Fluorinated scaffolds for antimalarial drug discovery

The outbreak and high speed global spread of the new strain of influenza A/H1N1 virus in 2009 posed a serious threat to global health. It is more likely that drug-resistant influenza strains will arise after the extensive use of anti-influenza drugs. Consequently, the identification of the potential resistant sites for drugs in advance and the understanding of the corresponding molecular mechanisms that cause drug resistance are quite important in the design of new drug candidates with better potency to combat drug resistance. Here, we performed molecular simulations to evaluate the potency of oseltamivir to combat drug resistance caused by the mutations in 2009 A/H1N1 neuraminidase (NA). We examined three representative drug-resistant mutations in NA, consisting of H274Y, N294S, and Y252H. First, a theoretical structure of A/H1N1 NA in complex with oseltamivir was constructed using homology modeling. Then, molecular dynamics (MD) simulations, molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculations, and MM/GBSA free energy decomposition were used to characterize the binding of oseltamivir with the wild type (WT) and three mutated NAs. Our predictions show that N294S and H274Y, two popular drug-resistant mutations in different variants of NA, still cause significant resistance to oseltamivir. However, the Y252H mutation does not impair the interactions between oseltamivir and A/H1N1 NA. An examination of individual energy components shows that the loss of polar interactions is the key source for the resistance of the studied mutations to oseltamivir. Moreover, free energy decomposition analysis and structural analysis reveal that the N294S or H274Y mutation triggers the large-scale conformational changes of the binding pocket and then impairs the affinity of oseltamivir. We expect that our results will be useful for the rational design of NA inhibitors with high potency against drug-resistant A/H1N1 mutants.

Computational methodologies are used to increase the efficiency of drug discovery process by rendering the design of new drug candidates more rapid and cost-efficient. In silico techniques can be divided in two main groups. Structure-based drug design procedures can be applied (such as docking simulations) if the target is known from experimental (i.e., X-ray crystallographic studies, NMR studies) or theoretical sources (receptor structure built by homology modeling techniques). Otherwise, ligand-based drug design methods can be used (e.g., QSAR or 3D QSAR models, pharmacophoric models) based on the analysis of a number of ligands known to act with a common mechanism of action. Adenosine receptors (ARs) are a family of G-protein coupled receptors (GPCRs) of great interest as targets for therapeutic intervention. Due partly to the lack of reliable adenosine receptor structures, ligand-based drug discovery methods remain the major computational molecular modeling approach applied in the research of new AR ligands. The scope of this review is to summarize the results on pharmacophoric models and 3D QSAR studies concerning AR ligands. In particular, the review will focus on the use of such ligand-based computational techniques for the identification of new AR ligands and/or for their optimization.

Structural information on metabolites obtained in relevant biological systems can have considerable impact on the design of new drug candidates. However, with demanding turnaround times, the amount of available structural information may become rate limiting. The workflow for metabolite identification used in our laboratory was compared to a workflow using a software tool built for computer-assisted metabolite identification. The present study covered the in vitro metabolism of a diverse set of 65 in-house compounds. The compounds were profiled across three liver-based systems, 17 compounds were tested in human liver microsomes (HLM), 12 in rat hepatocytes (RHEP), and 36 in human hepatocytes (HHEP). For 92% of the metabolites reported, the exact match or Markush representations were in agreement between the two workflows. The major specific biotransformations in hepatocytes which formed the metabolites were aromatic or aliphatic hydroxylations (33%), N-dealkylations (15%) and glucuronidations (12%). The software was shown to perform well for structural elucidation of metabolites from both phase I and phase II metabolism where the focus was on quickly understanding the rate-limiting metabolic step(s).

Artificial intelligence (AI) has grown widely into diverse areas of research over the last decade. Publications distributed over the years disclose that biochemistry and analytical chemistry are incorporating AI to the utmost level. Enormous complex data sets are produced by analytical instruments comprising a treasure of information which is very helpful to analytical chemists in characterization. Despite this, we lack capabilities in data analysis and algorithms, which restricts our aptitude to discover and employ this data entirely. Machine learning and AI are being instigated to deal with this current challenge by accelerating several other applications in analytical chemistry. A critical valuation of illustrative reports amalgamating AI with analytical chemistry, spectroscopies, and several sensors has been discussed widely. This comprehensive review evaluates the AI advancement in data interpretation, the assistance of AI in analyte identification and quantification in proteomics and metabolomics, AI databases for primary and secondary metabolites information, regulatory and ethical guidelines in analytical chemistry incorporating AI, machine learning applications in drug discovery, agriculture, cosmetic, and food and separation techniques. In summary, this current review puts forward a thorough examination concerning the advancements of AI in different domains of chemistry and strives to facilitate comprehension of its forthcoming trajectories.

Alzheimer disease (AD) is one of the most common and serious neurodegenerative disorders in humans. For this reason, the search for new anti-AD treatments is a very active area. Only few biological receptors associated with AD have been well studied. The efficacy of the current drugs is limited by the fact that they inhibit only one target like protein. Thus, the rational design of new drug candidates as versatile inhibitors for different proteins associated with AD, constitutes a major goal. With the aim to overcome this problem, we developed here the first fragment-based approach by exploring quantitative-structure-activity relationships (QSAR). The principal purpose was the in silico design of multitarget (mt) inhibitors against five proteins associated with AD. Our approach was focused on the construction of an mt- QSAR discriminant model using a large and heterogeneous database of compounds and substructural descriptors, which permitted the simultaneous classification and prediction of inhibitors against five proteins associated with AD. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. As principal advantage, this mt-QSAR discriminant model was used for the automatic and fast extraction of fragments responsible for the inhibitory activity against the five proteins under study, and new molecular entities were suggested as possible versatile inhibitors for these proteins.

Cytochromes P450 (P450s) play a major role in the clearance of drugs, toxins, and environmental pollutants. Additionally, metabolism by P450s can result in toxic or carcinogenic products. The metabolism of pharmaceuticals by P450s is a major concern during the design of new drug candidates. Determining the interactions between P450s and compounds of very diverse structures is complicated by the variability in P450-ligand interactions. Understanding the protein structural elements and the chemical attributes of ligands that dictate their orientation in the P450 active site will aid in the development of effective and safe therapeutic agents. The goal of this review is to describe P450-ligand interactions from two perspectives. The first is the various structural elements that microsomal P450s have at their disposal to assume the different conformations observed in X-ray crystal structures. The second is P450-ligand dynamics analyzed by NMR relaxation studies.

Chapter 31. Carbohydrates as Drug Discovery Leads

Computer-aided design of new drugs is an exponentially growing field, especially in the last decade. The support of theoretical tools may accelerate the drug discovery process, which is a long and very expensive journey. Tools as QSAR and docking calculations are on the top of the list for helping medicinal chemists to find more potent and selective molecules as potential leads for facing challenging diseases. Coumarins have been an important source of inspiration for the design of new drugs. Due to their chemical properties and their affinity to some targets, special attention has been paid to their role against neurodegenerative diseases. Therefore, the authors provide an overview of the scientific reports describing the research and development of new drug design tools supporting the discovery of coumarins as enzymatic inhibitors or receptor ligands involved in these diseases. This review emphasizes the rationale behind the design of new drug candidates, and particular attention is paid to the search for new leads over the last 10 years. QSAR and docking studies are discussed, as well as new technologies applied for the research in this field. The manuscripts discussed in this review have been collected from multiple electronic databases, including Pubmed, SciFinder, and Mendeley.

Accelerating antischistosomal drug discovery : preclinical studies of antimalarials, synthetic peroxides and praziquantel derivatives

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

Background and aimThe discovery of drugs capable of inhibiting SARS-CoV-2 is a priority for human beings due to the severity of the global health pandemic caused by COVID-19. To this end, natural products can provide therapeutic alternatives that could be employed as an effective safe treatment for COVID-19. Experimental procedureTwelve compounds were isolated from the aerial parts of C. officinalis L. and investigated for their inhibitory activities against SARS-CoV-2 Mpro compared to its co-crystallized N3 inhibitor using molecular docking studies. Furthermore, a 100 ns MD simulation was performed for the most active two promising compounds, Calendulaglycoside A (SAP5) and Osteosaponin-I (SAP8). Results and conclusionAt first, molecular docking studies showed interesting binding scores as compared to the N3 inhibitor. Calendulaglycoside A (SAP5) achieved a superior binding than the co-crystallized inhibitor indicating promising affinity and intrinsic activity towards the Mpro of SARS-CoV-2 as well. Moreover, findings illustrated preferential stability for SAP5 within the Mpro pocket over that of N3 beyond the 40 ns MD simulation course. Structural preferentiality for triterpene-Mpro binding highlights the significant role of 17β-glucosyl and carboxylic 3α-galactosyl I moieties through high electrostatic interactions across the MD simulation trajectories. Furthermore, this study clarified a promising SAR responsible for the antiviral activity against the SARS-CoV-2 Mpro and the design of new drug candidates targeting it as well. The above findings could be promising for fast examining the previously isolated triterpenes both pre-clinically and clinically for the treatment of COVID-19.

The first documented use of Cannabis for therapeutic purposes is dated from 2300 BC, with indications for the treatment of constipation, gout, beriberi, malaria, rheumatism and menstrual impairment. The search for therapeutic effects of Cannabis has already been pursued by scientists for over 50 years, and in the last 10 years, a new phase studies has started about marijuana constituents, such as 9 -tetrahydrocannabinol (THC, 1) and cannabidiol (CBD, 2) for medicinal application. CBD is present in about 40% in the crude extract of Cannabis sativa, besides other 60 types of cannabinoids and a number of noncannabinoid constituents, accounting for approximately three hundred secondary metabolites. In spite of CBD being a constituent of Cannabis, it is deprived of the typical effects of the plant, differently from THC that induces anxiety and psychotic effects, being its main psychoactive component. Recent studies suggest that CBD could acts as a THC modulator in the brain, exhibiting anxiolytic and/or antipsychotic effects, antagonizing some undesirable effects of THC, as well as contributing with analgesic, antiemetic and antineoplastic, also affecting synapsis plasticity and facilitating neurogenesis. In this context, the scientific literature reinforces the need of advances and more investments in this research field, aiming a better knowledge about endocannabinoid system and the search for new natural and synthetic cannabinoids, supporting their use in the clinics and improving the available therapeutic arsenal for psychiatric and neurodegenerative diseases. Current data from studies dedicated to an improved understanding of its pharmacology and potential therapeutic applications, suggest that CBD and its analogues could play a singular role in the modulation of molecular targets related to central nervous system disorders, making possible the development of innovative, safer and efficient drugs for the treatment of non-responsive patients to the conventional clinics, with significant improvement in life quality and, perhaps, their cure.

For many machine learning applications in drug discovery, only limited amounts of training data are available. This typically applies to compound design and activity prediction and often restricts machine learning, especially deep learning. For low-data applications, specialized learning strategies can be considered to limit required training data. Among these is meta-learning that attempts to enable learning in low-data regimes by combining outputs of different models and utilizing meta-data from these predictions. However, in drug discovery settings, meta-learning is still in its infancy. In this study, we have explored meta-learning for the prediction of potent compounds via generative design using transformer models. For different activity classes, meta-learning models were derived to predict highly potent compounds from weakly potent templates in the presence of varying amounts of fine-tuning data and compared to other transformers developed for this task. Meta-learning consistently led to statistically significant improvements in model performance, in particular, when fine-tuning data were limited. Moreover, meta-learning models generated target compounds with higher potency and larger potency differences between templates and targets than other transformers, indicating their potential for low-data compound design.

1,2,4-Oxadiazoles have emerged as a significant class of heterocyclic compounds in medicinal chemistry due to their diverse biological activities and versatile applications in drug discovery. Herein is reported an in-depth analysis of the structural properties, synthetic methodologies, and pharmacological significance of 1,2,4-oxadiazoles. The compound's unique five-membered ring containing three heteroatoms offers remarkable stability and tunable physicochemical properties, making it an attractive scaffold for the development of novel therapeutic agents, as confirmed by already approved drugs. The review highlights the broad spectrum of biological activities associated with 1,2,4-oxadiazoles, including antimicrobial, anti-inflammatory, anticancer, antiviral, and central nervous system related activities. These diverse effects underscore the scaffold's potential for targeting multiple disease pathways. Several studies demonstrate that structural modifications on the oxadiazole ring can significantly influence its pharmacokinetic and pharmacodynamic profiles, enabling the design of selective and potent drug candidates. Additionally, it has explored recent advances in synthetic strategies for constructing the 1,2,4-oxadiazole core, such as cyclization reactions involving amidoximes, nitrile oxides, and other precursors. Modern approaches using transition-metal catalysis, microwave-assisted synthesis, and green chemistry techniques are also discussed, emphasizing their importance in improving efficiency and scalability for pharmaceutical applications. Furthermore, the role of 1,2,4-oxadiazole as bioisostere replacement for esters and amides is highlighted, particularly in enhancing metabolic stability and modulating target selectivity. Application of computational methods, including molecular docking and QSAR modeling, have been also covered in understanding ligand-receptor interactions and guiding lead optimization. This review underscores the growing prominence of 1,2,4-oxadiazoles in modern drug design and their potential to address unmet medical needs. With continued research and innovation, these scaffolds are poised to play a pivotal role in the next generation of therapeutic agents across multiple disease areas.

Review: [recent developments towards the construction of compound libraries and their application in drug and probe discovery; 86 refs.