The azulene scaffold from a medicinal chemist's perspective: Physicochemical and in vitro parameters relevant for drug discovery

Computational Medicinal Chemistry

The application of quantum mechanical (QM) methods in drug discovery is becoming increasingly popular. This is a consequence of improvements in computing hardware, which have led to advances in QM-algorithm development and new applications of QM methods. The first-principles nature of quantum mechanics requires no implicit parameterization, and therefore should allow for the calculation of highly accurate molecular geometries and properties. However, in reality this level of accuracy needs to be balanced with the high computational expense of precise calculations as well as the rigorous pace of drug-discovery research. As a result, a number of approximations are required, resulting in numerous methodological developments in QM. This has spurred the development of QM methods for many computer-aided drug-discovery problems, such as describing molecular interactions, providing estimates of binding affinities, determining ligand energies, refining molecular geometries, scoring of docked protein–ligand poses, describing molecular similarity, and deriving descriptors for quantitative structure–activity relationships.

Is chemical synthetic accessibility computationally predictable for drug and lead-like molecules? A comparative assessment between medicinal and computational chemists

The Croatian Chemical Society was established in 1926 and has developed over the decades into a society that actively supports all chemical activities in Croatia. The Society has eight divisions, the youngest of which, the Division of Medicinal and Pharmaceutical Chemistry, was established in 2012 and immediately became a member of the European Federation of Medicinal Chemistry (EFMC). The mission of the Medicinal and Pharmaceutical Chemistry Division is the promotion and development of scientific, professional, and educational activities within the medicinal chemistry community in Croatia, as well as to build partnerships and collaborations with other primarily EU-based medicinal chemistry societies. In Croatia, medicinal chemistry research is ongoing at several institutes, including the University of Zagreb (Faculty of Science, Faculty of Pharmacy and Biochemistry, and Faculty of Chemical Engineering and Technology), national institutes of science (Ruđer Bošković Institute), and private-sector drug discovery companies (CRO Fidelta Ltd.). In order to effectively exchange knowledge, ideas, and scientific results, Croatian medicinal chemists meet twice annually.

ACS AMA Hi Reddit! My name is Donna Huryn. I am a medicinal chemist at the University of Pittsburgh’s School of Pharmacy and have an adjunct appointment at the University of Pennsylvania’s (Penn’s) Chemistry Department. I received my Ph.D. in Organic Chemistry at Penn, then spent the first part of my career as a medicinal chemist in the pharmaceutical industry, working on inventing drugs to treat HIV, cancer, Alzheimer’s disease and other CNS disorders. In 2004, I moved to academia. Currently we work on medicinal chemistry projects focusing on new treatments for cancer and kidney disease. I am PI of the University of Pittsburgh Chemical Diversity Center – we are a member of NCI’s Chemical Biology Consortium (https://next.cancer.gov/discoveryResources/cbc.htm). This consortium brings together experts in multiple disciplines to focus on drug discovery for cancer, with the goal of advancing compounds into Phase I clinical trials. Our group in Pittsburgh contributes our medicinal, synthetic and computational chemistry expertise to various projects; other centers bring expertise in biological assays, biophysics, pharmacokinetics and animal models, among others. I also am one of the Associate Editors of ACS Medicinal Chemistry Letters (http://pubs.acs.org/journal/amclct), which publishes short, urgent communications in all areas of medicinal chemistry. Ask me anything about medicinal chemistry / drug discovery in academia. I’ll be back at 12pm EDT (9am PDT, 4pm UTC) to answer your questions. [EDIT] - Hello Reddit! Thanks for the great questions so far - looking forward to a stimulation hour [EDIT] - Thanks Reddit! It was a great hour. I am signing off now, but will try to come back to answer a few other questions later in the day.

The wide world of medicinal chemistry: We look back at our activities in 2017, particularly the expansion of the journal's scope to nanomedicine and why we need a more inclusive medicinal chemistry journal. Additionally, we look at upcoming special issues and developments for ChemPubSoc Europe in 2018.

“MEDICINAL CHEMISTRY: PRINCIPLES, PRACTICE, AND PERSPECTIVES” is a comprehensive guide that delves into the multifaceted world of medicinal chemistry, offering an in-depth exploration of both the fundamental and advanced aspects of the field. This book is meticulously designed to cater to the needs of students, researchers, and professionals in the pharmaceutical industry, providing them with a thorough understanding of the principles and practices that drive drug discovery and development. The book opens with Chapter 1: Introduction to Medicinal Chemistry, setting the stage by providing an overview of medicinal chemistry, tracing its historical development, and elucidating key concepts and terminology. This chapter highlights the pivotal role of medicinal chemists in the drug discovery process, emphasizing the interdisciplinary nature of the field and discussing the ethical considerations and future trends that will shape its evolution. In Chapter 2: Fundamentals of Drug Design and Discovery, readers are introduced to the critical stages of target identification and validation, lead compound discovery, and the various design strategies employed in drug development, such as structure-based, ligand-based, and fragment-based drug design. The chapter also covers the rational design and computational approaches that are integral to modern medicinal chemistry. Chapter 3: Pharmacodynamics and Pharmacokinetics delves into the basic principles governing drug action and movement within the body. It covers receptor binding, dose-response relationships, and the ADME (Absorption, Distribution, Metabolism, Excretion) processes. The chapter also addresses bioavailability, bioequivalence, and the therapeutic index, along with pharmacokinetic-pharmacodynamic (PK-PD) modeling, providing a holistic view of how drugs interact with biological systems. The exploration of Structure-Activity Relationships (SAR) in Chapter 4 is pivotal for understanding how chemical structure influences biological activity. This chapter discusses methods for determining SAR, functional group modifications, bioisosterism, and conformational analysis. It also introduces Quantitative Structure-Activity Relationships (QSAR) and the use of molecular descriptors in SAR studies. Chapter 5: Lead Optimization focuses on transforming lead compounds into viable drug candidates. It covers the hit-to-lead process, strategies for optimizing binding affinity, pharmacokinetic properties, and reducing toxicity. The chapter also explores improving selectivity and prodrug design, emphasizing the importance of refining chemical compounds to enhance their therapeutic potential. In Chapter 6: Drug Metabolism and Toxicology, readers gain insights into the metabolic pathways drugs undergo in the body, including Phase I and Phase II reactions. The chapter discusses enzyme induction and inhibition, toxicokinetics, predictive toxicology, and drug-drug interactions, along with safety pharmacology, underscoring the significance of understanding metabolism and toxicity in drug development. Chapter 7: Natural Products in Drug Discovery highlights the valuable role of natural products in medicinal chemistry. It covers sources, isolation, and characterization of natural compounds, their structural diversity, and pharmacological activities. The chapter also addresses semi-synthetic modifications and the challenges associated with natural product drug discovery. The synthesis of drugs is thoroughly examined in Chapter 8: Synthetic Methods in Medicinal Chemistry. Fundamental synthetic techniques, retrosynthetic analysis, and green chemistry principles are discussed, along with combinatorial chemistry, solid-phase synthesis, microwave-assisted synthesis, flow chemistry, and catalysis, providing a comprehensive view of synthetic methodologies. Chapter 9: Computational Medicinal Chemistry explores the use of computational tools in drug discovery. It covers molecular modeling, docking studies, pharmacophore modeling, virtual screening, molecular dynamics simulations, chemoinformatics, predictive ADMET models, and the application of machine learning in medicinal chemistry. Innovative drug delivery systems are the focus of Chapter 10. It addresses the challenges in drug delivery, the use of nanotechnology, liposomes, micelles, polymeric systems, targeted and controlled release systems, transdermal and intranasal delivery, and the development of bioconjugates and prodrugs. Chapter 11: Case Studies and Perspectives provides real-world examples of successes and failures in medicinal chemistry, discussing the impact of the field on healthcare, regulatory considerations, ethical and social implications, and future trends. The chapter emphasizes collaborative approaches and educational perspectives, offering a forward-looking view of the future of medicinal chemistry. “MEDICINAL CHEMISTRY: PRINCIPLES, PRACTICE, AND PERSPECTIVES” is an essential resource for anyone involved in the pharmaceutical sciences, offering a wealth of knowledge and insights into the dynamic and ever-evolving field of medicinal chemistry.

La question a ete abordee de facon chronologique et selon la problematique suivante : quel fut le role de l'Etat dans la definition de la nation au XIXeme siecle ? Des l'epoque coloniale, l'unification territoriale, linguistique et religieuse, le metissage et l'âprete de la lutte contre les araucans ont cree une cohesion parmi les habitants de la zone centrale du Chili. Le mouvement d'emancipation, de caractere aristocratique, fut neanmoins accompagne de mesures visant a integrer les differents groupes sociaux et ethniques a la nation creole et les dirigeants favoriserent la naissance d'une culture nationale. L'independance ne devait pas se limiter a une perception politique, il s'agissait aussi de creer une nouvelle identite culturelle basee sur l'exaltation du patriotisme. L'Etat definit tres tot la nation en termes culturels et affectifs et non pas ethniques. Le droit du sol fut immediatement reconnu par les constitutions. C'est autour d'un projet, celui du progres et de la modernite que s'est cimentee la nation chilienne. Apres l'independance, le Chili connut plusieurs guerres, contre la Bolivie et le Perou de Santa Cruz en 1836, contre l’Espagne en 1864 et enfin la guerre du Pacifique qui eut le plus gros impact sur l'ensemble de la population car elle fit naitre une cohesion inedite face a la menace exterieure. Ainsi, le Chili a construit son identite dans un premier temps en rejetant l’Espagne et dans un second temps en se demarquant culturellement et politiquement des pays voisins. La fierte nationale nee de la reussite economique, culturelle et militaire sera une des composantes essentielles de l'emergence d'une conscience nationale au Chili a la fin du XIXeme siecle.

A review on antioxidant potential of bioactive heterocycle benzofuran: Natural and synthetic derivatives.

Molecules that feature a sulfonyl fluoride (SO2F) moiety have been gaining increasing interest due to their unique reactivity and potential applications in synthetic chemistry, medicinal chemistry, and other biological uses. A particular interest is towards 18F-radiochemistry where sulfonyl fluorides can be used as a method to radiolabel biomolecules or can be used as radiofluoride relay reagents that facilitate radiolabeling of other molecules. The low metabolic stability of sulfonyl fluoride S-F bonds, however, presents an issue and limits the applicability of sulfonyl fluorides. The aim of this work was to increase understanding of what features contribute to the metabolic instability of the S-F bond in model aryl sulfonyl fluorides and identify approaches to increasing sulfonyl fluoride stability for 18F-radiochemistry and other medicinal, synthetic chemistry and biological applications. To undertake this, 14 model aryl sulfonyl fluorides compounds with varying functional groups and substitution patterns were investigated, and their stabilities were examined in various media, including phosphate-buffered saline and rat serum as a model for biological conditions. The results indicate that both electronic and steric factors affect the stability of the S-F bond, with the 2,4,6-trisubstituted model aryl sulfonyl fluorides examined displaying the highest in vitro metabolic stability.

Macrocycles are arising considerable interest in medicinal chemistry. With the goal of harnessing C-H activation reactions for the development of efficient macrocyclization processes, the ruthenium(II)-catalyzed cyclization of O-methyl benzhydroxamates possessing an ω-acetylenic chain was investigated to access new structurally diverse macrocyclic isoquinolones. A slow addition of the substrate and the presence of Cu(OAc)2 ⋅H2 O as an additive were crucial for the success of the macrocyclization that features an excellent functional-group compatibility, as illustrated by the successful synthesis of a library of 21 macrocyclic isoquinolones of different ring sizes and substitution patterns. These results contribute to significantly highlight the synthetic interest of C-H activation-mediated processes for the synthesis of new macrocyles incorporating heterocyclic scaffolds of potential interest in medicinal chemistry.

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTIntroductory Editorial for ACS Medicinal Chemistry LettersDennis Liotta Ph.D.Dennis Liotta, Ph.D.Professor and Director, The Emory Institute for Drug Discovery, Emory UniversityMore by Dennis Liotta, Ph.D.Cite this: ACS Med. Chem. Lett. 2010, 1, 1, 1Publication Date (Web):April 8, 2010Publication History Received12 March 2010Published online8 April 2010Published inissue 8 April 2010https://pubs.acs.org/doi/10.1021/ml100054qhttps://doi.org/10.1021/ml100054qproduct-reviewACS PublicationsCopyright © 2010 American Chemical SocietyRequest reuse permissionsArticle Views3026Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail Other access optionsGet e-Alertsclose SUBJECTS:Drug discovery,Medicinal chemistry Get e-Alerts

Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational techniques that can be used in this sense. In any case, almost all these methods focus on few fundamental aspects including: type (1) methods to quantify the molecular structure, type (2) methods to link the structure with the biological activity, and others. In particular, MARCH-INSIDE (MI), acronym for Markov Chain Invariants for Networks Simulation and Design, is a well-known method for QSAR analysis useful in step (1). In addition, the bio-inspired Artificial-Intelligence (AI) algorithms called Artificial Neural Networks (ANNs) are among the most powerful type (2) methods. We can combine MI with ANNs in order to seek QSAR models, a strategy which is called herein MIANN (MI & ANN models). One of the first applications of the MIANN strategy was in the development of new QSAR models for drug discovery. MIANN strategy has been expanded to the QSAR study of proteins, protein-drug interactions, and protein-protein interaction networks. In this paper, we review for the first time many interesting aspects of the MIANN strategy including theoretical basis, implementation in web servers, and examples of applications in Medicinal and Biological chemistry. We also report new applications of the MIANN strategy in Medicinal chemistry and the first examples in Physical and Organic Chemistry, as well. In so doing, we developed new MIANN models for several self-assembly physicochemical properties of surfactants and large reaction networks in organic synthesis. In some of the new examples we also present experimental results which were not published up to date.

Ni-catalyzed enantioselective [2 + 2 + 2] cycloaddition of malononitriles with alkynes

Halogen bonding as a modern molecular interaction has received increasing attention not only in materials sciences but also in biological systems and drug discovery. Thus, there is a growing demand for fast, efficient, and easily applicable tailor-made tools supporting the use of halogen bonds in molecular design and medicinal chemistry. The potential strength of a halogen bond is dependent on several properties of the σ-hole donor, e.g., a (hetero)aryl halide, and the σ-hole acceptor, a nucleophile with n or π electron density. Besides the influence of the interaction geometry and the type of acceptor, significant tuning effects on the magnitude of the σ-hole can be observed, caused by different (hetero)aromatic scaffolds and their substitution patterns. The most positive electrostatic potential on the isodensity surface ( Vmax), representing the σ-hole, has been widely used as the standard descriptor for the magnitude of the σ-hole and the strength of the halogen bond. Calculation of Vmax using quantum-mechanical methods at a reasonable level of theory is time-consuming and thus not applicable for larger numbers of compounds in drug discovery projects. Herein we present a tool for the prediction of this descriptor based on a machine-learned model with a speedup of 5 to 6 orders of magnitude relative to MP2 quantum-mechanical calculations. According to the test set, the squared correlation coefficient is greater than 0.94.