The hepatitis C virus (HCV), with a global prevalence of roughly 2%, is among the most serious diseases today. Among the more promising HCV targets is the NS3 protease, for which several drug candidates have entered clinical trials. In this work, computational methods have been developed and applied to the design of inhibitors of the HCV NS3 protease. Comparative molecular field analysis (CoMFA) modeling and molecular docking are the two main computational tools used in this work. CoMFA is currently the most widely used 3D-QSAR method. Methodology for improving its predictive performance by evaluating 6120 combinations of non-default parameters has been developed. This methodology was tested on 9 data sets for various targets and found to consistently provide models of enhanced predictive accuracy. Validation was performed using q2, r2pred and response variable randomization. Molecular docking was used to develop SARs in two series of inhibitors of the HCV NS3 protease. In the first series, preliminary investigations indicated that replacement of P2 proline with phenylglycine would improve potency. Docking suggested that phenylglycine-based inhibitors may participate in two additional interactions but that the larger, more flexible phenylglycine group may result in worse ligand fit, explaining the loss in potency. In the second series, β-amino acids were explored as α-amino acid substitutes. Although β-amino acid substitution may reduce the negative attributes of peptide-like compounds, this study showed that β-amino acid substitution resulted in reduced potency. The P3 position was least sensitive to substitution and the study highlighted the importance of interactions in the oxyanion hole. Finally, docking was used to provide the conformations and alignment necessary for a CoMFA model. This CoMFA model, derived using default settings, had q2 = 0.31 and r2pred = 0.56. Application of the optimization methodology provided a more predictive model with q2 = 0.48 and r2pred = 0.68.

Computer-aided drug discovery involves the application of computer science and programming to solve chemical and biological problems. Specifically, the QSAR (Quantitative Structure Activity Relationships) methodology is used in drug development to provide a rational basis of drug synthesis, rather than a trial and error approach. Molecular dynamics (MD) studies focus on investigating the details of drug-target interactions to elucidate various biophysical characteristics of interest. Infectious diseases like Trypanosoma brucei rhodesiense (TBR) and P. falciparum (malaria) are responsible for millions of deaths annually around the globe. This necessitates an immediate need to design and develop new drugs that efficiently battle these diseases. As a part of the initiatives to improve drug efficacy QSAR studies accomplished the formulation of chemical hypothesis to assist development of drugs against TBR. Results show that CoMSIA 3D QSAR models, with a Pearson’s correlation coefficient of 0.95, predict a compound with meta nitrogens on the phenyl groups, in the combinatorial space based on a biphenyl-furan diamidine design template, to have higher activity against TBR relative to the existing compound set within the same space. Molecular dynamics study, conducted on a linear benzimidazole-biphenyl diamidine that has non-classical structural similarity to earlier known paradigms of minor groove binders, gave insights into the unique water mediated interactions between the DNA minor groove and this ligand. Earlier experiments suggested the interfacial water molecules near the terminal ends of the ligand to be responsible for the exceptianlly high binding constant of the ligand. Results from MD studies show two other modes of binding. The first conformation has a single water molecule with a residency time of 6ns (average) that is closer to the central part of the ligand, which stabilizes the structure in addition to the terminal water. The second conformation that was detected had the ligand completely away from the floor of the minor groove, and hydrogen bonded to the sugar oxygens.

The efficient production of hydrogen hydrates is a major goal in the attempt to exploit those materials as an alternative means for storing hydrogen. Up to now, a few processes have been reported in the literature which yield less than 1 wt% of hydrogen stored into clathrate hydrate or semi-clathrate forms. One main obstacle to the entrapment of sensible amounts of hydrogen (i.e., up to 4 wt% ) into a clathrate matrix appears to be of a kinetic origin, in that the mass transfer of hydrogen gas into clathrate structures is drastically limited by the (relatively) macroscopic scale of the gas-liquid or gas-ice interfaces involved. In this communication, we present a novel process for an enhanced production of binary hydrates of hydrogen and other hydrate-forming gases, which is characterized by the use of nanotechnology for reducing the size of hydrate particles down to a few nanometers. This drastic reduction of particle size, down to three orders of magnitude smaller than that obtainable by macroscopic methods, allows to reduce the kinetic hindrance to hydrate formation. This process has a huge potential for increasing the amount of hydrogen stored, as it has provided ca. 1 wt% of hydrogen, with THF as a co-former. The present process also allows to use several non-water soluble coformers; first reports of hydrogen/cyclopentane and hydrogen/tetrahydrothiophene hydrates are presented.

New high throughput experimental techniques have turned the life sciences into a data-intensive field. Scientists are faced with new types of problems, such as managing voluminous sources of information, integrating heterogeneous data, and applying the proper analysis algorithms; all to end up with reliable conclusions. These challenges call for an infrastructure of algorithms and technologies to supply researchers with the tools and methods necessary to maximize the usefulness of the data. eScience has emerged as a promising technology to take on these challenges, and denotes integrated science carried out in highly distributed network environments, or science that makes use of large data sets and requires high performance computing resources. In this thesis I present standards, exchange formats, algorithms, and software implementations for empowering researchers in the life sciences with the tools of eScience. The work is centered around Bioclipse - an extensible workbench developed in the frame of this thesis - which provides users with instruments for carrying out integrated research and where technical details are hidden under simple graphical interfaces. Bioclipse is a Rich Client that takes full advantage of the many offerings of eScience, such as networked databases and online services. The benefits of mixing local and remote software in a unifying platform are demonstrated with an integrated approach for predicting metabolic sites in chemical structures. To overcome the limitations of the commonly used technologies for interacting with networked services, I also present a new technology using the XMPP protocol. This enables service discovery and asynchronous communication between the client and server, which is ideal for long-running analyses. To maximize the usefulness of the available data there is a need for standards, ontologies, and exchange formats, in order to define what information should be captured and how it should be structured and exchanged. A novel format for exchanging QSAR data sets in a fully interoperable and reproducible form is presented, together with an implementation in Bioclipse that takes advantage of eScience components during the setup process. Bioclipse has been well received by the scientific community, attracted a large group of international users and developers, and has been awarded three international prizes for its innovative character. With continued development, the project has a good chance of becoming an important component in a sustainable infrastructure for the life sciences.

Une approche intégrée physicochimie et relations structure-activité a été mise en œuvre afin d'étudier le phénomène rétention-libération des composés d'arôme dans un gel laitier allégé additionné de pectine. Notre objectif était d'identifier les propriétés moléculaires qui régissent ce phénomène en supposant que la modification de la structure entraîne forcement un changement dans la rétention-libération des composés d'arôme. Dans ce but, nous avons déterminé les coefficients de partage de 28 composés d'arôme dans l'eau, dans des gels de pectine et dans des gels laitiers avec ou sans de pectine, à l'équilibre en utilisant la méthode PRV (Phase Ratio Variation). Nous avons ensuite effectué une étude des relations structure-rétention en évaluant les corrélations entre les coefficients de partage et quatre descripteurs traduisant quatre propriétés moléculaires : l'hydrophobie globale, la surface moléculaire, la polarisabilité et la densité de charge négative. Notre démarche d'étude des relations structure-activité (Structure-Activity Relationships, SAR) consistait à étudier des composés d'arôme appartenant à une gamme de structures variée, dans un même ensemble, puis en sous-groupes en fonction d'une particularité structurale donnée afin de révéler les particularités de la structure qui influent sur le phénomène rétention-libération. La comparaison des rétentions entre les milieux n'a pas montré l'existence d'un effet pectine. Les études des relations structure-activité ont montré l'impact de certaines particularités structurales telles que la ramification et la double liaison sur la rétention. Elles ont également montré que l'hydrophobie globale des molécules n'était pas la propriété moléculaire la plus à même d'expliquer les phénomènes impliqués dans les interactions de molécules odorantes avec les constituants du milieu (eau ou gel laitier). La surface et la polarisabilité rendent mieux compte des rétentions des composés d'arôme. Les corrélations impliquant la surface, la polarisabilité et l'hydrophobie globale, confirment que les interactions de type van der Waals (essentiellement Keesom et London) sont favorables à la rétention dans les gels laitiers et défavorables à la rétention dans l'eau. De même, les corrélations impliquant la densité de charge montrent que les interactions polaires sont favorables à la rétention dans l'eau. Notre choix de départ, qui consistait à faire varier la structure des composés d'arôme afin d'apprécier son effet sur le phénomène rétention-libération des composés d'arôme, s'est avéré concluant, et le groupe de 28 composés permet effectivement de mener une étude quantitative des relations structure-propriété. Cette démarche QSAR pourra se transposer à des systèmes alimentaires simples ou complexes.

Dans un premier temps, nous avons modélisé la structure d’une famille d’ARN avec une grammaire de graphes afin d’identifier les séquences qui en font partie. Plusieurs autres méthodes de modélisation ont été développées, telles que des grammaires stochastiques hors-contexte, des modèles de covariance, des profils de structures secondaires et des réseaux de contraintes. Ces méthodes de modélisation se basent sur la structure secondaire classique comparativement à nos grammaires de graphes qui se basent sur les motifs cycliques de nucléotides. Pour exemplifier notre modèle, nous avons utilisé la boucle E du ribosome qui contient le motif Sarcin-Ricin qui a été largement étudié depuis sa découverte par cristallographie aux rayons X au début des années 90. Nous avons construit une grammaire de graphes pour la structure du motif Sarcin-Ricin et avons dérivé toutes les séquences qui peuvent s’y replier. La pertinence biologique de ces séquences a été confirmée par une comparaison des séquences d’un alignement de plus de 800 séquences ribosomiques bactériennes. Cette comparaison a soulevée des alignements alternatifs pour quelques unes des séquences que nous avons supportés par des prédictions de structures secondaires et tertiaires. Les motifs cycliques de nucléotides ont été observés par les membres de notre laboratoire dans l'ARN dont la structure tertiaire a été résolue expérimentalement. Une étude des séquences et des structures tertiaires de chaque cycle composant la structure du Sarcin-Ricin a révélé que l'espace des séquences dépend grandement des interactions entre tous les nucléotides à proximité dans l’espace tridimensionnel, c’est-à-dire pas uniquement entre deux paires de bases adjacentes. Le nombre de séquences générées par la grammaire de graphes est plus petit que ceux des méthodes basées sur la structure secondaire classique. Cela suggère l’importance du contexte pour la relation entre la séquence et la structure, d’où l’utilisation d’une grammaire de graphes contextuelle plus expressive que les grammaires hors-contexte. Les grammaires de graphes que nous avons développées ne tiennent compte que de la structure tertiaire et négligent les interactions de groupes chimiques spécifiques avec des éléments extra-moléculaires, comme d’autres macromolécules ou ligands. Dans un deuxième temps et pour tenir compte de ces interactions, nous avons développé un modèle qui tient compte de la position des groupes chimiques à la surface des structures tertiaires. L’hypothèse étant que les groupes chimiques à des positions conservées dans des séquences prédéterminées actives, qui sont déplacés dans des séquences inactives pour une fonction précise, ont de plus grandes chances d’être impliqués dans des interactions avec des facteurs. En poursuivant avec l’exemple de la boucle E, nous avons cherché les groupes de cette boucle qui pourraient être impliqués dans des interactions avec des facteurs d'élongation. Une fois les groupes identifiés, on peut prédire par modélisation tridimensionnelle les séquences qui positionnent correctement ces groupes dans leurs structures tertiaires. Il existe quelques modèles pour adresser ce problème, telles que des descripteurs de molécules, des matrices d’adjacences de nucléotides et ceux basé sur la thermodynamique. Cependant, tous ces modèles utilisent une représentation trop simplifiée de la structure d’ARN, ce qui limite leur applicabilité. Nous avons appliqué notre modèle sur les structures tertiaires d’un ensemble de variants d’une séquence d’une instance du Sarcin-Ricin d’un ribosome bactérien. L’équipe de Wool à l’université de Chicago a déjà étudié cette instance expérimentalement en testant la viabilité de 12 variants. Ils ont déterminé 4 variants viables et 8 létaux. Nous avons utilisé cet ensemble de 12 séquences pour l’entraînement de notre modèle et nous avons déterminé un ensemble de propriétés essentielles à leur fonction biologique. Pour chaque variant de l’ensemble d’entraînement nous avons construit des modèles de structures tertiaires. Nous avons ensuite mesuré les charges partielles des atomes exposés sur la surface et encodé cette information dans des vecteurs. Nous avons utilisé l’analyse des composantes principales pour transformer les vecteurs en un ensemble de variables non corrélées, qu’on appelle les composantes principales. En utilisant la distance Euclidienne pondérée et l’algorithme du plus proche voisin, nous avons appliqué la technique du « Leave-One-Out Cross-Validation » pour choisir les meilleurs paramètres pour prédire l’activité d’une nouvelle séquence en la faisant correspondre à ces composantes principales. Finalement, nous avons confirmé le pouvoir prédictif du modèle à l’aide d’un nouvel ensemble de 8 variants dont la viabilité à été vérifiée expérimentalement dans notre laboratoire. En conclusion, les grammaires de graphes permettent de modéliser la relation entre la séquence et la structure d’un élément structural d’ARN, comme la boucle E contenant le motif Sarcin-Ricin du ribosome. Les applications vont de la correction à l’aide à l'alignement de séquences jusqu’au design de séquences ayant une structure prédéterminée. Nous avons également développé un modèle pour tenir compte des interactions spécifiques liées à une fonction biologique donnée, soit avec des facteurs environnants. Notre modèle est basé sur la conservation de l'exposition des groupes chimiques qui sont impliqués dans ces interactions. Ce modèle nous a permis de prédire l’activité biologique d’un ensemble de variants de la boucle E du ribosome qui se lie à des facteurs d'élongation. / Initially, we modeled the structure of an RNA family with a graph grammar to identify sequences that correspond to it. Several other modeling approaches have been developed to derive sequences, such as stochastic context-free grammars, covariance models, secondary structures profiles and constraint networks. These modeling methods are based on secondary structure compared to our graph grammars which are based on the nucleotide cyclic motifs. To exemplify our graph grammar model, we used the loop E of the ribosome that contains the Sarcin-Ricin motif that has been widely studied since its discovery by X-ray crystallography in the early 90s. We built a graph grammar for the structure of the Sarcin-Ricin motif and derived the sequences that correspond to it. The biological relevance of these sequences is supported by an alignment of 800 bacterial ribosomal sequences. This comparison raised alternative alignments for some of the sequences that we supported by predictions of secondary and tertiary structures. According to a new tertiary structure, those alternative alignments accommodate the new derived sequences. The nucleotide cyclic motifs used in the grammar were observed by members of our laboratory in RNA tertiary structures that were solved experimentally. We study the sequences and tertiary structures of the nucleotide cyclic motifs of the Sarcin-Ricin motif. This study suggests that the space of sequences depends heavily on interactions between all nucleotides in the nearby three-dimensional space and not only between two adjacent base pairs. We compare the number of sequences generated by the graph grammar with non contextual methods and our graph grammar generates less sequences. This suggests the importance of context for the relationship between sequence and structure, hence the use of a contextual graph grammar is more expressive than context-free grammars. The graph grammars we used include the tertiary structure but neglect the interactions with extra-molecular factors, such as other macromolecules or ligands. In a second stage and to take into account these interactions, we developed a model incorporating the positioning of chemical groups on the surface of the tertiary structures. The assumption being that the chemical groups that are conserved on the surface of the RNA in active sequences are more likely to be involved in interactions with extra-molecular factors. Continuing with the example of the loop E, we searched the groups that could be involved its interactions with elongation factors. Knowledge of the groups involved in the important interactions serves to predict by three-dimensional modeling new sequences that have potentials to realize these interactions and thus the same function. There are few models that have been developed to address this problem: molecular descriptors, nucleotide adjacency matrices and others based on thermodynamics. These models use an oversimplified representation of the RNA structure, which limits their applicability. We applied our model to the tertiary structures of a set of variants of a sequence of one instance of the Sarcin-Ricin motif from a bacterial ribosome. Wool and coworkers at the University of Chicago studied this proceeding experimentally by testing the viability of twelve variants. They identified four viable variants and eight lethal. We used this set of twelve sequences for training our model and we identified a set of essential properties to their biological function. For each variant of the training set we built models of tertiary structures. We then measured the partial charges of exposed atoms on the surface and we encoded this information into vectors. We used principal component analysis to transform the vectors into a set of uncorrelated variables, called principal components. Using the weighted Euclidean distance and a nearest neighbor algorithm, we applied the technique of "Leave-One-Out Cross-Validation" to choose the best parameters to predict the activity of a new sequence to match these principal components. Finally, we validated the predictive model using a new set of eight variants whose viability has been verified experimentally in our laboratory. In conclusion, graph grammars are used to model the relationship between sequence and structure of an RNA structural element, such as the ribosomal loop E containing the Sarcin-Ricin motif. Applications range from the correction of sequence alignment to sequence design with a predetermined structure. We also developed a model to take into account the specific interactions related to a specific biological function. Our model is based on the retention of the exposure of chemical groups that are involved in these interactions. This model has allowed us to predict the biological activity of a set of variants of the loop E that binds to elongation factors.

Despite the large arsenal of antibiotics available on the market, treatment of bacterial infections becomes more challenging in view of the fact that microbes develop resistance against existing drugs. There is an obvious need for novel drugs acting on both old and new targets in bacteria. In this thesis we have employed a whole cell bacterial assay for screening and identification of type III secretion system (T3SS) inhibitors in Yersinia pseudotuberculosis. The T3SS is a common virulence mechanism utilized by several clinically relevant Gram-negative bacteria including Salmonella, Shigella, Pseudomonas aeruginosa, Chlamydiae and Escherichia coli. Several components in the T3SS have proved to be conserved and hence data generated with Y. pseudotuberculosis as model might also be valid for other bacterial species. We have screened a 9,400 commercial compound library for T3S inhibitors in Y. pseudotuberculosis using a yopE reporter gene assay. The initial ~ 30 hits were followed up in a growth inhibition assay resulting in 26 interesting compounds that were examined in more detail. Three of the most interesting compounds, salicylanilides, 2-hydroxybenzylidene-hydrazides and 2-arylsulfonamino-benzanilides, were selected for continued investigations. The inhibitor classes show different profiles regarding the effects on T3SS in Yersinia and their use as research tools and identification of the target proteins using a chemical biology approach will increase our understanding of bacterial virulence. The 2-hydroxybenzylidene-hydrazides have been extensively studied in vitro and show potential as selective T3S inhibitors in several Gram-negative pathogens besides Y. pseudotuberculosis. The data obtained suggest that this inhibitor class targets a conserved protein in the secretion apparatus. In cell-based ex vivo infection models T3SS was inhibited to the advantage of the infected eukaryotic cells. The salicylanilides and 2-arylsulfonamino-benzanilides have been further investigated by statistical molecular design (SMD) followed by synthesis and biological evaluation in the T3SS linked reporter gene assay. Multivariate QSAR models were established despite the challenges with data obtained from assays using viable bacteria. Our results indicate that this SMD QSAR strategy is powerful in development of virulence inhibitors targeting the T3SS.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xvii, 271 p.; also includes graphics. Includes bibliographical references (p. 245-269). Available online via OhioLINK's ETD Center

In our modern society we are exposed to many natural and synthetic chemicals. The assessment of chemicals with regard to human safety is difficult but nevertheless of high importance. Beside clinical studies, which are restricted to potential pharmaceuticals only, most toxicity data relevant for regulatory decision-making are based on in vivo data. Due to the ban on animal testing of cosmetic ingredients in the European Union, alternative approaches, such as in vitro and in silico tests, have become more prevalent. In this thesis existing non-testing approaches (i.e. studies without additional experiments) have been extended, e.g. QSAR models, and new non-testing approaches, e.g. in vitro data supported structural alert systems, have been created. The main aspect of the thesis depends on the determination of data quality, improving modelling performance and supporting Adverse Outcome Pathways (AOPs) with definitions of structural alerts and physico-chemical properties. Furthermore, there was a clear focus on the transparency of models, i.e. approaches using algorithmic feature selection, machine learning etc. have been avoided. Furthermore structural alert systems have been written in an understandable and transparent manner. Beside the methodological aspects of this work, cosmetically relevant examples of models have been chosen, e.g. skin penetration and hepatic steatosis. Interpretations of models, as well as the possibility of adjustments and extensions, have been discussed thoroughly. As models usually do not depict reality flawlessly, consensus approaches of various non-testing approaches and in vitro tests should be used to support decision-making in the regulatory context. For example within read-across, it is feasible to use supporting information from QSAR models, docking, in vitro tests etc. By applying a variety of models, results should lead to conclusions being more usable/acceptable within toxicology. Within this thesis (and associated publications) novel methodologies on how to assess and employ statistical data quality and how to screen for potential liver toxicants have been described. Furthermore computational tools, such as models for skin permeability and dermal absorption, have been created.

Submitted by Erika Demachki (erikademachki@gmail.com) on 2015-10-23T17:15:13Z No. of bitstreams: 2 Dissertação - Karlla Gonçalves Macedo - 2014.pdf: 3559333 bytes, checksum: be77b4325f787c0048a0c9a15e8c800b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2015-10-23T17:16:49Z (GMT) No. of bitstreams: 2 Dissertação - Karlla Gonçalves Macedo - 2014.pdf: 3559333 bytes, checksum: be77b4325f787c0048a0c9a15e8c800b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-10-23T17:16:49Z (GMT). No. of bitstreams: 2 Dissertação - Karlla Gonçalves Macedo - 2014.pdf: 3559333 bytes, checksum: be77b4325f787c0048a0c9a15e8c800b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2014-08-05 / Drug discovery and development process requires high investments of both time and money. Strategies for drug design aided by computers, CADD (Computer-Aided Drug Design) have gained prominence over the last decades, in order to minimize the impact of those costs. CADD techniques also allow the exploration of a greater number of biological targets and promising molecules. Malaria is an endemic disease in Africa and in South American caused by the protozoa of the genus Plasmodium. In 2012, 207 million cases and 627,000 deaths were estimated, according to the World Health Organization. The enzyme dihydroorotate dehydrogenase (DHODH) catalyzes the fourth step of the pyrimidine biosynthesis, and consists in a validated target for the design of new antimalarial agents. The aim of this study was to develop structure-activity relationships (SAR) rules and to generate quantitative structure-activity relationships (QSAR) models using a set of triazolopyrimidines described in the literature as inhibitors of DHODH from P. falciparum (PfDHODH). SAR rules were established using methods of clustering, activity cliffs and activity landscapes. In addition, several models of 2D-QSAR and hologram QSAR (HQSAR) were developed and validated. The SAR analyses allowed the understanding of the basic structural requirements for the antimalarial activity of triazolopyrimidines, like alkyl halides substituents on the triazolopimidinic ring, hydrophobic substituents in the para position on the benzene ring, all in agreement with the chemical space inside the active site of the PfDHODH. The HQSAR and 2D-QSAR models showed good statistical parameters and good predictive ability. The HQSAR contour maps were also consistent with the chemical space of the active site of the enzyme. The results of this study could serve as guide for the design of new antimalarials with higher potency. / O processo de planejamento e desenvolvimento de novos fármacos é um trabalho complexo, que demanda elevados investimentos de tempo e dinheiro. Estratégias de planejamento de fármacos auxiliadas por computador, CADD (Computer-Aided Drug Design) vêm se destacando, pois minimizam gastos e tempo, além de poder explorar um número maior de alvos biológicos e moléculas promissoras. A malária é uma doença endêmica grave na África e América do Sul, causada por protozoários do gênero Plasmodium. Em 2012 foram estimados 207 milhões de casos e 627.000 mortes, de acordo com a Organização Mundial da Saúde. A enzima diidroorotato desidrogenase (DHODH) atua na quarta etapa da biossíntese de pirimidinas, é um alvo validado para o planejamento de novos agentes antimaláricos. O objetivo geral deste trabalho foi desenvolver regras de relação entre estrutura e atividade (SAR) e modelos robustos e preditivos de relações quantitativas entre estrutura e atividade bidimensionais (QSAR-2D), utilizando um conjunto de triazolopirimidinas descritas na literatura como inibidores da DHODH de P. falciparum (PfDHODH). Foram desenvolvidas regras de SAR utilizando os métodos de análise de agrupamentos, cliffs de atividade e landscapes de atividade. Além disso, desenvolveu-se e validou-se vários modelos de QSAR–2D e de holograma QSAR (HQSAR). As análises de SAR, permitiram estabelecer requisitos estruturais essenciais para a atividade antimalárica das triazolopirimidinas, como substituintes haletos de alquila no anel triazolopimidínico, substituintes hidrofóbicos na posição para no anel benzênico, todos de acordo com o espaço químico da cavidade de interação da PfDHODH. Os modelos de HQSAR e QSAR-2D apresentaram bons parâmetros estatísticos e boa capacidade preditiva. Os mapas de contribuição de HQSAR também estão de acordo com o espaço químico da cavidade de interação da PfDHODH. Os dados obtidos servem como guia para o planejamento de novos antimaláricos com maior potência.