RESUMO

The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development process and low success rates. Traditional methodologies that rely on the one drug-one target paradigm have proven insufficient for the treatment of multifactorial diseases, leading to a shift to multitarget approaches. In this emerging paradigm, molecules with off-target and promiscuous interactions may result in preferred therapies. In this study, we developed a general pipeline combining machine learning algorithms and a deep generator network to train a dual inhibitor classifier capable of identifying putative pharmacophoric traits. As a case study, we focused on dual inhibitors targeting DNA methyltransferase 1 (DNMT) and histone deacetylase 2 (HDAC2), two enzymes that play a central role in epigenetic regulation. We used this approach to identify dual inhibitors from a novel large natural product database in the public domain. We used docking and atomistic simulations as complementary approaches to establish the ligand-interaction profiles between the best hits and DNMT1/HDAC2. By using the combined ligand- and structure-based approaches, we discovered two promising novel scaffolds that can be used to simultaneously target both DNMT1 and HDAC2. We conclude that the flexibility and adaptability of the proposed pipeline has predictive capabilities of similar or derivative methods and is readily applicable to the discovery of small molecules targeting many other therapeutically relevant proteins.

RESUMO

The incidence of skin cancers such as non-melanoma skin cancer and malignant melanoma has increased in the last few years mainly because of chronic exposure to ultraviolet (UV) radiation. Sunscreens protect the skin against harmful UV radiations; however, some limitations of these products justify the discovery of new UV filters. Novel 1,3,5-triazine derivatives (12a-h) obtained by the optimization of prototype resveratrol were synthesized and characterized. All compounds exhibited sun protection factor (SPF) and UVA protection factor (UVAPF) in the range of 3-17 and 3-13, respectively. These values were superior to resveratrol and the UV filter ethylhexyl triazone (EHT) currently available on the market. In addition, all compounds demonstrated in vitro antioxidant activity and thermal stability with the decomposition at temperatures above 236 °C. In conclusion, the novel 1,3,5-triazine derivatives have emerged as new UV filters with antioxidant effect useful to prevent skin cancer.

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RESUMO

The term drug design describes the search of novel compounds with biological activity, on a systematic basis. In its most common form, it involves modification of a known active scaffold or linking known active scaffolds, although de novo drug design (i.e., from scratch) is also possible. Though highly interrelated, identification of active scaffolds should be conceptually separated from drug design. Traditionally, the drug design process has focused on the molecular determinants of the interactions between the drug and its known or intended molecular target. Nevertheless, current drug design also takes into consideration other relevant processes than influence drug efficacy and safety (e.g., bioavailability, metabolic stability, interaction with antitargets).This chapter provides an overview on possible approaches to identify active scaffolds (including in silico approximations to approach that task) and computational methods to guide the subsequent optimization process. It also discusses in which situations each of the overviewed techniques is more appropriate.

Assuntos