early detection of drug candidate ADEs.Doctor of Pharmac

Metody eksploracji baz danych w poszukiwaniu nowych reguł projektowania leków

The pharmaceutical industry has been one of the most lucrative business areas of all time. However, faced with numerous problems in recent years, it appears to be declining. Despite the use of the latest technology, there has been no significant improvement which would result in the implementation of new, better, and more innovative pharmaceuticals. The aim of this study was to optimize the database exploration and analysis methods in terms of determining trends in pharma R&D and to propose strategies to facilitate the process of drug development. This study shows the synergy between the numerous research methods in several scientific fields, such as chemistry, computer science, economics and pharmacy. Thus, the calculated chemical descriptors and economic parameters were used in statistical analysis. Moreover, comparative analysis of trends and developments, both in science and the pharmaceutical industry, has been presented. Thus, an effort was made to explain the role and impact of the market for the trends in pharma which could encourage changes in the drug structure. A population of FDA drugs were collected, characterized by drug-like and drug-age properties and additional implementation of the fragmentation algorithms helped to understand the topology of the investigated drug population. In summary, the analysis revealed many interesting trends in drug development in recent years. The study shed new light on the known methods for the design of active compounds