Denis Fourches

QSAR Modeling: Where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research

Molecular modelers and cheminformaticians typically analyze experimental data generated by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are always at the mercy of data providers who may inadvertently publish (partially) erroneous data. Thus, dataset curation is crucial for any cheminformatics analysis such as similarity searching, clustering, QSAR modeling, virtual screening, etc., especially nowadays when the availability of chemical datasets in public domain has skyrocketed in recent years. Despite the obvious importance of this preliminary step in the computational analysis of any dataset, there appears to be no commonly accepted guidance or set of procedures for chemical data curation. The main objective of this paper is to emphasize the need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation. Herein, we discuss several simple but important steps for cleaning chemical records in a database including the removal of a fraction of the data that cannot be appropriately handled by conventional cheminformatics techniques. Such steps include the removal of inorganic and organometallic compounds, counterions, salts and mixtures; structure validation; ring aromatization; normalization of specific chemotypes; curation of tautomeric forms; and the deletion of duplicates. To emphasize the importance of data curation as a mandatory step in data analysis, we discuss several case studies where chemical curation of the original “raw” database enabled the successful modeling study (specifically, QSAR analysis) or resulted in a significant improvement of models prediction accuracy. We also demonstrate that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds. We believe that good practices for curation of chemical records outlined in this paper will be of value to all scientists working in the fields of molecular modeling, cheminformatics, and QSAR studies.

Chemical basis of interactions between engineered nanoparticles and biological systems.

As defined by the European Commission, nanomaterial is a natural, incidental or manufactured material containing particles in an unbound state or as an aggregate or agglomerate in which ≥ 50% of the particles in the number size distribution have one or more external dimensions in the size range 1 to 100 nm. In specific cases and where warranted by concerns for the environment, health, safety or competition, the number size distribution threshold of 50% may be replaced with a threshold between 1 and 50%.1 Engineered nanomaterials (ENMs) refer to man-made nanomaterials. Materials in the nanometer range often possess unique physical, optical, electronic, and biological properties compared with larger particles, such as the strength of graphene,2 the electronic properties of carbon nanotubes (CNTs),3 the antibacterial activity of silver nanoparticles4 and the optical properties of quantum dots (QDs).5 The unique and advanced properties of ENMs have led to a rapid increase in their application. These applications include aerospace and airplanes, energy, architecture, chemicals and coatings, catalysts, environmental protection, computer memory, biomedicine and consumer products. Driven by these demands, the worldwide ENM production volume in 2016 is conservatively estimated in a market report by Future Markets to be 44,267 tons or ≥

Quantitative Nanostructure−Activity Relationship Modeling

5 billion.6 As the production and applications of ENMs rapidly expand, their environmental impacts and effects on human health are becoming increasingly significant.7 Due to their small sizes, ENMs are easily made airborne.8 However, no accurate method to quantitatively measure their concentration in air currently exists. A recently reported incident of severe pulmonary fibrosis caused by inhaled polymer nanoparticles in seven female workers obtained much attention.9 In addition to the release of ENM waste from industrial sites, a major release of ENMs to environmental water occurs due to home and personal use of appliances, cosmetics and personal products, such as shampoo and sunscreen.10 Airborne and aqueous ENMs pose immediate danger to the human respiratory and gastrointestinal systems. ENMs may enter other human organs after they are absorbed into the bloodstream through the gastrointestinal or respiratory systems.11,12 Furthermore, ENMs in cosmetics and personal care products, such as lotion, sunscreen and shampoo may enter human circulation through skin penetration.13 ENMs are very persistent in the environment and are slowly degraded. The dissolved metal ions from ENMs can also revert back to nanoparticles under natural conditions.14 ENMs are stored in plants, microbes and animal organs and can be transferred and accumulated through the food chain.15,16 In addition to the accidental entry of ENMs into human and biological systems, ENMs are also purposefully injected into or enter humans for medicinal and diagnostic purposes.17 Therefore, interactions of ENMs with biological systems are inevitable. In addition to engineered nanomaterials, there are also naturally existing nanomaterials such as proteins and DNA molecules, which are key components of biological systems. These materials, combined with lipids and organic and inorganic small molecules, form the basic units of living systems –cells.18 To elucidate how nanomaterials affect organs and physiological functions, a thorough understanding of how nanomaterials perturb cells and biological molecules is required (Figure 1). Rapidly accumulating evidence indicates that ENMs interact with the basic components of biological systems, such as proteins, DNA molecules and cells.19-21 The driving forces for such interactions are quite complex and include the size, shape and surface properties (e.g., hydrophobicity, hydrogen-bonding capability, pi-bonds and stereochemical interactions) of ENMs.22-25 Figure 1 Interactions of nanoparticles with biological systems at different levels. Nanoparticles enter the human body through various pathways, reaching different organs and contacting tissues and cells. All of these interactions are based on nanoparticle-biomacromolecule ... Evidence also indicates that chemical modifications on a nanoparticle’s surface alter its interactions with biological systems.26-28 These observations not only support the hypothesis that basic nano-bio interactions are mainly physicochemical in nature but also provide a powerful approach to controlling the nature and strength of a nanoparticle’s interactions with biological systems. Practically, a thorough understanding of the fundamental chemical interactions between nanoparticles and biological systems has two direct impacts. First, this knowledge will encourage and assist experimental approaches to chemically modify nanoparticle surfaces for various industrial or medicinal applications. Second, a range of chemical information can be combined with computational methods to investigate nano-biological properties and predict desired nanoparticle properties to direct experiments.29-31 The literature regarding nanoparticle-biological system interactions has increased exponentially in the past decade (Figure 2). However, a mechanistic understanding of the chemical basis for such complex interactions is still lacking. This review intends to explore such an understanding in the context of recent publications. Figure 2 An analysis of literature statistics indicates growing concern for the topics that are the focus of this review. The number of publications and citations were obtained using the keywords “nanoparticles” and “biological systems” ... A breakthrough technology cannot prosper without wide acceptance from the public and society; that is, it must pose minimal harm to human health and the environment. Nanotechnology is now facing such a critical challenge. We must elucidate the effects of ENMs on biological systems (such as biological molecules, human cells, organs and physiological systems). Accumulating experimental evidence suggests that nanoparticles interact with biological systems at nearly every level, often causing unwanted physiological consequences. Elucidating these interactions is the goal of this review. This endeavor will help regulate the proper application of ENMs in various products and their release into the environment. A more significant mission of this review is to direct the development of “safe-by-design” ENMs, as their demands for and applications continue to increase.

Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis

Evaluation of biological effects, both desired and undesired, caused by manufactured nanoparticles (MNPs) is of critical importance for nanotechnology. Experimental studies, especially toxicological, are time-consuming, costly, and often impractical, calling for the development of efficient computational approaches capable of predicting biological effects of MNPs. To this end, we have investigated the potential of cheminformatics methods such as quantitative structure-activity relationship (QSAR) modeling to establish statistically significant relationships between measured biological activity profiles of MNPs and their physical, chemical, and geometrical properties, either measured experimentally or computed from the structure of MNPs. To reflect the context of the study, we termed our approach quantitative nanostructure-activity relationship (QNAR) modeling. We have employed two representative sets of MNPs studied recently using in vitro cell-based assays: (i) 51 various MNPs with diverse metal cores (Proc. Natl. Acad. Sci. 2008, 105, 7387-7392) and (ii) 109 MNPs with similar core but diverse surface modifiers (Nat. Biotechnol. 2005, 23, 1418-1423). We have generated QNAR models using machine learning approaches such as support vector machine (SVM)-based classification and k nearest neighbors (kNN)-based regression; their external prediction power was shown to be as high as 73% for classification modeling and having an R(2) of 0.72 for regression modeling. Our results suggest that QNAR models can be employed for: (i) predicting biological activity profiles of novel nanomaterials, and (ii) prioritizing the design and manufacturing of nanomaterials toward better and safer products.

ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors

Selecting most rigorous quantitative structure-activity relationship (QSAR) approaches is of great importance in the development of robust and predictive models of chemical toxicity. To address this issue in a systematic way, we have formed an international virtual collaboratory consisting of six independent groups with shared interests in computational chemical toxicology. We have compiled an aqueous toxicity data set containing 983 unique compounds tested in the same laboratory over a decade against Tetrahymena pyriformis. A modeling set including 644 compounds was selected randomly from the original set and distributed to all groups that used their own QSAR tools for model development. The remaining 339 compounds in the original set (external set I) as well as 110 additional compounds (external set II) published recently by the same laboratory (after this computational study was already in progress) were used as two independent validation sets to assess the external predictive power of individual models. In total, our virtual collaboratory has developed 15 different types of QSAR models of aquatic toxicity for the training set. The internal prediction accuracy for the modeling set ranged from 0.76 to 0.93 as measured by the leave-one-out cross-validation correlation coefficient ( Q abs2). The prediction accuracy for the external validation sets I and II ranged from 0.71 to 0.85 (linear regression coefficient R absI2) and from 0.38 to 0.83 (linear regression coefficient R absII2), respectively. The use of an applicability domain threshold implemented in most models generally improved the external prediction accuracy but at the same time led to a decrease in chemical space coverage. Finally, several consensus models were developed by averaging the predicted aquatic toxicity for every compound using all 15 models, with or without taking into account their respective applicability domains. We find that consensus models afford higher prediction accuracy for the external validation data sets with the highest space coverage as compared to individual constituent models. Our studies prove the power of a collaborative and consensual approach to QSAR model development. The best validated models of aquatic toxicity developed by our collaboratory (both individual and consensus) can be used as reliable computational predictors of aquatic toxicity and are available from any of the participating laboratories.

Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.

In this paper we illustrate the application of the ISIDA (In SIlico design and Data Analysis) software to perform virtual screening of large databases of compounds and reactions and to assess some ADME/Tox properties. ISIDA represents an ensemble of tools allowing users to store, search and analyze the data, to perform similarity searches in large databases of molecules and reactions, to build and validate QSAR models, and to generate and screen virtual combinatorial libraries. It uses its own descriptors (substructural molecular fragments and fuzzy pharmacophore triplets). Workflow can be easily organized by combining different ISIDA modules. Several examples of ISIDA applications (similarity search of potent benzodiazepine ligands with FPT, QSAR modeling of aqueous solubility, aquatic toxicity, tissue-air partition coefficients, anti-HIV activity, and screening of the “Chimiotheque Nationale” Database), are discussed. Particular attention is paid to mining reaction databases using Condensed Reaction Graphs approach.

Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures.

Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically 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 toxicogenomics 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 end point was hepatotoxicity in the rat following 28 days of continuous 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 toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics 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 toxicogenomics 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 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 subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.

Comprehensive characterization of the Published Kinase Inhibitor Set

SummarySubstructural fragments are proposed as a simple and safe way to encode molecular structures in a matrix containing the occurrence of fragments of a given type. The knowledge retrieved from QSPR modelling can also be stored in that matrix in addition to the information about fragments. Complex supramolecular systems (using special bond types) and chemical reactions (represented as Condensed Graphs of Reactions, CGR) can be treated similarly. The efficiency of fragments as descriptors has been demonstrated in QSPR studies of aqueous solubility for a diverse set of organic compounds as well as in the analysis of thermodynamic parameters for hydrogen-bonding in some supramolecular complexes. It has also been shown that CGR may be an interesting opportunity to perform similarity searches for chemical reactions. The relationship between the density of information in descriptors/knowledge matrices and the robustness of QSPR models is discussed.

Materials Cartography: Representing and Mining Materials Space Using Structural and Electronic Fingerprints

Despite the success of protein kinase inhibitors as approved therapeutics, drug discovery has focused on a small subset of kinase targets. Here we provide a thorough characterization of the Published Kinase Inhibitor Set (PKIS), a set of 367 small-molecule ATP-competitive kinase inhibitors that was recently made freely available with the aim of expanding research in this field and as an experiment in open-source target validation. We screen the set in activity assays with 224 recombinant kinases and 24 G protein–coupled receptors and in cellular assays of cancer cell proliferation and angiogenesis. We identify chemical starting points for designing new chemical probes of orphan kinases and illustrate the utility of these leads by developing a selective inhibitor for the previously untargeted kinases LOK and SLK. Our cellular screens reveal compounds that modulate cancer cell growth and angiogenesis in vitro. These reagents and associated data illustrate an efficient way forward to increasing understanding of the historically untargeted kinome.