Virtual Sreening (VS) is an in silico technique for drug discovery. An overview of VS methods is given and is seen to be approachable from two sides: structure based and ligand based. Structure based virtual screening uses explicit knowledge of the target receptor to suggest candidate receptor-ligand complexes. Ligand based virtual screening can infer required characteristics of binders from known ligands. A consideration for all virtual screening techniques is the amount of computing time required to arrive at a solution. For this reason, techniques of high performance computing have been applied to both the structural and ligand based approaches. A proven structure based virtual screening code LIDAEUS (Ligand Discovery At Edinburgh University) has been ported and parallelised to a massively parallel computing platform, the University of Edinburgh’s IBM Bluegene/l, consisting of 2,048 processor cores. A challenge in achieving scaling to such a large number of processors required implementation of a minimal communication parallel sort algorithm. Parallel efficiencies achieved within this parallelisation exceeded 99%, confirming that a near optimum strategy has been followed and capacity for running the code on a greater number of processors exists. This implementation of the program has been successfully used with a number of protein targets. The development of a new ligand based virtual screening code has been completed. The program UFSRAT (Ultra Fast Shape Recognition with Atom Types) takes the features of known binders and suggests molecules which will be able to make similar interactions. This similarity method is both fast (1 million molecules per hour per processor) and independent of input orientation. Along with UFSRAT, some other methods (VolRAT and UFSRGraph) based on UFSRAT have been developed, addressing different approaches to ligand based virtual screening. UFSRAT as an approach to discovering novel protein-ligand complexes has been validated with the discovery of a number of inhibitors for 11β-Hydroxysteroid Dehydrogenase type 1 and FK binding protein 12.

A acne é uma condição da pele multifatorial com implicações socioeconômicas importantes. Um dos principais fatores que contribuem com a sua etiologia é a superprodução de sebo. Até o momento, há poucos tratamentos seguros e eficazes disponíveis. O receptor de melanocortina-5 (MC5R), um receptor acoplado à proteína G da família das rodopsinas, é uma das proteínas responsáveis pela diferenciação de sebócitos e consequente produção de sebo, mas não há opções de tratamento através do antagonismo deste receptor. Neste trabalho, investigamos a melanocortina-5 como alvo molecular para a descoberta de ligantes como moduladores da sebogênese. Para tanto, empregamos estudos de modelagem por homologia e triagem virtual baseada em estrutura do alvo para construir um modelo 3D da MC5R e identificar de candidatos a ligantes da proteína, respectivamente. Em seguida, avaliamos o potencial de inibição da sebogênese em sebócitos SEBO662AR em meio lipogênico. Os resultados obtidos indicaram a descoberta de peptídeos e flavonoides com características inibidoras e estimuladoras da produção de sebo. Novos esqueletos moleculares foram identificados como promissores para a modulação da sebogênese. Os estudos realizados permitirão o desenvolvimento de novos ativos dermatológicos e cosméticos com potencial de modular a oleosidade da pele, de modo a contribuir com a mitigação dos efeitos da acne, psoríase, alopecia e seborreia, entre outras doenças. / Acne is a multifactorial skin condition with important socioeconomic implications. One of the main factors that contribute with its etiology is sebum overproduction. Until now, there are few safe, effective treatments available. Melanocortin-5 receptor (MC5R), a G protein-coupled receptor of the rhodopsin family, is one of the proteins responsible for sebocyte differentiation and consequent sebum production, but there are no options for treatment by antagonism of this receptor. In this work, we investigated MC5R as molecular target for the discovery of ligands as sebogenesis modulators. For that, we used homology modeling studies, and structure-based virtual screening in order to, respectively, build a MC5R 3D model and identify ligand candidates for this protein. Then, we evaluated their sebogenesis inhibition potential on SEBO662AR sebocytes in lipogenic conditions. The obtained results indicated the discovery of peptides and flavonoids with inhibitory and stimulatory sebum production characteristics. New scaffolds were identified as promising for sebogenesis modulation. The performed studies will allow the development of novel dermatologic and cosmetic actives with the potential to modulate skin oiliness in order to contribute to the mitigation of the effects of acne, psoriasis, alopecia and seborrhea, among other diseases.

Computational drug design methods have great potential in drug discovery particularly in lead identification and lead optimization. 3-Phosphoinositide dependent kinase-1 (PDK1) is a protein kinase and a well validated anti-cancer target. Inhibitors of PDK1 have the potential to be developed as anti-cancer drugs. In this work, we have applied various novel computational drug design strategies to design and identify new PDK1 inhibitors with potential anti-cancer activity. We have pursued novel structure-based drug design strategies and identified a new binding mode for celecoxib and its derivatives binding with PDK1. This new binding mode provides a valuable basis for rational design of potent PDK1 inhibitors. In order to understand the structure-activity relationship of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The predictive ability of the developed 3D-QSAR models were validated using an external test set of compounds. An efficient strategy of the hierarchical virtual screening with increasing complexity was pursued to identify new hits against PDK1. Our approach uses a combination of ligand-based and structure-based virtual screening including shape-based filtering, rigid docking, and flexible docking. In addition, a more sophisticated molecular dynamics/molecular mechanics- Poisson-Boltzmann surface area (MD/MM-PBSA) analysis was used as the final filter in the virtual screening. Our screening strategy has led to the identification of a new PDK1 inhibitor. The anticancer activities of this compound have been confirmed by the anticancer activity assays of national cancer institute-developmental therapeutics program (NCI-DTP) using 60 cancer cell lines. The PDK1-inhibitor binding mode determined in this study may be valuable in future de novo drug design. The virtual screening approach tested and used in this study could also be applied to lead identification in other drug discovery efforts.