Virtual screening (VS) techniques are well-established tools in the modern drug discovery process, mainly used for hit finding in drug discovery. The availability of knowledge of structural information, which includes an increasing number of 3D protein structures and the readiness of free databases of commercially available smallmolecules, provides a broad platform for VS. This review summarizes the current developments in VS regarding chemical databases and highlights the achievements as well as the challenges with an emphasis on a recent example of the successful application for the identification of new hits for sterol 14α-demethylase (CYP51) of Trypanosoma cruzi.

R E S U M OEste trabalho objetivou avaliar, em casa de vegetação e usando colunas de solo e ciclos de umedecimento, o efeito da aplicação de resíduos da extração de celulose sobre a composição química do solo e da solução extraída de dois Latossolos Vermelhos. O delineamento experimental utilizado foi inteiramente casualizado. Os tratamentos utilizados foram: T1 -Sem adição de calcário e resíduo (testemunha); T2 -1,0 t ha -1 de calcário calcítico; T3 -1,2 t ha -1 de dregs/grits; T4 -0,8 t ha -1 de lama de cal; T5 -2,4 t ha -1 de cinza; T6 -1,8 t ha -1 de dregs/grits + lama de cal + cinza na proporção 1:3:6; T7 -1,0 t ha -1 de dregs/grits + lama de cal na proporção 1:3. A avaliação da composição química da solução extraída de cada solo estudado, foi realizada durante quatro meses de incubação e análises químicas do solo, após este período. Os resultados foram analisados efetuando-se a análise de variância e o teste de Tukey para as comparações de média no nível de 0,05 de probabilidade. Dentre os tratamentos aplicados o 1,2 t ha -1 de dregs/grits pode não ser adequado para utilização no solo visto que transferiu maior concentração de Na + para a solução do solo em relação ao recomendado pela literatura enquanto o tratamento 2,4 t ha -1 de cinza pode ser utilizado como fonte de K ao solo.Chemical attributes of the solution and soil after application of residue of cellulose extraction A B S T R A C TThe purpose of the study was to evaluate, under greenhouse conditions, using soil columns and cycles of wetting, the effect of residues from the extraction of cellulose on the chemical composition of the soil solution and on the two Red Oxisols. The experimental design was completely randomized. The treatments were: T1 -without the addition of lime and residue (control), T2 -1.0 t ha -1 of limestone, T3 -1.2 t ha -1 dregs/grits, T4 -0.8 t ha -1 of lime mud; T5 -2.4 t ha -1 ash, T6 -1.8 t ha -1 dregs/grits + lime mud + ash in the ratio 1:3:6, T7 -1.0 t ha -1 dregs/grits + lime mud in the ratio 1:3. Evaluation was performed of the chemical composition of the solution extracted from each soil during four months of incubation and also chemical analysis of soil thereafter. The results were analysed by performing analysis of variance and Tukey test for comparison of means at 0.05 probability level. Among the applied treatments, 1.2 t ha -1 dregs/grits is not found suitable for use in soil because of larger concentration of Na + transferred to the soil solution than recommended in the literature and the 2.4 t ha -1 ash treatment can be used as a source of K to the soil. Palavras-chave:Latossolo cinzas dregs/grits potássio sódio

The pharmacokinetic properties of absorption, distribution, metabolism and excretion (ADME) play a crucial role in drug discovery and development, since many drug candidates fail due to an inappropriate pharmacokinetic profile. Cytochrome P450 enzymes are predominantly involved in Phase 1 metabolism of xenobiotics. Thus, it is important to better understand and prognosticate substrate binding and inhibition of CYP450. The goal of this study was to obtain QSAR (Quantitative Structure-Activity Relationship) models to identify substrates and inhibitors of CYP3A4. The data sets were collected and curated from online available databases and literature. Several QSAR models were obtained and validated according to the recommendations of the Organization for Economic Cooperation Development (OECD). The combination of different descriptors and machine learning methods led to robust and predictive QSAR models with high coverage. The interpretation of developed models was performed using the predicted probability maps (PPMs). These maps help to encode major structural fragments to classify compounds as inhibitors or not inhibitors of CYP3A4. In conclusion, the obtained models can reliably identify substrates and non-substrates, and inhibitors and non-inhibitors of CYP3A4, which is very important in the early stages of the development of new drugs.

Many drug candidates fail during the drug development process due to an inappropriate pharmacokinetic profile. Biological evaluations are expensive and time consuming, thus, there is a strong need to develop cheap and fast alternatives. Computational methods have earned prestige the last decades as reliable tool to predict biological properties. In this chapter, we present commonly used in silico strategies (both ligand‐ and structure‐based) to evaluate the site of metabolism (SOM) of compounds that lack of experimental information.