Applications of the Pharmacophore Concept in Natural Product inspired Drug Design

Combining Molecular Docking and Pharmacophore Models Predicts Ligand Binding of Endocrine-Disrupting Chemicals to Nuclear Receptors

Nuclear receptors form a family of proteins capable of accommodating a wide variety of small molecules in their ligand binding domain, ranging from therapeutic compounds to endocrine-disrupting chemicals. The rapid identification of these compounds, especially within the latter category, is of paramount importance. Using data extracted from the CompTox Dashboard, an Environmental Protection Agency initiative, we assessed the effectiveness of a combination of molecular docking and pharmacophore models in identifying ligands binding to six nuclear receptors: androgen receptor, estrogen receptor alpha, estrogen receptor beta, glucocorticoid receptor, peroxisome proliferator-activated receptor gamma, and thyroid hormone receptor alpha. For each nuclear receptor, we selected a specifically designed and optimized in silico protocol that, in conjunction with experimental assays, can prioritize compounds for further evaluation to detect any potential toxicological concerns.

Identification of potential inhibitors targeting yellow fever virus helicase through ligand and structure-based computational studies

Yellow fever is a flavivirus having plus-sensed RNA which encodes a single polyprotein. Host proteases cut this polyprotein into seven nonstructural proteins including a vital NS3 protein. The present study aims to identify the most effective inhibitor against the helicase (NS3) using different advanced ligand and structure-based computational studies. A set of 300 ligands was selected against helicase by chemical structural similarity model, which are similar to S-adenosyl-l-cysteine using infiniSee. This tool screens billions of compounds through a similarity search from in-built chemical spaces (CHEMriya, Galaxi, KnowledgeSpace and REALSpace). The pharmacophore was designed from ligands in the library that showed same features. According to the sequence of ligands, six compounds (29, 87, 99, 116, 148, and 208) were taken for pharmacophore designing against helicase protein. Subsequently, compounds from the library which showed the best pharmacophore shared-features were docked using FlexX functionality of SeeSAR and their optibrium properties were analyzed. Afterward, their ADME was improved by replacing the unfavorable fragments, which resulted in the generation of new compounds. The selected best compounds (301, 302, 303 and 304) were docked using SeeSAR and their pharmacokinetics and toxicological properties were evaluated using SwissADME. The optimal inhibitor for yellow fever helicase was 2-amino-N-(4-(dimethylamino)thiazol-2-yl)-4-methyloxazole-5-carboxamide (302), which exhibits promising potential for drug development.

Silyl-Lipid Functionalized N-Acyl Homoserine Lactones as Modulators of Bacterial Cell-Cell Communication

We report silyl-lipid derivatives of N-acyl l-homoserine lactones (AHLs) that have nanomolar activities in LuxR-type quorum sensing receptors in Gram-negative bacterial pathogens. A collection of silyl-lipid AHLs were designed and synthesized to represent three general structural classes based on native AHL signals and synthetic LuxR-type receptor modulators. The synthetic routes feature straightforward hydrosilylation and aryl silylation reactions to access silyl-lipid groups that are not readily accessible in analogous all-carbon chemistry. Of the 17 compounds evaluated, eight silyl-lipid AHLs were identified with either nanomolar agonistic or submicromolar antagonistic activities in the LasR receptor from the common pathogen Pseudomonas aeruginosa using E. coli reporter gene assays. Several silyl-lipid AHL agonists retained high activities in LasR in a native P. aeruginosa reporter system and also were active in another related LuxR-type receptor, EsaR from Pantoea stewartii. Light scattering and computational experiments indicate that the silyl-lipid group can alter the aggregation capabilities and lipophilicities of AHLs relative to native all-carbon tails, engendering larger aggregate formation in water and higher lipophilicities on average. These properties, along with their strong activity profiles in LuxR-type receptors, suggest silyl-lipid AHLs could provide value as chemical probes to study the mechanisms of quorum sensing in Gram-negative bacteria and the roles of signal lipophilicity in this chemical communication process.

Exploring quinoline-type inhibitors of ergosterol biosynthesis: Binding mechanism investigation via molecular docking, pharmacophore mapping, and dynamics simulation approaches

Drug-resistant fungal infections pose a formidable challenge in healthcare, attributed to ergosterol production as a key mechanism of resistance. It is therefore imperative to target this pathway for effective therapeutic interventions. In this study, we have analyzed the binding mode of twelve quinoline derivatives known to be effective against various Candida species, Microsporum gypseum, and Cryptococcus neoformans. Employing molecular docking techniques, pharmacological modeling, and molecular dynamics, we have delved into interactions with Erg1, Erg11, and Erg24 proteins, crucial in ergosterol biosynthesis. Our analysis unveiled critical interactions that facilitate the docking and stabilization of C-2-substituted quinoline derivatives on these proteins, highlighting their potential as regulators of ergosterol synthesis. Furthermore, complexes formed with Erg1 … 8 (MIC = 125 μg/mL) and Erg24 … 4 (MIC = 62 μg/mL) showed higher affinity and stability during the docking process, pointing to their promising role as regulatory agents of these proteins. This in silico approach provides insights into potential pathways to combat drug-resistant fungal infections.

Ex situ fabrication and bioactivity characterization of Neem and Sage-infused bacterial cellulose membranes for sustainable antimicrobial applications

This study presents the ex situ development and characterization of bacterial cellulose (BC) membranes loaded with bioactive Sage and Neem extracts for enhanced antimicrobial applications. Utilizing discarded fruit waste as a cost-effective carbon source, BC production was optimized, yielding membranes with improved properties. Neem and Sage extracts, obtained via Soxhlet extraction, exhibited significant antibacterial activity against Escherichia coli and Staphylococcus aureus, with minimum inhibitory concentrations of 3.125 mg/mL and 25 mg/mL, respectively, for Neem extract, and 25 mg/mL and 50 mg/mL for Sage extract. These extracts (20 wt%) were successfully incorporated into BC membranes ex situ, resulting in BC-Neem (BC-N) and BC-Sage (BC-S) composites. Fourier-transform infrared spectroscopy (FTIR) confirmed the chemical interactions between the extracts and the BC matrix, revealing the introduction of new functional groups and enhancing the composite properties. Scanning electron microscopy (SEM) illustrated changes in morphology, indicating deeper penetration and attachment of the extracts within the BC structure. Quantitative analysis of water holding capacity demonstrated that BC-N and BC-S absorbed about 90 times water of their dry weight. Antibacterial assays through the colony-forming unit method showed that BC-N significantly inhibited S. aureus growth by 78 % and E. coli by 51 %, while BC-S exhibited a 48 % reduction against S. aureus. Agar disc-diffusion assay showed the formation of inhibition zones of 1.2 cm and 0.1 cm by BC-N against S. aureus and E. coli, respectively, in contrast to 0.2 cm and no inhibition by BC-S composite. These results highlight the potential of bioactive plant extract-loaded BC membranes as effective antimicrobial agents, offering a sustainable alternative to conventional materials in medical and food packaging applications.

Novel drug design and repurposing: An opportunity to improve translational research in cardiovascular diseases?

Drug repurposing is defined as the use of approved therapeutic drugs for indications different from those for which they were originally designed. Repositioning diminishes both the time and cost for drug development by omitting the discovery stage, the analysis of absorption, distribution, metabolism, and excretion routes, as well as the studies of the biochemical and physiological effects of a new compound. Besides, drug repurposing takes advantage of the increased bioinformatics knowledge and availability of big data biology. There are many examples of drugs with repurposed indications evaluated in in vitro studies, and in pharmacological, preclinical, or retrospective clinical analyses. Here, we briefly review some of the experimental strategies and technical advances that may improve translational research in cardiovascular diseases. We also describe exhaustive research from basic science to clinical studies that culminated in the final approval of new drugs and provide examples of successful drug repurposing in the field of cardiology.

A Chronicle Review of In-Silico Approaches for Discovering Novel Antimicrobial Agents to Combat Antimicrobial Resistance

Antimicrobial resistance (AMR) poses a foremost threat to global health, necessitating innovative strategies for discovering antimicrobial agents. This review explores the role and recent advances of in-silico techniques in identifying novel antimicrobial agents and combating AMR giving few briefings of recent case studies of AMR. In-silico techniques, such as homology modeling, virtual screening, molecular docking, pharmacophore modeling, molecular dynamics simulation, density functional theory, integrated machine learning, and artificial intelligence, are systematically reviewed for their utility in discovering antimicrobial agents. These computational methods enable the rapid screening of large compound libraries, prediction of drug-target interactions, and optimization of drug candidates. The review discusses integrating in-silico approaches with traditional experimental methods and highlights their potential to accelerate the discovery of new antimicrobial agents. Furthermore, it emphasizes the significance of interdisciplinary collaboration and data-sharing initiatives in advancing antimicrobial research. Through a comprehensive discussion of the latest developments in in-silico techniques, this review provides valuable insights into the future of antimicrobial research and the fight against AMR.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-024-01355-x.

Z. A, Alhazzani K. Insilico exploration C. koseri ATP synthase inhibitors by pharmacophore-based virtual screening, molecular docking and MD simulation

Citrobacter koseri is a gram-negative rod that causes infections in people who have significant comorbidities and are immunocompromised. Antibiotic-resistant strains are becoming more common, which complicates infection treatment and highlights the need for innovative, effective drugs to fight these resistant strains. The enzyme complex ATP synthase participates in the adenosine triphosphate (ATP) synthesis, the fundamental energy currency of cells. This study used Computer-Aided Drug Design approaches to identify potential inhibitors of C. koseri ATP synthase. SWISS-MODEL was used to predict the 3D structure of C. koseri ATP synthase. A ligand-based pharmacophore model was developed using chemical features of ampicillin. Following ligand-based virtual screening across nine databases, the 2043 screened hits were docked to the ATP synthase active site using the standard precision mode of the glide tool. Based on their binding affinities, the top ten compounds were selected for additional investigation. The binding affinities of the chosen compounds ranged from -10.021 to -8.452 kcal/mol. The top four compounds (PubChem-25230613, PubChem-74936833, CHEMBL263035, PubChem-44208924) with the best ADMET characteristics and binding modes were chosen. Thus, the feasible binding mechanisms of the selected compounds were subjected to stability analysis using the MD Simulation study, which revealed the compounds' stability as potent inhibitors within the protein binding pocket. This computational approach provides important insights into the rational design of novel therapeutics and emphasizes the importance of targeting essential metabolic pathways when combating antibiotic-resistant pathogens. Future experimental validation and optimization of the identified inhibitors is required to determine their efficacy and safety profiles for clinical use.

A systematic review of novel cannabinoids and their targets: Insights into the significance of structure in activity

To provide a comprehensive framework of the current information on the potency and efficacy of interaction between phyto- and synthetic cannabinoids and their respective receptors, an electronic search of the PubMed, Scopus, and EMBASE literature was performed. Experimental studies included reports of mechanistic data providing affinity, efficacy, and half-maximal effective concentration (EC50). Among the 108 included studies, 174 structures, and 16 targets were extracted. The most frequent ligands belonged to the miscellaneous category with 40.2% followed by phytocannabinoid-similar, indole-similar, and pyrrole-similar structures with an abundance of 17.8%, 16.6%, and 12% respectively. 64.8% of structures acted as agonists, 17.1 % appeared as inverse agonists, 10.8% as antagonists, and 7.2% of structures were reported with antagonist/inverse agonist properties. Our outcomes identify the affinity, EC50, and efficacy of the interactions between cannabinoids and their corresponding receptors and the subsequent response, evaluated in the available evidence. Considering structures' significance and very important effects of on the activities, the obtained results also provide clues to drug repurposing.

Hybrid Virtual Screening Approach to Predict Novel Natural Compounds against HIV-1 CCR5

BACKGROUND

Human immunodeficiency virus (HIV) infection continues to pose a major global health challenge. HIV entry into host cells via membrane fusion mediated by the viral envelope glycoprotein gp120/gp41 is a key step in the HIV life cycle. CCR5, expressed on CD4+ T cells and macrophages, acts as a coreceptor facilitating HIV-1 entry. The CCR5 antagonist maraviroc is used to treat HIV infection. However, it can cause adverse effects and has limitations such as only inhibiting CCR5-tropic viruses. There remains a need to develop alternative CCR5 inhibitors with improved safety profiles.

PROBLEM STATEMENT

Natural products may offer advantages over synthetic inhibitors including higher bioavailability, binding affinity, effectiveness, lower toxicity, and molecular diversity. However, screening the vast chemical space of natural compounds to identify novel CCR5 inhibitors presents challenges. This study aimed to address this gap through a hybrid ligand-based pharmacophore modeling and molecular docking approach to virtually screen large natural product databases.

METHODS

A reliable pharmacophore model was developed based on 311 known CCR5 antagonists and validated against an external data set. Five natural product databases containing over 306,000 compounds were filtered based on drug-likeness rules. The validated pharmacophore model screened the databases to identify 611 hits. Key residues of the CCR5 receptor crystal structure were identified for docking. The top hits were docked, and interactions were analyzed. Molecular dynamics simulations were conducted to examine complex stability. Computational prediction evaluated pharmacokinetic properties.

RESULTS

Three compounds exhibited similar interactions and binding energies to maraviroc. MD simulations demonstrated complex stability comparable to maraviroc. One compound showed optimal predicted absorption, minimal metabolism, and a lower likelihood of interactions than maraviroc.

CONCLUSION

This computational screening workflow identified three natural compounds with promising CCR5 inhibition and favorable pharmacokinetic profiles. One compound emerged as a lead based on bioavailability potential and minimal interaction risk. These findings present opportunities for developing alternative CCR5 antagonists and warrant further experimental investigation. Overall, the hybrid virtual screening approach proved effective for mining large natural product spaces to discover novel molecular entities with drug-like properties.

Complications and comorbidities associated with antineoplastic chemotherapy: Rethinking drug design and delivery for anticancer therapy

Despite the considerable advancements in chemotherapy as a cornerstone modality in cancer treatment, the prevalence of complications and pre-existing diseases is on the rise among cancer patients along with prolonged survival and aging population. The relationships between these disorders and cancer are intricate, bearing significant influence on the survival and quality of life of individuals with cancer and presenting challenges for the prognosis and outcomes of malignancies. Herein, we review the prevailing complications and comorbidities that often accompany chemotherapy and summarize the lessons to learn from inadequate research and management of this scenario, with an emphasis on possible strategies for reducing potential complications and alleviating comorbidities, as well as an overview of current preclinical cancer models and practical advice for establishing bio-faithful preclinical models in such complex context.

TRAF6 Inhibitors from Marine Compound Library: Pharmacophore, Virtual Screening, Fragment Replacement, ADMET, and Molecular Dynamics

TRAF6 is an E3 ubiquitin ligase that plays a crucial role in cell signaling. It is known that MMP is involved in tumor metastasis, and TRAF6 induces MMP-9 expression by binding to BSG. However, inhibiting TRAF6's ubiquitinase activity without disrupting the RING domain is a challenge that requires further research. To address this, we conducted computer-based drug screening to identify potential TRAF6 inhibitors. Using a ligand-receptor complex pharmacophore based on the inhibitor EGCG, known for its anti-tumor properties, we screened 52,765 marine compounds. After the molecular docking of 405 molecules with TRAF6, six compounds were selected for further analysis. By replacing fragments of non-binding compounds and conducting second docking, we identified two promising molecules, CMNPD9212-16 and CMNPD12791-8, with strong binding activity and favorable pharmacological properties. ADME and toxicity predictions confirmed their potential as TRAF6 inhibitors. Molecular dynamics simulations showed that CMNPD12791-8 maintained a stable structure with the target protein, comparable to EGCG. Therefore, CMNPD12791-8 holds promise as a potential inhibitor of TRAF6 for inhibiting tumor growth and metastasis.

NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer

Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO models were trained under different task scenarios by recognizing syntactic patterns and structure-property relationships. We further explored the performance of NIMO in structure-guided, activity-oriented, and pocket-based molecule generation tasks. Our results show that NIMO had excellent performance for molecule generation from scratch and structure optimization from a scaffold.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies

During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.

Curcumin, its derivatives, and their nanoformulations: Revolutionizing cancer treatment

Curcumin is a natural compound derived from turmeric and can target malignant tumor molecules involved in cancer propagation. It has potent antioxidant activity, but its effectiveness is limited due to poor absorption and rapid elimination from the body. Various curcumin derivatives have also shown anticancer potential in in-vitro and in-vivo models. Curcumin can target multiple signaling pathways involved in cancer development/progression or induce cancer cell death through apoptosis. In addition, curcumin and its derivatives could also enhance the effectiveness of conventional chemotherapy, radiation therapy and reduce their associated side effects. Lately, nanoparticle-based delivery systems are being developed/explored to overcome the challenges associated with curcumin's delivery, increasing its overall efficacy. The use of an imaging system to track these formulations could also give beneficial information about the bioavailability and distribution of the nano-curcumin complex. In conclusion, curcumin holds significant promise in the fight against cancer, especially in its nanoform, and could provide precise delivery to cancer cells without affecting normal healthy cells.

Computational approaches for modeling and structural design of biological systems: A comprehensive review

The convergence of biology and computational science has ushered in a revolutionary era, revolutionizing our understanding of biological systems and providing novel solutions to global problems. The field of genetic engineering has facilitated the manipulation of genetic codes, thus providing opportunities for the advancement of innovative disease therapies and environmental enhancements. The emergence of bio-molecular simulation represents a significant advancement in this particular field, as it offers the ability to gain microscopic insights into molecular-level biological processes over extended periods. Biomolecular simulation plays a crucial role in advancing our comprehension of organismal mechanisms by establishing connections between molecular structures, interactions, and biological functions. The field of computational biology has demonstrated its significance in deciphering intricate biological enigmas through the utilization of mathematical models and algorithms. The process of decoding the human genome has resulted in the advancement of therapies for a wide range of genetic disorders, while the simulation of biological systems contributes to the identification of novel pharmaceutical compounds. The potential of biomolecular simulation and computational biology is vast and limitless. As the exploration of the underlying principles that govern living organisms progresses, the potential impact of this understanding on cancer treatment, environmental restoration, and other domains is anticipated to be transformative. This review examines the notable advancements achieved in the field of computational biology, emphasizing its potential to revolutionize the comprehension and enhancement of biological systems.

Identification of Novel β-Tubulin Inhibitors Using a Combined In Silico/In Vitro Approach

Due to their potential as leads for various therapeutic applications, including as antimitotic and antiparasitic agents, the development of tubulin inhibitors offers promise for drug discovery. In this study, an in silico pharmacophore-based virtual screening approach targeting the colchicine binding site of β-tubulin was employed. Several structure- and ligand-based models for known tubulin inhibitors were generated. Compound databases were virtually screened against the models, and prioritized hits from the SPECS compound library were tested in an in vitro tubulin polymerization inhibition assay for their experimental validation. Out of the 41 SPECS compounds tested, 11 were active tubulin polymerization inhibitors, leading to a prospective true positive hit rate of 26.8%. Two novel inhibitors displayed IC50 values in the range of colchicine. The most potent of which was a novel acetamide-bridged benzodiazepine/benzimidazole derivative with an IC50 = 2.9 μM. The screening workflow led to the identification of diverse inhibitors active at the tubulin colchicine binding site. Thus, the pharmacophore models show promise as valuable tools for the discovery of compounds and as potential leads for the development of cancer therapeutic agents.

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.

Identification of Promising Sulfonamide Chalcones as Inhibitors of SARS-CoV-2 3CLpro through Structure-Based Virtual Screening and Experimental Approaches

3CLpro is a viable target for developing antiviral therapies against the coronavirus. With the urgent need to find new possible inhibitors, a structure-based virtual screening approach was developed. This study recognized 75 pharmacologically bioactive compounds from our in-house library of 1052 natural product-based compounds that satisfied drug-likeness criteria and exhibited good bioavailability and membrane permeability. Among these compounds, three promising sulfonamide chalcones were identified by combined theoretical and experimental approaches, with SWC423 being the most suitable representative compound due to its competitive inhibition and low cytotoxicity in Vero E6 cells (EC50 = 0.89 ± 0.32 μM; CC50 = 25.54 ± 1.38 μM; SI = 28.70). The binding and stability of SWC423 in the 3CLpro active site were investigated through all-atom molecular dynamics simulation and fragment molecular orbital calculation, indicating its potential as a 3CLpro inhibitor for further SARS-CoV-2 therapeutic research. These findings suggested that inhibiting 3CLpro with a sulfonamide chalcone such as SWC423 may pave the effective way for developing COVID-19 treatments.

A Deadly Liaison between Oxidative Injury and p53 Drives Methyl-Gallate-Induced Autophagy and Apoptosis in HCT116 Colon Cancer Cells

Methyl gallate (MG), which is a gallotannin widely found in plants, is a polyphenol used in traditional Chinese phytotherapy to alleviate several cancer symptoms. Our studies provided evidence that MG is capable of reducing the viability of HCT116 colon cancer cells, while it was found to be ineffective on differentiated Caco-2 cells, which is a model of polarized colon cells. In the first phase of treatment, MG promoted both early ROS generation and endoplasmic reticulum (ER) stress, sustained by elevated PERK, Grp78 and CHOP expression levels, as well as an upregulation in intracellular calcium content. Such events were accompanied by an autophagic process (16-24 h), where prolonging the time (48 h) of MG exposure led to cellular homeostasis collapse and apoptotic cell death with DNA fragmentation and p53 and γH2Ax activation. Our data demonstrated that a crucial role in the MG-induced mechanism is played by p53. Its level, which increased precociously (4 h) in MG-treated cells, was tightly intertwined with oxidative injury. Indeed, the addition of N-acetylcysteine (NAC), which is a ROS scavenger, counteracted the p53 increase, as well as the MG effect on cell viability. Moreover, MG promoted p53 accumulation into the nucleus and its inhibition by pifithrin-α (PFT-α), which is a negative modulator of p53 transcriptional activity, enhanced autophagy, increased the LC3-II level and inhibited apoptotic cell death. These findings provide new clues to the potential action of MG as a possible anti-tumor phytomolecule for colon cancer treatment.

Design, synthesis, and biological evaluation of artemyrianolide H derivatives as potential antihepatoma agents

Artemyrianolide H (AH) is a germacrene-type sesquiterpenolid isolated from Artemisia myriantha, and showed potent cytotoxicity against three human hepatocellular carcinoma cell lines HepG2, Huh7, and SK-Hep-1 with IC₅₀ values of 10.9, 7.2, and 11.9 µM, respectively. To reveal structure-activity relationship, 51 artemyrianolide H derivatives including 19 dimeric analogs were designed, synthesized, and assayed for their cytotoxicity against three human hepatoma cell lines. Among them, 34 compounds were more active than artemyrianolide H and sorafenib on the three cell lines. Especially, compound 25 exhibited the most promising activity with IC₅₀ values of 0.7 (HepG2), 0.6 (Huh7), and 1.3 µM (SK-Hep-1), which were 15.5, 12.0, and 9.2-fold higher than that of AH and 16.4, 16.3 and 17.5-fold higher than that of sorafenib. Cytotoxicity evaluation on normal human liver cell lines (THLE-2) demonstrated good safety profile of compound 25 with SI of 1.9 (HepG2), 2.2 (Huh 7) and 1.0 (SK-Hep1). Further studies revealed that compound 25 dose-dependently arrested cells at G2/M phase which was correlated with the up-regulation of both cyclin B1 and p-CDK1, and induced apoptosis through the activation of mitochondrial pathways in HepG2 cells. In addition, the migratory and invasive abilities in HepG2 cells after treatment with 1.5 μM of compound 25 were decreased by 89% and 86% with the increase of E-cadherin expression accompanied by the decrease of N-cadherin, vimentin expression. Bioinformatics analysis based on machine learning predicted that PDGFRA and MAP2K2 might be acting targets of compound 25, and SPR assays demonstrated compound 25 were bound with PDGFRA and MAP2K2 with K_D value of 0.168 nM, and 8.49 µM, respectively. This investigation proposed that compound 25 might be considered as a promising lead compound for the development of antihepatoma candidate.

Combination of In Silico and In Vitro Screening to Identify Novel Glutamate Carboxypeptidase II Inhibitors

Glutamate carboxypeptidase II (GCPII) is a metalloprotease implicated in neurological diseases and prostate oncology. While several classes of potent GCPII-specific inhibitors exist, the development of novel active scaffolds with different pharmacological profiles remains a challenge. Virtual screening followed by in vitro testing is an effective means for the discovery of novel active compounds. Structure- and ligand-based pharmacophore models were created based on a dataset of known GCPII-selective ligands. These models were used in a virtual screening of the SPECS compound library (∼209.000 compounds). Fifty top-scoring virtual hits were further experimentally tested for their ability to inhibit GCPII enzymatic activity in vitro. Six hits were found to have moderate to high inhibitory potency with the best virtual hit, a modified xanthene, inhibiting GCPII with an IC50 value of 353 ± 24 nM. The identification of this novel inhibitory scaffold illustrates the applicability of pharmacophore-based modeling for the discovery of GCPII-specific inhibitors.

Computational Approaches to the Rational Design of Tubulin-Targeting Agents

Microtubules are highly dynamic polymers of α,β-tubulin dimers which play an essential role in numerous cellular processes such as cell proliferation and intracellular transport, making them an attractive target for cancer and neurodegeneration research. To date, a large number of known tubulin binders were derived from natural products, while only one was developed by rational structure-based drug design. Several of these tubulin binders show promising in vitro profiles while presenting unacceptable off-target effects when tested in patients. Therefore, there is a continuing demand for the discovery of safer and more efficient tubulin-targeting agents. Since tubulin structural data is readily available, the employment of computer-aided design techniques can be a key element to focus on the relevant chemical space and guide the design process. Due to the high diversity and quantity of structural data available, we compiled here a guide to the accessible tubulin-ligand structures. Furthermore, we review different ligand and structure-based methods recently used for the successful selection and design of new tubulin-targeting agents.

Virtual Screening-Based Drug Development for the Treatment of Nervous System Diseases

The incidence rate of nervous system diseases has increased in recent years. Nerve injury or neurodegenerative diseases usually cause neuronal loss and neuronal circuit damage, which seriously affect motor nerve and autonomic nervous function. Therefore, safe and effective treatment is needed. As traditional drug research becomes slower and more expensive, it is vital to enlist the help of cutting- edge technology. Virtual screening (VS) is an attractive option for the identification and development of promising new compounds with high efficiency and low cost. With the assistance of computer- aided drug design (CADD), VS is becoming more and more popular in new drug development and research. In recent years, it has become a reality to transform non-neuronal cells into functional neurons through small molecular compounds, which provides a broader application prospect than transcription factor-mediated neuronal reprogramming. This review mainly summarizes related theory and technology of VS and the drug research and development using VS technology in nervous system diseases in recent years, and focuses more on the potential application of VS technology in neuronal reprogramming, thus facilitating new drug design for both prevention and treatment of nervous system diseases.

In silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models

In silico profiling is used in identification of active compounds and guide rational use of traditional medicines. Previous studies on Ethiopian indigenous aloes focused on documentation of phytochemical compositions and traditional uses. In this study, ADMET and drug-likeness properties of phytochemicals from Ethiopian indigenous aloes were evaluated, and pharmacophore-based profiling was done using Discovery Studio to predict therapeutic targets. The targets were examined using KEGG pathway, gene ontology and network analysis. Using random-walk with restart algorithm, network propagation was performed in CODA network to find diseases associated with the targets. As a result, 82 human targets were predicted and found to be involved in several molecular functions and biological processes. The targets also were linked to various cancers and diseases of immune system, metabolism, neurological system, musculoskeletal system, digestive system, hematologic, infectious, mouth and dental, and congenital disorder of metabolism. 207 KEGG pathways were enriched with the targets, and the main pathways were metabolism of steroid hormone biosynthesis, lipid and atherosclerosis, chemical carcinogenesis, and pathways in cancer. In conclusion, in silico target fishing and network analysis revealed therapeutic activities of the phytochemicals, demonstrating that Ethiopian indigenous aloes exhibit polypharmacology effects on numerous genes and signaling pathways linked to many diseases.

Pharmacophore Mapping Combined with dbCICA Reveal New Structural Features for the Development of Novel Ligands Targeting α4β2 and α7 Nicotinic Acetylcholine Receptors

The neuronal nicotinic acetylcholine receptors (nAChRs) belong to the ligand-gated ion channel (GLIC) group, presenting a crucial role in several biological processes and neuronal disorders. The α4β2 and α7 nAChRs are the most abundant in the central nervous system (CNS), being involved in challenging diseases such as epilepsy, Alzheimer's disease, schizophrenia, and anxiety disorder, as well as alcohol and nicotine dependencies. In addition, in silico-based strategies may contribute to revealing new insights into drug design and virtual screening to find new drug candidates to treat CNS disorders. In this context, the pharmacophore maps were constructed and validated for the orthosteric sites of α4β2 and α7 nAChRs, through a docking-based Comparative Intermolecular Contacts Analysis (dbCICA). In this sense, bioactive ligands were retrieved from the literature for each receptor. A molecular docking protocol was developed for all ligands in both receptors by using GOLD software, considering GoldScore, ChemScore, ASP, and ChemPLP scoring functions. Output GOLD results were post-processed through dbCICA to identify critical contacts involved in protein-ligand interactions. Moreover, Crossminer software was used to construct a pharmacophoric map based on the most well-behaved ligands and negative contacts from the dbCICA model for each receptor. Both pharmacophore maps were validated by using a ROC curve. The results revealed important features for the ligands, such as the presence of hydrophobic regions, a planar ring, and hydrogen bond donor and acceptor atoms for α4β2. Parallelly, a non-planar ring region was identified for α7. These results can enable fragment-based drug design (FBDD) strategies, such as fragment growing, linking, and merging, allowing an increase in the activity of known fragments. Thus, our results can contribute to a further understanding of structural subunits presenting the potential for key ligand-receptor interactions, favoring the search in molecular databases and the design of novel ligands.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer's disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

Development of 3D-QSAR and pharmacophoric models to design new anti-Trypanosoma cruzi agents based on 2-aryloxynaphthoquinone scaffold

In this work we have collected a set of 30 trypanosomicidal naphthoquinones and developed pharmacophoric and 3D-QSAR models as tools for the design of new potential anti-Chagasic compounds. Firstly, qualitative information was obtained from SAR and pharmacophoric models identifying some fragments around the 2-aryloxynaphthoquinone scaffold important for the antiparasitic activity. Then, 3D-QSAR CoMFA and CoMSIA models were developed. The models showed adequate statistical parameters where the steric, electrostatic, and hydrophobic features explain the trypanosomicidal effect. Therefore, to validate our models, we carried out the design, synthesis, and biological evaluation on T. cruzi epimastigotes of five new compounds (33a-e). According to CoMFA model, three out of five compounds showed pIC₅₀ values within one logarithmic unit of deviation. The two compounds that did not fit the predictions were those with high lipophilicity, which agreed with the SAR and pharmacophore models. Docking and molecular dynamic studies were performed on T. cruzi trypanothione reductase, in a proposed binding site for this type of naphthoquinone. Interestingly, 33a-e showed the same interaction pattern as a naphthoquinone inhibitor (2). Finally, predicted drug-likeness properties indicated that 33a-e have optimal oral bioavailability. Thus, this study provides new in silico models for obtaining novel trypanosomicidal compounds.

Synthesis, docking study, and structure-activity relationship of novel niflumic acid derivatives acting as anticancer agents by inhibiting VEGFR or EGFR tyrosine kinase activities

A new series of niflumic acid (NF) derivatives were synthesized by esterification of (NF) to give ester compound 1, which was treated with hydrazine hydrate to produce (NF) hydrazide 2. Hydrazine-1-carboxamide compounds (3A–C), and hydrazine-1-carbothioamide derivatives (4A–D) were synthesized by treatment of (NF) hydrazide with phenyl isocyanate, and phenyl isothiocyanate derivatives, respectively. The cyclization of (4B–D) and (3B) was achieved using NaOH solution to produce 1,2,4-triazole derivatives (5A–C) and 6, respectively. The prepared compounds were characterized using IR, 1HNMR, 13CNMR, and MS (ESI) spectroscopy. A molecular docking study was performed to evaluate the binding affinity of the synthesized compounds against EGFR and VEGFR kinase domains which revealed that compounds 3B, and 4A had the best binding energy (-7.87, and -7.33 kcal/mol, respectively) against VEGFR, while compound 5A had the best binding energy (-7.95 kcal/mol) against EGFR. The biological investigation results indicated that all the tested compounds caused cell killing in the two cancer cell lines (Hep G2 and A549) studied, with compound 4C being the most cytotoxic, as well as being cancer selective. Additionally, compound 4C-treated Hep G2 cells were arrested at the S and G2/M cell cycle phases. Cytotoxicity of compound 4C was attributed to apoptosis as determined by flow cytometry and qRT-PCR results of the apoptosis markers p53, BAX, and caspase-3. Finally, compound 4C inhibited VEGFR kinase activity, while compound 5B inhibited EGFR kinase activity. In conclusion, the novel (NF) derivatives are potent anticancer agents, inhibiting cell proliferation by inhibiting EGFR and VEGFR tyrosine kinase enzymes.

Drug Design by Pharmacophore and Virtual Screening Approach

Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

Antimicrobial Random Peptide Mixtures Eradicate Acinetobacter baumannii Biofilms and Inhibit Mouse Models of Infection

Antibiotic resistance is one of the greatest crises in human medicine. Increased incidents of antibiotic resistance are linked to clinical overuse and overreliance on antibiotics. Among the ESKAPE pathogens, Acinetobacter baumannii, especially carbapenem-resistant isolates, has emerged as a significant threat in the context of blood, urinary tract, lung, and wound infections. Therefore, new approaches that limit the emergence of antibiotic resistant A. baumannii are urgently needed. Recently, we have shown that random peptide mixtures (RPMs) are an attractive alternative class of drugs to antibiotics with strong safety and pharmacokinetic profiles. RPMs are antimicrobial peptide mixtures produced by incorporating two amino acids at each coupling step, rendering them extremely diverse but still defined in their overall composition, chain length, and stereochemistry. The extreme diversity of RPMs may prevent bacteria from evolving resistance rapidly. Here, we demonstrated that RPMs rapidly and efficiently kill different strains of A. baumannii, inhibit biofilm formation, and disrupt mature biofilms. Importantly, RPMs attenuated bacterial burden in mouse models of acute pneumonia and soft tissue infection and significantly reduced mouse mortality during sepsis. Collectively, our results demonstrate RPMs have the potential to be used as powerful therapeutics against antibiotic-resistant A. baumannii.

Application of the alchemical transfer and potential of mean force methods to the SAMPL8 host-guest blinded challenge

We report the results of our participation in the SAMPL8 GDCC Blind Challenge for host-guest binding affinity predictions. Absolute binding affinity prediction is of central importance to the biophysics of molecular association and pharmaceutical discovery. The blinded SAMPL series have provided an important forum for assessing the reliability of binding free energy methods in an objective way. In this challenge, we employed two binding free energy methods, the newly developed alchemical transfer method (ATM) and the well-established potential of mean force (PMF) physical pathway method, using the same setup and force field model. The calculated binding free energies from the two methods are in excellent quantitative agreement. Importantly, the results from the two methods were also found to agree well with the experimental binding affinities released subsequently, with R values of 0.89 (ATM) and 0.83 (PMF). These results were ranked among the best of the SAMPL8 GDCC challenge and second only to those obtained with the more accurate AMOEBA force field. Interestingly, the two host molecules included in the challenge (TEMOA and TEETOA) displayed distinct binding mechanisms, with TEMOA undergoing a dehydration transition whereas guest binding to TEETOA resulted in the opening of the binding cavity that remains essentially dry during the process. The coupled reorganization and hydration equilibria observed in these systems is a useful prototype for the study of these phenomena often observed in the formation of protein-ligand complexes. Given that the two free energy methods employed here are based on entirely different thermodynamic pathways, the close agreement between the two and their general agreement with the experimental binding free energies are a testament to the high quality and precision achieved by theory and methods. The study provides further validation of the novel ATM binding free energy estimation protocol and paves the way to further extensions of the method to more complex systems.

Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design

Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.

Role of Fyn Kinase Inhibitors in Switching Neuroinflammatory Pathways

Fyn kinase is a member of the Src non-receptor tyrosine kinase family. Fyn is involved in multiple signaling pathways extending from cell proliferation and differentiation to cell adhesion and cell motility, and it has been found to be overexpressed in various types of cancers. In the central nervous system, Fyn exerts several different functions such as axon-glial signal transduction, oligodendrocyte maturation and myelination, and it is implicated in neuroinflammatory processes. Based on these premises, Fyn emerges as an attractive target in cancer and neurodegenerative disease therapy, particularly Alzheimer disease (AD), based on its activation by Aβ via cellular prion protein and its interaction with tau protein. However, Fyn is also a challenging target since the Fyn inhibitors discovered so far, due to the relevant homology of Fyn with other kinases, suffer from off-target effects. This review covers the efforts performed in the last decade to identify and optimize small molecules that effectively inhibit Fyn, both in enzymatic and in cell assays, including drug repositioning practices, as an opportunity of therapeutic intervention in neurodegeneration.

Current Knowledge Regarding Pharmacological Profile and Chemical Constituents of Gynura procumbens

Gynura procumbens (Lour.) Merr. is a well-known plant used in the folkloric medicine in tropical Asian countries. The plant is prevalently employed by traditional healers in the treatment of diabetes, cancer, hypertension, inflammation, fever and skin disorders. Several scientific studies reported that, Gynura procumbens possesses considerable therapeutic value for the development of emerging treatment options. The diverse pharmacological effects of this plant are attributed to its vast phytoconstituent content. Different chemical classes including alkaloids, flavonoids, phenolics, steroids, proteins and polysaccharides have been isolated from this plant. In this review, we tried to explore the different aspects of Gynura procumbens as an established medicinal plant. The data gathered here give an indication that the plant Gynura procumbens is a good natural source of chemical compounds with different types of pharmacological actions and these chemical compounds can be used as model for the development of de novo therapeutic agents.

Computational study to discover potent phytochemical inhibitors against drug target, squalene synthase from Leishmania donovani

AIMS

The parasite, Leishmania donovani is responsible for lethal visceral leishmaniasis (VL) in humans. There is a need to investigate novel medicines as antileishmanial drugs, as medication currently introduced for leishmaniasis may cause resistance, serious side-effects, chemical instability and high cost. Therefore, this computational study was designed to explore potential phytochemical inhibitors against Leishmania donovani squalene synthase (LdSQS) enzyme, a drug target.

MAIN METHODS

Multiple sequence alignment was carried to detect conserved regions across squalene synthases from different Leishmania spp. Their evolutionary relationships were studied by generating phylogenetic tree. Homology modeling method was used to build a three dimensional model of the protein. The validated model was explored by docking simulation with the phytochemicals of interest to identify the most potent inhibitors. Two reported inhibitors were used as references in the virtual screening. The top hit compounds (binding energy less than -9 kcal/mol) were further subjected to intermolecular interaction analysis, pharmacophore modeling, pharmacokinetic and toxicity prediction.

KEY FINDINGS

Seven phytochemicals displayed binding energies less than -9 kcal/mol hence demonstrating ability to be strongly bound to the active site of LdSQS to inhibit the enzymatic activity. Ancistrotanzanine B demonstrated the lowest binding affinity of -9.83 kcal/mol superior to reported inhibitors in literature. Conserved two aspartate rich regions and two signatory motifs were found in the L. donovani squalene synthase by multiple sequence alignment. In addition, study of pharmacophore modeling confirmed that top hit phytochemicals and the reported inhibitor (E5700) share common chemical features for their biochemical interaction with LdSQS. Among seven phytochemicals, 3-O-methyldiplacol showed admissible physicochemical, pharmacokinetic and toxicity predictions compared to the reported inhibitors. All seven phytochemicals satisfied in silico prediction criteria for oral bioavailability.

SIGNIFICANCE

Based on the current study, these hits can be further structurally optimized and validated under laboratory conditions to develop antileishmanial drugs.

Structure-based molecular modeling in SAR analysis and lead optimization

In silico methods like molecular docking and pharmacophore modeling are established strategies in lead identification. Their successful application for finding new active molecules for a target is reported by a plethora of studies. However, once a potential lead is identified, lead optimization, with the focus on improving potency, selectivity, or pharmacokinetic parameters of a parent compound, is a much more complex task. Even though in silico molecular modeling methods could contribute a lot of time and cost-saving by rationally filtering synthetic optimization options, they are employed less widely in this stage of research. In this review, we highlight studies that have successfully used computer-aided SAR analysis in lead optimization and want to showcase sound methodology and easily accessible in silico tools for this purpose.

Applications of the Pharmacophore Concept in Natural Product inspired Drug Design

Pharmacophore-based techniques are nowadays an important part of many computer-aided drug design workflows and have been successfully applied for tasks such as virtual screening, lead optimization and de novo design. Natural products, on the other hand, can serve as a valuable source for unconventional molecular scaffolds that stimulate ideas for novel lead compounds in a more diverse chemical space that does not follow the rules of traditional medicinal chemistry. The first part of this review provides a brief introduction to the pharmacophore concept, the methods for pharmacophore model generation, and their applications. The second, concluding part, presents examples for recent, pharmacophore method related research in the field of natural product chemistry. The selected examples show, that pharmacophore-based methods which get mainly applied on synthetic drug-like molecules work equally well in the realm of natural products and thus can serve as a valuable tool for researchers in the field of natural product inspired drug design.