Nitriles: an attractive approach to the development of covalent inhibitors.

Nitriles have broad applications in medicinal chemistry, with more than 60 small molecule drugs on the market containing the cyano functional group. In addition to the well-known noncovalent interactions that nitriles can perform with macromolecular targets, they are also known to improve drug candidates' pharmacokinetic profiles. Moreover, the cyano group can be used as an electrophilic warhead to covalently bind an inhibitor to a target of interest, forming a covalent adduct, a strategy that can present benefits over noncovalent inhibitors. This approach has gained much notoriety in recent years, mainly with diabetes and COVID-19-approved drugs. Nevertheless, the application of nitriles in covalent ligands is not restricted to it being the reactive center, as it can also be employed to convert irreversible inhibitors into reversible ones, a promising strategy for kinase inhibition and protein degradation. In this review, we introduce and discuss the roles of the cyano group in covalent inhibitors, how to tune its reactivity and the possibility of achieving selectivity only by replacing the warhead. Finally, we provide an overview of nitrile-based covalent compounds in approved drugs and inhibitors recently described in the literature.

Investigating the Lack of Translation from Cruzain Inhibition to Trypanosoma cruzi Activity with Machine Learning and Chemical Space Analyses.

Chagas disease is a neglected tropical disease caused by the protozoa Trypanosoma cruzi. Cruzain, its main cysteine protease, is commonly targeted in drug discovery efforts to find new treatments for this disease. Even though the essentiality of this enzyme for the parasite has been established, many cruzain inhibitors fail as trypanocidal agents. This lack of translation from biochemical to biological assays can involve several factors, including suboptimal physicochemical properties. In this work, we aim to rationalize this phenomenon through chemical space analyses of calculated molecular descriptors. These include statistical tests, visualization of projections, scaffold analysis, and creation of machine learning models coupled with interpretability methods. Our results demonstrate a significant difference between the chemical spaces of cruzain and T. cruzi inhibitors, with compounds with more hydrogen bond donors and rotatable bonds being more likely to be good cruzain inhibitors, but less likely to be active on T. cruzi. In addition, cruzain inhibitors seem to occupy specific regions of the chemical space that cannot be easily correlated with T. cruzi activity, which means that using predictive modeling to determine whether cruzain inhibitors will be trypanocidal is not a straightforward task. We believe that the conclusions from this work might be of interest for future projects that aim to develop novel trypanocidal compounds.

SELFIES and the future of molecular string representations.

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.

A patent review on cathepsin K inhibitors to treat osteoporosis (2011 - 2021).

INTRODUCTION: Cathepsin K (CatK) is a lysosomal cysteine protease and the predominant cathepsin expressed in osteoclasts, where it degrades the bone matrix. Hence, CatK is an attractive therapeutic target related to diseases characterized by bone resorption, like osteoporosis. AREAS COVERED: This review summarizes the patent literature from 2011 to 2021 on CatK inhibitors and their potential use as new treatments for osteoporosis. The inhibitors were classified by their warheads, with the most explored nitrile-based inhibitors. Promising in vivo results have also been disclosed. EXPERT OPINION: As one of the most potent lysosomal proteins whose primary function is to mediate bone resorption, cathepsin K remains an excellent target for therapeutic intervention. Nevertheless, there is no record of any approved drug that targets CatK. The most notable cases of drug candidates targeting CatK were balicatib and odanacatib, which reached Phase II and III clinical trials, respectively, but did not enter the market. Further developments include exploring new chemical entities beyond the nitrile-based chemical space, with improved ADME and safety profiles. In addition, CatK's role in cancer immunoexpression and its involvement in the pathophysiology of osteo- and rheumatoid arthritis have raised the race to develop activity-based probes with excellent potency and selectivity.