Molecular modeling of biomacromolecules. The main scope is the construction of structural models of proteins and their complexes with other macromolecules and also small molecules. In this sense, a wide range of methods are used such as homology modeling, threading and ab initio modeling.

Molecular dynamics. The understanding of conformational changes undergone by macromolecules and their effects on the function and modes of interaction with other macromolecules at the atomistic level is still quite incomplete. In this sense, molecular dynamics simulations have the power to provide a high resolution view of the biomolecular function and underlying mechanisms. These analyzes become even more powerful when one can compare, for example, unmodified native proteins with their modified pairs either by post-translational changes or mutations. To meet this goal, we create  procedures that will allow the understanding of modifications in biomacromolecules. The use of this tool will quantitatively show that microscopic properties are most affected by the modifications.

Modeling of Protein-Protein Interaction. Targets based on protein-protein interaction (IPP) have traditionally been avoided by several drug developers, despite their therapeutic relevance and abundance, largely due to technological obstacles. However, scientific advances suggest that these challenges are less and less worrying. The structures of more than 6,000 protein complexes have been solved using various techniques such as X-ray crystallography, Cryo Electron Microscopy, Small-Angle X-ray Scattering (SAXS) and Nuclear Magnetic Resonance (NMR), which, however, are expensive , complex and time-consuming. Because these limitations of experimental techniques, reconciling any available information (such as mass spectrometry and mutagenesis, for example) with computational techniques may help elucidate molecular mechanisms mediated by IPP. One of the techniques used is the macromolecular “docking” that consists of the theoretical modeling of the quaternary structure of complexes formed by two or more macromolecules. This method is widely applied for the elucidation of IPP and Protein-DNA / RNA interactions in its biological context and is helping in the planning of new drugs that act at the contact interface of these molecules.

Rational  Drug design The Computer Aided Drug Design (CADD) aims to accelerate production and reduce the cost of developing new drugs acting at all stages of the development of a drug. The process begins by obtaining the three dimensional structure of the target molecule using computational or experimental techniques such as, for example, IPP prediction or molecular modeling. The three dimensional structure is examined for potential regions of a binding interaction, these regions are called pharmacophores. Typical pharmacophorical features include  acceptor or hydrogen bond donors, hydrophobic cores, aromatic rings, cations and anions. These pharmacophoric points may be located on the ligand or can be estimated is assumed that the points be located in the receptor. The second step is to establish a list of molecules that can interact with the target. Currently there are several methods to select molecules that can be divided into two groups: steered methods and large scale methods. The steered methods can be divided into two groups: methods that use information about other molecules that interact with the target and methods that utilize the information obtained in the analysis of the pharmacophore. The large-scale methods can be divided into two groups based on the type of ligand library: i) ligands with multiple moieties and; ii)  library of molecular fragments and posteriori reconstruction of  possible molecules. Targeted methods are faster, but need a priori information, large-scale methods are more time consuming but do not require prior information, the choice of which method should be applied is made according to the knowledge we have of the target. The third stage of CADD is the molecular docking molecular. This technique predicts possible conformations of the molecules selected in the previous step and the receptor, besides to quantify the affinity of the complex (free Gibbs interaction energy).  These comparisons generate information relevant to the development of optimized ligands that promote increased stability of the complex and increased pharmacodynamic effectiveness. The refinement of the interaction between ligand and receptor can be accomplished using molecular simulation to a more precise claculation of the ligand binding energy. From these analyzes it is possible to select the best ligands or suggest chemical changes that can improve the affinity between the ligand and the receptor.