Query: medicinal chemistry

title: Drug Development and Medicinal Chemistry Efforts toward SARS Coronavirus and Covid 19 Therapeutics
abstract: The COVID 19 pandemic caused by SARS CoV 2 infection is spreading at an alarming rate and has created an unprecedented health emergency around the globe. There is no effective vaccine or approved drug treatment against COVID 19 and other pathogenic coronaviruses. The development of antiviral agents is an urgent priority. Biochemical events critical to the coronavirus replication cycle provided a number of attractive targets for drug development. These include, spike protein for binding to host cell surface receptors, proteolytic enzymes that are essential for processing polyproteins into mature viruses, and RNA dependent RNA polymerase for RNA replication. There has been a lot of ground work for drug discovery and development against these targets. Also, high throughput screening efforts have led to the identification of diverse lead structures, including natural product derived molecules. This review highlights past and present drug discovery and medicinal chemistry approaches against SARS CoV, MERS CoV and COVID 19 targets. The review hopes to stimulate further research and will be a useful guide to the development of effective therapies against COVID 19 and other pathogenic coronaviruses.

title: Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis
abstract: Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

title: A comprehensive overview of the medicinal chemistry of antifungal drugs: perspectives and promise.
abstract: The emergence of new fungal pathogens makes the development of new antifungal drugs a medical imperative that in recent years motivates the talents of numerous investigators across the world. Understanding not only the structural families of these drugs but also their biological targets provides a rational means for evaluating the merits and selectivity of new agents for fungal pathogens and normal cells. An equally important aspect of modern antifungal drug development takes a balanced look at the problems of drug potency and drug resistance. The future development of new antifungal agents will rest with those who employ synthetic and semisynthetic methodology as well as natural product isolation to tackle these problems and with those who possess a clear understanding of fungal cell architecture and drug resistance mechanisms. This review endeavors to provide an introduction to a growing and increasingly important literature, including coverage of the new developments in medicinal chemistry since 2015, and also endeavors to spark the curiosity of investigators who might enter this fascinatingly complex fungal landscape.

title: The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology
abstract: Medicinal chemistry plays a fundamental and underlying role in chemical biology, pharmacology, and medicine to discover safe and efficacious drugs. Small molecule medicinal chemistry relies on iterative learning cycles composed of compound design, synthesis, testing, and data analysis to provide new chemical probes and lead compounds for novel and druggable targets. Using traditional approaches, the time from hypothesis to obtaining the results can be protracted, thus limiting the number of compounds that can be advanced into clinical studies. This challenge can be tackled with the recourse of enabling technologies that are showing great potential in improving the drug discovery process. In this Perspective, we highlight recent developments toward innovative medicinal chemistry strategies based on continuous flow systems coupled with automation and bioassays. After a discussion of the aims and concepts, we describe equipment and representative examples of automated flow systems and end to end prototypes realized to expedite medicinal chemistry discovery cycles.

title: Medicinal chemistry strategies to discover P glycoprotein inhibitors: An update.
abstract: The presence of multidrug resistance (MDR) in malignant tumors is one of the primary causes of treatment failure in cancer chemotherapy. The overexpression of the ATP binding cassette (ABC) transporter, P glycoprotein (P gp) which significantly increases the efflux of certain anticancer drugs from tumor cells, produces MDR. Therefore, inhibition of P gp may represent a viable therapeutic strategy to overcome cancer MDR. Over the past 4 decades, many compounds with P gp inhibitory efficacy (referred to as first and second generation P gp inhibitors) have been identified or synthesized. However, these compounds were not successful in clinical trials due to a lack of efficacy and/or untoward toxicity. Subsequently, third and fourth generation P gp inhibitors were developed but dedicated clinical trials did not indicate a significant therapeutic effect. In recent years, an extraordinary array of highly potent, selective, and low toxicity P gp inhibitors have been reported. Herein, we provide a comprehensive review of the synthetic and natural products that have specific inhibitory activity on P gp drug efflux as well as promising chemosensitizing efficacy in MDR cancer cells. The present review focuses primarily on the structural features, design strategies, and structure activity relationships (SAR) of these compounds.

title: Benefits and applications of microwave assisted synthesis of nitrogen containing heterocycles in medicinal chemistry
abstract: Nitrogen containing heterocycles are of immense research interest because they are often found as naturally occurring bioactive compounds. The prominence of N heterocycles makes it vital to develop methods to increase their synthetic efficiencies and probe the effects of their modifications on biological efficacy. Medicinal chemists have exploited microwave assisted organic synthesis (MAOS) to facilitate the development of complex heterocyclic structures. MAOS is a growing synthetic methodology among medicinal chemists and has proven to be more efficient in terms of reaction yield, reaction time, product purity and environmental friendliness for many reactions when compared to conventional thermal methods for cycloaddition and selective functionalization. The importance of nitrogen containing ring systems in medicine cannot be understated, as such ring systems have shown to be applicable in compounds such as vitamins, herbicides, anti fungal agents, anti bacterial agents and anti cancer agents, among other things. The significance of these applications has created an unprecedented need for more efficient synthetic methods. This review presents an overview of MAOS and its role in recent and pressing advancements for the synthesis of small and medium sized nitrogen containing heterocycles, including pyrroles, indoles, pyridines, pyrrolidines, imidazoles, pyrazoles, pyrazolines, lactams, and 1,2,3 triazoles, which are significant scaffolds for compounds with medicinal uses.

title: Assessing the impact of generative AI on medicinal chemistry
abstract: The profound challenges of drug discovery, coupled with the societal importance of the task, make it imperative that we investigate novel, creative methods that improve our abilities to design new medicines. In recent years, attempts at developing and deploying a wide range of computational methods to support drug discovery have accelerated sometimes with extraordinary claims made about their significance. Novel computational approaches require rigorous evaluation to determine their true utility in realworld drug discovery settings. Sadly, novel methods often are disclosed without sufficient documentation, making it difficult or impossible to carry out such an objective evaluation. Over the past few years, interest has grown in the application of artificial intelligence (AI) techniques to drug discovery1,2. One active branch of AI that has been the focus of a tremendous amount of recent activity is the field of generative modeling3 9. In this technique, a deep learning model is trained based on a corpus of existing molecules. The model typically 'encodes' a higher dimensional representation, such as a SMILES (simplified molecular input line entry system)10, into a lower dimensional representation, often referred to as a latent space. This latent space can then be 'decoded' back to the higherdimensional representation to create new molecules. The exploration of this latent space can be coupled with a predictive model with the aim of discovering novel, active molecules. In a sense, generative models can be seen as a variation on the de novo design11 programs that were in vogue during the 1990s and early 2000s. As with de novo design, evaluating the significance of the output of these models is not straightforward. Although two groups have made initial efforts at developing methods for benchmarking generative models12,13, evaluating the novelty, and ultimately the significance, of the molecules generated by these methods remains an open question. Such benchmarks provide a common ground for evaluation and comparison, but the ultimate value of generative models will be demonstrated through the synthesis and biological evaluation of the novel molecules they identify. One of the first examples of the synthesis and testing of molecules derived from a generative model was reported in a 2018 paper by Merk and co workers14. In this paper, the authors began by training a generative model on a set of >500,000 bioactive molecules from the ChEMBL database. They then fine tuned the model to generate molecules that would be agonists of retinoid X receptors (RXRs) or peroxisome proliferator activated receptors (PPARs) This fine tuning process involved training based on a set of 25 fatty acid mimetics known to be agonists of PPAR or RXR. The molecules produced by the generative model were evaluated based on a quantitative structure activity relationship (QSAR) model for PPAR and RXR activity. The authors then used the rankings from the QSAR model, along with a manual assessment of synthetic accessibility and chemical building block availability, to select five molecules to be synthesized. Although the authors reported that the five molecules selected for synthesis were not found in databases of reported molecules, they did not report the structures of the 25 fatty acid mimetics used in the training process. The five selected molecules were then synthesized, and two were found to be PPAR agonists with half maximal effective concentration (EC50) values between 4 mM and 14 mM. Two additional compounds were dual PPAR and RXR inhibitors with EC50 values between 60 nM and 13 mM. The fifth compound was reported as inactive. Another, more recent example of the synthesis of a set of compounds based on a generative model comes from a paper by Zhavoronkov et al.15 published in the September issue of Nature Biotechnology. In this paper, the authors trained a generative model based on a large set of discoidin domain receptor family member 1 (DDR1) inhibitors extracted from the scientific and patent literature. Based on the output of the generative model, they synthesized six molecules. Of these six molecules, four had measurable biochemical activity, with the best, 'compound 1' having a 10 nM biochemical half maximal inhibitory concentration (IC50) Compound 1 was then tested in the U2OS bone osteosarcoma cell line and shown to have an IC50 of 10.3 nM. In a subsequent mouse pharmacokinetic study, it was also shown to have reasonable bioavailability as well as a half life of 3.5 h. This result received quite a bit of notice in the scientific and popular press and was hailed as 'revolutionary' by several pundits, some of them with clear competing financial interests16. An investor in the company that published the paper went as far as to refer to this as "Pharma's AlphaGo Moment," referring to the recent case in which Google's program AlphaGo17 was able to defeat grandmaster Fan Hui in the challenging strategy game known as Go. One fact that seems to have escaped most of these pundits is the striking similarity between compound 1 in the Zhavoronkov paper and ponatinib (marketed as the drug Iclusig;  Ponatinib was originally developed by ARIAD Pharmaceuticals as a BCR ABL inhibitor18 for the treatment of acute lymphocytic leukemia and chronic myelogenous leukemia. Subsequent testing showed that the compound was a potent inhibitor of multiple tyrosine kinases19. This broad kinase profile is believed to be Gao et al. Compound 7r 6 nM Zhavoronkov et al. Compound 1 10 nM Ponatinib 9 nM

title: Systemic delivery of peptides by the oral route: Formulation and medicinal chemistry approaches.
abstract: In its 33 years, ADDR has published regularly on the potential of oral delivery of biologics especially peptides and proteins. In the intervening period, analysis of the preclinical and clinical trial failures of many purported platform technologies has led to reflection on the true status of the field and reigning in of expectations. Oral formulations of semaglutide, octreotide, and salmon calcitonin have completed Phase III trials, with oral semaglutide being approved by the FDA in 2019. The progress made with oral peptide formulations based on traditional permeation enhancers is against a background of low oral and variable bioavailability values of ~1% leading to a current perception that only potent peptides with a viable cost of synthesis can be realistically considered. Desirable features of candidates should include a large therapeutic index, some stability in the GI tract, a long elimination half life, and a relatively low clearance rate. Administration in nanoparticle formats have largely disappointed, with few prototypes reaching clinical trials: insufficient particle loading, lack of controlled release, low epithelial particle uptake, and lack of scalable synthesis are being the main reasons for discontinuation. Disruptive technologies based on engineered devices promise improvements, but scale up and toxicology aspects are issues to address. In parallel, medicinal chemists are synthesizing stable hydrophobic macrocyclic candidate peptides of lower molecular weight with potential for greater oral bioavailability than linear peptides, but without a requirement for elaborate drug delivery systems. In summary, while there have been advances in understanding the limitations of peptides for oral delivery, low membrane permeability, metabolism, and high clearance rates continue to hamper progress.

title: Fragment to Lead Medicinal Chemistry Publications in 2018.
abstract: This Perspective, the fourth in an annual series, summarizes fragment to lead (F2L) success stories published during 2018. Topics such as target class, screening methods, physicochemical properties, and ligand efficiency are discussed for the 2018 examples as well as for the combined 111 F2L examples covering 2015 2018. While the overall properties of fragments and leads have remained constant, a number of new trends are noted, for example broadening of target class coverage and application of FBDD to covalent inhibitors. Moreover, several studies make use of fragment hits that were previously described in the literature, illustrating that fragments are versatile starting points that can be optimized to structurally diverse leads. By focusing on success stories, the hope is that this Perspective will identify and inform best practices in fragment based drug discovery.

title: A medicinal chemistry perspective of drug repositioning: Recent advances and challenges in drug discovery
abstract: Drug repurposing is a strategy consisting of finding new indications for already known marketed drugs used in various clinical settings or highly characterized compounds despite they can be failed drugs. Recently, it emerges as an alternative approach for the rapid identification and development of new pharmaceuticals for various rare and complex diseases for which lack the effective drug treatments. The success rate of drugs repurposing approach accounts for approximately 30% of new FDA approved drugs and vaccines in recent years. This review focuses on the status of drugs repurposing approach for various diseases including skin diseases, infective, inflammatory, cancer, and neurodegenerative diseases. Efforts have been made to provide structural features and mode of actions of drugs.