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This chapter, composed of two parts, firstly provides molecular docking overview and secondly two molecular docking case studies are described. In overview, basic information about molecular docking are presented such as description of search algorithms and scoring functions applied in various docking programs. Brief description of methods utilized in some of the most popular docking programs also applied in our experimental work is provided. AutoDock, CDOCKER, GOLD, FlexX and FRED were used for docking studies of the DC-SIGN protein, while GOLD was further used for docking studies of cathepsin K protein. Methods and results of our studies with their contribution to science and medicine are presented. Content of the chapter is therefore appropriate for public of Medicinal and Organic Chemistry as an overview of docking studies, and also for Computational Chemists at the beginning of their work as the introduction to application of molecular docking programs.

This chapter, composed of two parts, firstly provides molecular docking overview and secondly two molecular docking case studies are described. In overview, basic information about molecular docking are presented such as description of search algorithms and scoring functions applied in various docking programs. Brief description of methods utilized in some of the most popular docking programs also applied in our experimental work is provided. AutoDock, CDOCKER, GOLD, FlexX and FRED were used for docking studies of the DC-SIGN protein, while GOLD was further used for docking studies of cathepsin K protein. Methods and results of our studies with their contribution to science and medicine are presented. Content of the chapter is therefore appropriate for public of Medicinal and Organic Chemistry as an overview of docking studies, and also for Computational Chemists at the beginning of their work as the introduction to application of molecular docking programs.

Structure activity relationship (SAR) methods are applied for a study of inhibition of peptidoglycan metabolizing enzymes, which could represent new antibacterial targets. In this study, we exploit experimental data of inhibition of Mur A and Mur B enzymes for classification of large set of chemicals. Based on inhibitory potency of compounds and their structures from the literature, we developed classification models for new, potential inhibitors of Mur A and Mur B enzymes. The best model for Mur A has the following performance measures for the validation set: 0.85, 0.75, and 0.80, for sensitivity, specificity, and normalized Matthews correlation coefficient, respectively. The same measures of the best Mur B model are 0.94, 0.75, and 0.86. Such models could represent valuable computational tools for theoretic predictions of compounds' activities against specific targets. Additionally, application of such models, like any other computational tools, significantly reduces time and costs in the early phase of drug design.

La glicoproteína P (P-gp) es una proteína transmembrana que pertenece a la superfamilia de transportadores del cassette de unión a ATP, y es una bomba de eflujo xenobiótico que limita la acumulación intracelular de fármacos mediante el bombeo de compuestos fuera de las células. P-gp contribuye a una reducción de la toxicidad y tiene una amplia especificidad de sustrato. Está involucrado en el fracaso de muchas quimioterapias contra el cáncer y antivirales debido al fenómeno de resistencia a múltiples fármacos (RMF), en el que el transportador de membrana elimina los fármacos quimioterapéuticos de las células objetivo. Por lo tanto, comprender los detalles de la interacción ligando-P-gp es fundamental para el desarrollo de fármacos que puedan superar el fenómeno MDR, para la identificación temprana de sustratos de P-gp que nos ayudarán a obtener una predicción más eficaz de la toxicidad, y para el posterior diseño superior de las propiedades del sustrato si es necesario. En este trabajo, se realizaron una serie de simulaciones de dinámica molecular (MD) de P-gp humana (hP-gp) en un entorno explícito de membrana y agua para investigar los efectos de la unión de diferentes compuestos en la dinámica conformacional de P-gp. . Los resultados revelaron diferencias significativas en el comportamiento de P-gp en presencia de compuestos activos y no activos dentro del bolsillo de unión, ya que se identificaron diferentes patrones de movimiento que podrían estar correlacionados con cambios conformacionales que conducen a la activación del mecanismo de translocación. Las interacciones predichas del ligando-P-gp concuerdan bien con los datos experimentales disponibles, así como con la estimación de las energías libres de unión de los complejos estudiados, lo que demuestra la validez de los resultados derivados de las simulaciones MD