Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

Research into how carvacrol and metformin affect several human proteins in a hyperglycemic condition: A comparative study in silico and in vitro

Carvacrol (CV) is an organic compound found in the essential oils of many aromatic herbs. It is nearly unfeasible to analyze all the current human proteins for a query ligand using in vitro and in vivo methods. This study aimed to clarify whether CV possesses an anti-diabetic feature via Docking-based inverse docking and molecular dynamic (MD) simulation and in vitro characterization against a set of novel human protein targets. Herein, the best poses of CV docking simulations according to binding energy ranged from -7.9 to -3.5 (kcal/mol). After pathway analysis of the protein list through GeneMANIA and WebGestalt, eight interacting proteins (DPP4, FBP1, GCK, HSD11β1, INSR, PYGL, PPARA, and PPARG) with CV were determined, and these proteins exhibited stable structures during the MD process with CV. In vitro application, statistically significant results were achieved only in combined doses with CV or metformin. Considering all these findings, PPARG and INSR, among these target proteins of CV, are FDA-approved targets for treating diabetes. Therefore, CV may be on its way to becoming a promising therapeutic compound for treating Diabetes Mellitus (DM). Our outcomes expose formerly unexplored potential target human proteins, whose association with diabetic disorders might guide new potential treatments for DM.

Decoding the Structural Diversity: A New Horizon in Antimicrobial Prospecting and Mechanistic Investigation

The escalating crisis of antimicrobial resistance (AMR) underscores the urgent need for novel antimicrobials. One promising strategy is the exploration of structural diversity, as diverse structures can lead to diverse biological activities and mechanisms of action. This review delves into the role of structural diversity in antimicrobial discovery, highlighting its influence on factors such as target selectivity, binding affinity, pharmacokinetic properties, and the ability to overcome resistance mechanisms. We discuss various approaches for exploring structural diversity, including combinatorial chemistry, diversity-oriented synthesis, and natural product screening, and provide an overview of the common mechanisms of action of antimicrobials. We also describe techniques for investigating these mechanisms, such as genomics, proteomics, and structural biology. Despite significant progress, several challenges remain, including the synthesis of diverse compound libraries, the identification of active compounds, the elucidation of complex mechanisms of action, the emergence of AMR, and the translation of laboratory discoveries to clinical applications. However, emerging trends and technologies, such as artificial intelligence, high-throughput screening, next-generation sequencing, and open-source drug discovery, offer new avenues to overcome these challenges. Looking ahead, we envisage an exciting future for structural diversity-oriented antimicrobial discovery, with opportunities for expanding the chemical space, harnessing the power of nature, deepening our understanding of mechanisms of action, and moving toward personalized medicine and collaborative drug discovery. As we face the continued challenge of AMR, the exploration of structural diversity will be crucial in our search for new and effective antimicrobials.

A guide to the use of bioassays in exploration of natural resources

Bioassays are the main tool to decipher bioactivities from natural resources thus their selection and quality are critical for optimal bioprospecting. They are used both in the early stages of compounds isolation/purification/identification, and in later stages to evaluate their safety and efficacy. In this review, we provide a comprehensive overview of the most common bioassays used in the discovery and development of new bioactive compounds with a focus on marine bioresources. We present a comprehensive list of practical considerations for selecting appropriate bioassays and discuss in detail the bioassays typically used to explore antimicrobial, antibiofilm, cytotoxic, antiviral, antioxidant, and anti-ageing potential. The concept of quality control and bioassay validation are introduced, followed by safety considerations, which are critical to advancing bioactive compounds to a higher stage of development. We conclude by providing an application-oriented view focused on the development of pharmaceuticals, food supplements, and cosmetics, the industrial pipelines where currently known marine natural products hold most potential. We highlight the importance of gaining reliable bioassay results, as these serve as a starting point for application-based development and further testing, as well as for consideration by regulatory authorities.

Design and Diversity Analysis of Chemical Libraries in Drug Discovery

Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.

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.

Synthesis of fusidane triterpenoid Mannich bases as potential antibacterial and antitumor agents

Mannich bases (8 examples) were synthesized via aminomethylation of fusidane propargyl esters. In vitro antimicrobial screening against key ESKAPE pathogens showed that the fusidic acid based Mannich products exhibit a high antimicrobial effect against Gram-positive bacteria Staphylococcus aureus and the fungus Cryptococcus neoformans. Moreover, the cytotoxic effect of fusidic acid and its analogs, which showed high antibacterial activity, was determined by MTT assay on cancer HepG2, HCT-116, SH-SY5Y, MCF-7, A549 and conditionally normal cells HEK293. A remarkable cytotoxic activity of fusidic acid propargyl ester and its aminomethylene derivatives against cancer and nontumoral HEK293 cells with IC50 values within 4.2-25 µM was found.

Virtual Insights into Natural Compounds as Potential 5α-Reductase Type II Inhibitors: A Structure-Based Screening and Molecular Dynamics Simulation Study

Androgenic alopecia (AGA) is a dermatological disease with psychosocial consequences for those who experience hair loss. AGA is linked to an increase in androgen levels caused by an excess of dihydrotestosterone in blood capillaries produced from testosterone by 5α-reductase type II (5αR2), which is expressed in scalp hair follicles; 5αR2 activity and dihydrotestosterone levels are elevated in balding scalps. The diverse health benefits of flavonoids have been widely reported in epidemiological studies, and research interest continues to increase. In this study, a virtual screening approach was used to identify compounds that interact with active site residues of 5αR2 by screening a library containing 241 flavonoid compounds. Here, we report two potent flavonoid compounds, eriocitrin and silymarin, that interacted strongly with 5αR2, with binding energies of -12.1 and -11.7 kcal/mol, respectively, which were more significant than those of the control, finasteride (-11.2 kcal/mol). Molecular dynamic simulations (200 ns) were used to optimize the interactions between compounds and 5αR2 and revealed that the interaction of eriocitrin and silymarin with 5αR2 was stable. The study shows that eriocitrin and silymarin provide developmental bases for novel 5αR2 inhibitors for the management of AGA.

Challenges in natural product-based drug discovery assisted with in silico-based methods

The application of traditional medicine by humans for the treatment of ailments as well as improving the quality of life far outdates recorded history. To date, a significant percentage of humans, especially those living in developing/underprivileged communities still rely on traditional medicine for primary healthcare needs. In silico-based methods have been shown to play a pivotal role in modern pharmaceutical drug discovery processes. The application of these methods in identifying natural product (NP)-based hits has been successful. This is very much observed in many research set-ups that use rationally in silico-based methods in combination with experimental validation techniques. The combination has rendered the use of in silico-based approaches even more popular and successful in the investigation of NPs. However, identifying and proposing novel NP-based hits for experimental validation comes with several challenges such as the availability of compounds by suppliers, the huge task of separating pure compounds from complex mixtures, the quantity of samples available from the natural source to be tested, not to mention the potential ecological impact if the natural source is exhausted. Because most peer-reviewed publications are biased towards "positive results", these challenges are generally not discussed in publications. In this review, we highlight and discuss these challenges. The idea is to give interested scientists in this field of research an idea of what they can come across or should be expecting as well as prompting them on how to avoid or fix these issues.

An integrative computational approach for the identification of dual inhibitors of isocitrate dehydrogenase 1 and 2 from phytocompounds of Phyllantus amarus

Genetic alterations of the genes encoding the isocitrate dehydrogenase (IDH) enzymes have been identified in about 20% of acute myeloid leukemia (AML) cases as well as many other forms of cancers. Notable among these alterations are the neomorphic IDH1_R132H and IDH2_R140Q mutations which lead to the production of an oncometabolite. Hence, their inhibition is widely considered a therapeutic strategy in the treatment of many cancers. While many inhibitors of the mutant enzymes have been developed, an inhibitor that is capable of co-inhibiting both enzymes are currently lacking while drug resistance has also limited the clinical usage of previously identified mono inhibitors. Consequently, this study employed molecular modeling approaches, such as molecular docking, molecular mechanics generalized Born Surface area (MM/GBSA), molecular dynamics (MD) simulation, and density functional theory (DFT) analysis to identify potential dual inhibitors of the previously mentioned mutant IDH1/2 from the phytocompounds of Phyllantus amarus. Of the 31 phytocompounds identified, 20 showed good binding affinities for both IDH1 _R132H and IDH2 _R140Q (ranging from -5.2 Kca/mol to -9.6 Kcal/mol) and had desirable pharmacokinetic properties. However, ellagic acid and pinoresinol possessed better pharmacokinetic properties, rendering suitable hits. Investigation of the behavior of the IDH1_R132H and IDH2_R140Q complexes with ellagic acid and pinoresinol via the RMSD, RMSF, and contact map analyses showed that all the complexes-maintained stability throughout the simulation time. Ultimately, ellagic acid and pinoresinol were identified as promising hits for the development of IDH1_R132H and IDH2_R140Q dual inhibitors. However, further experimental studies are needed to confirm their potential as therapies.Communicated by Ramaswamy H. Sarma.

Profiling the Antidiabetic Potential of Compounds Identified from Fractionated Extracts of Entada africana toward Glucokinase Stimulation: Computational Insight

Glucokinase plays an important role in regulating the blood glucose level and serves as an essential therapeutic target in type 2 diabetes management. Entada africana is a medicinal plant and highly rich source of bioactive ligands with the potency to develop new target drugs for glucokinase such as diabetes and obesity. Therefore, the study explored a computational approach to predict identified compounds from Entada africana following its intermolecular interactions with the allosteric binding site of the enzymes. We retrieved the three-dimensional (3D) crystal structure of glucokinase (PDB ID: 4L3Q) from the online protein data bank and prepared it using the Maestro 13.5, Schrödinger Suite 2022-3. The compounds identified were subjected to ADME, docking analysis, pharmacophore modeling, and molecular simulation. The results show the binding potential of the identified ligands to the amino acid residues, thereby suggesting an interaction of the amino acids with the ligand at the binding site of the glucokinase activator through conventional chemical bonds such as hydrogen bonds and hydrophobic interactions. The compatibility of the molecules was highly observed when compared with the standard ligand, thereby leading to structural and functional changes. Therefore, the bioactive components from Entada africana could be a good driver of glucokinase, thereby paving the way for the discovery of therapeutic drugs for the treatment of diabetes and its related complications.

Virtual screening of natural products inspired in-house library to discover potential lead molecules against the SARS-CoV-2 main protease

SARS-CoV-2, a new coronavirus emerged in 2019, causing a global healthcare epidemic. Although a variety of drug targets have been identified as potential antiviral therapies, and effective candidate against SARS-CoV-2 remains elusive. One of the most promising targets for combating COVID-19 is SARS-CoV-2 Main protease (M^pro, a protein responsible for viral replication. In this work, an in-house curated library was thoroughly evaluated for druggability against M^pro. We identified four ligands (FG, Q5, P5, and PJ4) as potential inhibitors based on docking scores, predicted binding energies (MMGBSA), in silico ADME, and RMSD trajectory analysis. Among the selected ligands, FG, a natural product from Andrographis nallamalayana, exhibited the highest binding energy of -10.31 kcal/mol close to the docking score of clinical candidates Boceprevir and GC376. Other ligands (P5, natural product from cardiospermum halicacabum and two synthetic molecules Q5 and PJ4) have shown comparable docking scores ranging -7.65 kcal/mol to -7.18 kcal/mol. Interestingly, we found all four top ligands had Pi bond interaction with the main amino acid residues HIS41 and CYS145 (catalytic dyad), H-bonding interactions with GLU166, ARG188, and GLN189, and hydrophobic interactions with MET49 and MET165 in the binding site of M^pro. According to the ADME analysis, Q5 and P5 are within the acceptable range of drug likeliness, compared to FG and PJ4. The interaction stability of the lead molecules with viral protease was verified using replicated MD simulations. Thus, the present study opens up the opportunity of developing drug candidates targeting SARS-CoV-2 main protease (M^pro) to mitigate the disease.Communicated by Ramaswamy H. Sarma.

Current Landscape of Methods to Evaluate Antimicrobial Activity of Natural Extracts

Natural extracts have been and continue to be used to treat a wide range of medical conditions, from infectious diseases to cancer, based on their convenience and therapeutic potential. Natural products derived from microbes, plants, and animals offer a broad variety of molecules and chemical compounds. Natural products are not only one of the most important sources for innovative drug development for animal and human health, but they are also an inspiration for synthetic biology and chemistry scientists towards the discovery of new bioactive compounds and pharmaceuticals. This is particularly relevant in the current context, where antimicrobial resistance has risen as a global health problem. Thus, efforts are being directed toward studying natural compounds' chemical composition and bioactive potential to generate drugs with better efficacy and lower toxicity than existing molecules. Currently, a wide range of methodologies are used to analyze the in vitro activity of natural extracts to determine their suitability as antimicrobial agents. Despite traditional technologies being the most employed, technological advances have contributed to the implementation of methods able to circumvent issues related to analysis capacity, time, sensitivity, and reproducibility. This review produces an updated analysis of the conventional and current methods to evaluate the antimicrobial activity of natural compounds.

Computational design of prospective molecular targets for Burkholderia cepacia complex by molecular docking and dynamic simulation studies

The study aimed to screen prospective molecular targets of BCC and potential natural lead candidates as effective binders by computational modeling, molecular docking, and dynamic (MD) simulation studies. Based on the virulent functions, tRNA 5-methylaminomethyl-2-thiouridine biosynthesis bifunctional protein (mnmC) and pyrimidine/purine nucleoside phosphorylase (ppnP) were selected as the prospective molecular targets. In the absence of experimental data, the three-dimensional (3D) structures of these targets were computationally predicted. After a thorough literature survey and database search, the drug-likeness, and pharmacokinetic properties of 70 natural molecules were computationally predicted and the effectual binding of the best lead molecules against both the targets was predicted by molecular docking. The stabilities of the best-docked complexes were validated by MD simulation and the binding energy calculations were carried out by MM-GBSA approaches. The present study revealed that the hypothetical models of mnmC and ppnP showed stereochemical accuracy. The study also showed that among 70 natural compounds subjected to computational screening, Honokiol (3',5-Di(prop-2-en-1-yl) [1,1'-biphenyl]-2,4'-diol) present in Magnolia showed ideal drug-likeness, pharmacokinetic features and showed effectual binding with mnmC and ppnP (binding energies -7.3 kcal/mol and -6.6 kcal/mol, respectively). The MD simulation and GBSA calculation studies showed that the ligand-protein complexes stabilized throughout tMD simulation. The present study suggests that Honokiol can be used as a potential lead molecule against mnmC and ppnP targets of BCC and this study provides insight into further experimental validation for alternative lead development against drug resistant BCC.

Prospective Challenges for Patenting and Clinical Trials of Anticancer Compounds from Natural Products: Coherent Review

Cancer is a leading cause of morbidity and mortality worldwide. Each year, millions of people worldwide are diagnosed with cancer, and more than half of them die. Various conventional therapies for cancer, including chemotherapy and radiotherapy, have extreme side effects. Therefore, to minimize the global burden of lethal diseases like cancer, an effective and novel drug must be discovered. Its patent should be acquired to secure the novel medicament. The pharmacological potential of different natural products has made them popular in the healthcare and pharmaceutical industries. Various anticancer compounds are obtained from natural sources such as plants, microbes, and marine and terrestrial animals, including alkaloids, terpenoids, biophenols, enzymes, glycosides, etc. The term "natural products" is defined as the product of secondary or non-essential metabolic processes produced by living organisms (such as plants, invertebrates, and microorganisms). Although more precise definitions of NPs exist, they do not always meet consensus. Others define NPs as small molecules (excluding biomolecules) that emerge from the metabolic reaction. A handful of effective compounds are used currently from natural or analog moieties, and many more are in clinical studies. There is an excellent need for patenting molecules from natural products as the hit lead molecules are derived, isolated, and synthesized from natural products. However, these naturally occurring products may not be patentable under the law because they come from nature. This review highlights why natural products and compounds are hard to patent, under what patent law criteria we can patent these natural products and compounds, patent procedural guideline sources and why researchers prefer publication rather than a patent. Here, various patent scenarios of natural products and compounds for cancer have been given.

Docking and Molecular Dynamic Investigations of Phenylspirodrimanes as Cannabinoid Receptor-2 Agonists

Cannabinoid receptor ligands are renowned as being therapeutically crucial for treating diverse health disorders. Phenylspirodrimanes are meroterpenoids with unique and varied structural scaffolds, which are mainly reported from the Stachybotrys genus and display an array of bioactivities. In this work, 114 phenylspirodrimanes reported from Stachybotrys chartarum were screened for their CB2 agonistic potential using docking and molecular dynamic simulation studies. Compound 56 revealed the highest docking score (-11.222 kcal/mol) compared to E3R_6KPF (native agonist, gscore value -12.12 kcal/mol). The molecular docking and molecular simulation results suggest that compound 56 binds to the putative binding site in the CB2 receptor with good affinity involving key interacting amino acid residues similar to that of the native ligands, E3R. The molecular interactions displayed π-π stacking with Phe183 and hydrogen bond interactions with Thr114, Leu182, and Ser285. These findings identified the structural features of these metabolites that might lead to the design of selective novel ligands for CB2 receptors. Additionally, phenylspirodrimanes should be further investigated for their potential as a CB2 ligand.

Identification and characterization of a novel peptide from rainbow trout (Oncorhynchus mykiss) with antimicrobial activity against Streptococcus iniae

The overuse and misuse of antibiotics has led to the emergence of antibiotic-resistant bacterial species which remain a challenge to treat therapeutically. Novel and efficacious drugs are desperately needed to combat pathogens. One method to facilitate these discoveries is the use of in silico methods. Computational biology has the power to scan large data sets and screen for potential molecules with antibacterial function. In the current study, an in silico approach was used to identify an antimicrobial peptide (AMP) derived from rainbow trout von Willebrand Factor. The AMP was tested against a panel of aquatic bacterial pathogens and was found to possess antibacterial activity against Streptococcus iniae (S. iniae). Since S. iniae is a zoonotic pathogen, this may be useful in other species as well. The peptide was non-hemolytic and non-cytotoxic at the concentrations tested in rainbow trout cells. Pre-treatment of rainbow trout cells with the peptide did not result in an upregulation of immune genes but stimulating the rainbow trout macrophage/monocyte-like cell line, RTS11, with heat-killed S. iniae, did result in a significant upregulation of the tumor necrosis factor alpha (tnfa) gene. In this study, a new AMP has been identified but its expression, synthesis and role in vivo remains unknown. Nevertheless, the findings presented improve our understanding of fish gill and macrophage responses towards this important zoonotic pathogen.

Lead/Drug Discovery from Natural Resources

Natural products and their derivatives have been shown to be effective drug candidates against various diseases for many years. Over a long period of time, nature has produced an abundant and prosperous source pool for novel therapeutic agents with distinctive structures. Major natural-product-based drugs approved for clinical use include anti-infectives and anticancer agents. This paper will review some natural-product-related potent anticancer, anti-HIV, antibacterial and antimalarial drugs or lead compounds mainly discovered from 2016 to 2022. Structurally typical marine bioactive products are also included. Molecular modeling, machine learning, bioinformatics and other computer-assisted techniques that are very important in narrowing down bioactive core structural scaffolds and helping to design new structures to fight against key disease-associated molecular targets based on available natural products are considered and briefly reviewed.

Molecular Docking and Dynamics Simulation Studies of Ginsenosides with SARS-CoV-2 Host and Viral Entry Protein Targets

Despite the contemporary advancements in the field of science and medicine, combating the coronavirus disease 2019 (COVID-19) is extremely challenging in many aspects as the virus keeps spreading and mutating rapidly. As there is no effective and conclusive drug therapy to date, it is crucial to explore plant-based natural compounds for their potential to inhibit SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). Recent research highly focuses on screening various phytochemicals to elucidate their anti-viral efficacy. However, very few studies were published investigating the anti-viral efficacy of ginsenosides. Hence, the main aim of this study was to investigate the inhibitory potential of the available 122 ginsenosides from Panax ginseng against SARS-CoV-2-related proteins using a molecular docking and molecular dynamics approach. The major bioactive compounds “ginsenosides” of P. ginseng were docked to six vital SAR-CoV-2 host entry-related proteins such as ACE2, Spike RBD, ACE2 and Spike RBD complex, Spike (pre-fused), Spike (post-fused), and HR domain, with lowest binding energies of −9.5 kcal/mol, −8.1 kcal/mol, −10.4 kcal/mol, −10.4 kcal/mol, −9.3 kcal/mol, and −8.2 kcal/mol, respectively. Almost all the ginsenosides have shown low binding energies and were found to be favourable for efficient docking and resultant inhibition of the viral proteins. However, ACE2 has shown the highest interaction capability. Hence, the top five ginsenosides with the highest binding energy with ACE2 were subjected to MD, post MD analysis, and MM/PBSA calculations. MD simulation results have shown higher stability, flexibility, and mobility of the selected compounds. Additionally, MM-PBSA also affirms the docking results. The results obtained from this study have provided highly potential candidates for developing natural inhibitors against COVID-19.

An in-silico investigation of potential natural polyphenols for the targeting of COVID main protease inhibitor

The deadliest recent pandemic outbreak of COVID-19 disease has severely damaged the socio-economic health of the people globally. Due to unavailability of any effective vaccine or treatment the human beings are still struggling to overcome the pandemic condition. In an attempt to discover anti-COVID molecule, we used in-silico approach and reported 160 natural polyphenols to identify the most promising druggable HITs that can further used for drug discovery process. The co-crystallized structure COVID protease enzyme (PDB id 6LU7) was used. HTVS, MD simulation, binding energy calculations and in-silico ADME calculation were done and analyzed. Depending upon the scores three compounds galangin, nalsudaldain and rhamnezine were identified and the docking score were found to be -7.704, -6.51, -4.212 respectively. These docked complexes were further subjected to MD simulation runs over a 100 ns time and the RMSD and RMSF values were determined. The RMSD values of three compounds were found to be 2.9 Å, 7.6 Å & 9.5 Å respectively and the lowest RMSF values suggested the steady stability of ligand-protein complexes. The binding free energies (ΔG) of compounds with protein were found to be -49.8, -56.45, -62.87 kJ/mole. Moreover, in-silico ADME calculations indicated the drug likeliness properties of these molecules. By considering all these in-silico results the identified HITs would be the most probable anti-COVID drug molecules that can be further taken in wet lab and can act as lead for development of newer inhibitor of COVID-19 main protease enzyme.

Automating Predictive Toxicology Using ComptoxAI

ComptoxAI is a new data infrastructure for computational and artificial intelligence research in predictive toxicology. Here, we describe and showcase ComptoxAI's graph-structured knowledge base in the context of three real-world use-cases, demonstrating that it can rapidly answer complex questions about toxicology that are infeasible using previous technologies and data resources. These use-cases each demonstrate a tool for information retrieval from the knowledge base being used to solve a specific task: The "shortest path" module is used to identify mechanistic links between perfluorooctanoic acid (PFOA) exposure and nonalcoholic fatty liver disease; the "expand network" module identifies communities that are linked to dioxin toxicity; and the quantitative structure-activity relationship (QSAR) dataset generator predicts pregnane X receptor agonism in a set of 4,021 pesticide ingredients. The contents of ComptoxAI's source data are rigorously aggregated from a diverse array of public third-party databases, and ComptoxAI is designed as a free, public, and open-source toolkit to enable diverse classes of users including biomedical researchers, public health and regulatory officials, and the general public to predict toxicology of unknowns and modes of action.

Ethnomedicinal, Phytochemical and Pharmacological Investigations of Tetradenia riparia (Hochst.) Codd (Lamiaceae)

Tetradenia riparia Hochsteter codd. (Lamiaceae) in its native African continent, is considered one of the most popular aromatic medicinal plants. In folk medicine it may be used as an infusion to treat respiratory problems, cough, headache, stomach pain, diarrhea, fever, malaria, and dengue; and in the form of compresses it is applied for the relief of headaches and toothaches. The species T. riparia has been researched for decades to isolate and identify chemical constituents present in extracts or essential oil obtained from the leaves, floral buds, or stems of this plant. The present study reviews the scientific literature on ethnomedicinal, phytochemical, and pharmacological aspects of T. riparia. We discuss issues related to the botanical and geographical description of the species, ethnobotanical uses, phytochemical studies on its essential oil and extracts, and biological activities of T. riparia. Several compounds have already been isolated from leaves, such as ibozol, 7α-hydroxyroileanone, 1',2'-dideacetylboronolide, 8(14),15-sandaracopimaradiene-7α,18-diol; 5,6-dihydro-α-pyrone and α-pyrone. Terpenes predominated in the essential oil, comprising monoterpenes, sesquiterpenes, diterpenes, hydrocarbons, and oxygenates. Most phytocompounds were isolated from the leaves and flower buds, namely fenchone, 14-hydroxy-9-epi (E)-caryophyllene, 9β, 13β-epoxy-7-abietene, and 6,7-dehydroroileanone. These compounds provide the species a high pharmacological potential, with antimicrobial, antioxidant, antitumor, analgesic, anti-leishmania, anti-tuberculosis, and anti-parasitic activities. Therefore, this species is a promising herbal medicine.

Investigating Multi-Target Antiviral Compounds by Screening of Phytochemicals From Neem (Azadirachta indica) Against PRRSV: A Vetinformatics Approach

Porcine reproductive and respiratory syndrome virus (PRRSV) is a global health problem for pigs. PRRSV is highly destructive and responsible for significant losses to the swine industry. Vaccines are available but incapable of providing adequate and long-term protection. As a result, effective and safe strategies are urgently needed to combat the virus. The scavenger receptor cysteine-rich domain 5 (SRCR5) in porcine CD163, non-structural protein 4 (Nsp4), and Nsp10 are known to play significant roles in PRRSV infection and disease development. Therefore, we targeted these proteins to identify multi-target antiviral compounds. To identify potent inhibitors, molecular docking of neem phytochemicals was conducted; three compounds [7-deacetyl-7-oxogedunin (CID:1886), Kulactone (CID:15560423), and Nimocin (CASID:104522-76-1)] were selected based on the lowest binding energy and multi-target inhibitory nature. The efficacy and safety of the selected compounds were revealed through the pharmacokinetics analysis and toxicity assessment. Moreover, 100 ns molecular dynamics (MD) simulation was performed to evaluate the stability and dynamic behavior of target proteins and their docked complexes with selected compounds. Besides, molecular mechanics Poisson–Boltzmann surface area method was used to estimate the binding free energy of each protein-ligand complex obtained from the MD simulations and validate the affinities of selected compounds to target proteins. Based on our analysis, we concluded that the identified multi-target compounds can be utilized as lead compounds for the development of natural drugs against PRRSV. If further validated in clinical studies, these compounds can be used individually or in combination against the virus.

Deep Neural Network-Assisted Drug Recommendation Systems for Identifying Potential Drug-Target Interactions

In silico methods to identify novel drug-target interactions (DTIs) have gained significant importance over conventional techniques owing to their labor-intensive and low-throughput nature. Here, we present a machine learning-based multiclass classification workflow that segregates interactions between active, inactive, and intermediate drug-target pairs. Drug molecules, protein sequences, and molecular descriptors were transformed into machine-interpretable embeddings to extract critical features from standard datasets. Tools such as CHEMBL web resource, iFeature, and an in-house developed deep neural network-assisted drug recommendation (dNNDR)-featx were employed for data retrieval and processing. The models were trained with large-scale DTI datasets, which reported an improvement in performance over baseline methods. External validation results showed that models based on att-biLSTM and gCNN could help predict novel DTIs. When tested with a completely different dataset, the proposed models significantly outperformed competing methods. The validity of novel interactions predicted by dNNDR was backed by experimental and computational evidence in the literature. The proposed methodology could elucidate critical features that govern the relationship between a drug and its target.

Teaching an old dog new tricks: drug discovery by repositioning natural products and their derivatives

Given the substantial cost and low success rate of drug discovery and development, repositioning existing drugs to treat new diseases has gained significant attention in recent years, with potentially lower development costs and shorter time frames. Natural products show great promise in drug repositioning because they have been used for various medical purposes for thousands of years. In this review, we discuss the drug repositioning of six prototypical natural products and their derivatives to reveal new drug-disease associations. We also highlight opportunities and challenges in natural product-based drug repositioning for future reference.

Natural product drug discovery in the artificial intelligence era

Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.

Translational Informatics for Natural Products as Antidepressant Agents

Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

Antibacterial Activity Prediction of Plant Secondary Metabolites Based on a Combined Approach of Graph Clustering and Deep Neural Network

The plants produce numerous types of secondary metabolites which have pharmacological importance in drug development for different diseases. Computational methods widely use the fingerprints of the metabolites to understand different properties and similarities among metabolites and for the prediction of chemical reactions etc. In this work, we developed three different deep neural network models (DNN) to predict the antibacterial property of plant metabolites. We developed the first DNN model using the fingerprint set of metabolites as features. In the second DNN model, we searched the similarities among fingerprints using correlation and used one representative feature from each group of highly correlated fingerprints. In the third model, the fingerprints of metabolites were used to find structurally similar chemical compound clusters. Form each cluster a representative metabolite is selected and made part of the training dataset. The second model reduced the number of features where the third model achieved better classification results for test data. In both cases, we applied the simple graph clustering method to cluster the corresponding network. The correlation‐based DNN model reduced some features while retaining an almost similar performance compared to the first DNN model. The third model improves classification results for test data by capturing wider variance within training data using graph clustering method. This third model is somewhat novel approach and can be applied to build DNN models for other purposes.

Antiviral plant-derived natural products to combat RNA viruses: Targets throughout the viral life cycle

There is a need for new effective antivirals, particularly in response to the development of antiviral drug resistance and emerging RNA viruses such as SARS‐CoV‐2. Plants are a significant source of structurally diverse bioactive compounds for drug discovery suggesting that plant‐derived natural products could be developed as antiviral agents. This article reviews the antiviral activity of plant‐derived natural products against RNA viruses, with a focus on compounds targeting specific stages of the viral life cycle. A range of plant extracts and compounds have been identified with antiviral activity, often against multiple virus families suggesting they may be useful as broad‐spectrum antiviral agents. The antiviral mechanism of action of many of these phytochemicals is not fully understood and there are limited studies and clinical trials demonstrating their efficacy and toxicity in vivo. Further research is needed to evaluate the therapeutic potential of plant‐derived natural products as antiviral agents.

Repositioning of Fungal-based Peptides as Modulators of Angiotensin-converting Enzyme-related Carboxypeptidase, SARS-coronavirus HR2 Domain, and Coronavirus Disease 2019 Main Protease

Background and Objectives

Angiotensin-converting enzyme-related carboxypeptidase, SARS-Coronavirus HR2 Domain, and COVID-19 main protease are essential for the cellular entry and replication of coronavirus in the host. This study investigated the putative inhibitory action of peptides form medicinal mushrooms, namely Pseudoplectania nigrella, Russula paludosa, and Clitocybe sinopica, towards selected proteins through computational studies.

Materials and Methods

The respective physicochemical properties of selected peptides were predicted using ProtParam tool, while the binding modes and binding free energy of selected peptides toward proteins were computed through HawkDock server. The structural flexibility and stability of docked protein-peptide complexes were assessed through iMODS server.

Results

The peptides showed an optimum binding afinity with the molecular targets; plectasin from P. nigrella showed the highest binding free energy compared to peptides from R. paludosa and C. sinopica. Besides, molecular dynamic simulations showed all fungal-based peptides could influence the flexibility and stability of selected proteins.

Conclusion

The study revealed fungal-based peptides could be explored as functional modulators of essential proteins that are involved in the cellular entry of coronavirus.

Potential Anti-Mycobacterium tuberculosis Activity of Plant Secondary Metabolites: Insight with Molecular Docking Interactions

Tuberculosis (TB) is a recurrent and progressive disease, with high mortality rates worldwide. The drug-resistance phenomenon of Mycobacterium tuberculosis is a major obstruction of allelopathy treatment. An adverse side effect of allelopathic treatment is that it causes serious health complications. The search for suitable alternatives of conventional regimens is needed, i.e., by considering medicinal plant secondary metabolites to explore anti-TB drugs, targeting the action site of M. tuberculosis. Nowadays, plant-derived secondary metabolites are widely known for their beneficial uses, i.e., as antioxidants, antimicrobial agents, and in the treatment of a wide range of chronic human diseases (e.g., tuberculosis), and are known to “thwart” disease virulence. In this regard, in silico studies can reveal the inhibitory potential of plant-derived secondary metabolites against Mycobacterium at the very early stage of infection. Computational approaches based on different algorithms could play a significant role in screening plant metabolites against disease virulence of tuberculosis for drug designing.

Novel Molecules derived from 3-O-(6-galloylglucoside) inhibit Main Protease of SARS-CoV 2 In Silico

The ongoing pandemic caused by the severe acute respiratory syndrome 2 (SARS-CoV 2) has led to more than 168 million confirmed cases with 3.5 million deaths as at 28th May, 2021 across 218 countries. The virus has a cysteine protease called main protease (Mpro) which is significant to it life cycle, tagged as a suitable target for novel antivirals. In this computer-assisted study, we designed 100 novel molecules through an artificial neural network-driven platform called LigDream (https://playmolecule.org/LigDream/) using 3-O-(6-galloylglucoside) as parent molecule for design. Druglikeness screening of the molecules through five (5) different rules was carried out, followed by a virtual screening of those molecules without a single violation of the druglike rules using AutoDock Vina against Mpro. The in silico pharmacokinetic features were predicted and finally, quantum mechanics/molecular mechanics (QM/MM) study was carried out using Molecular Orbital Package 2016 (MOPAC2016) on the overall hit compound with controls to determine the stability and reactivity of the lead molecule. The findings showed that eight (8) novel molecules violated none of the druglikeness rules of which three (3) novel molecules (C33, C35 and C54) showed the utmost binding affinity of −8.3 kcal/mol against Mpro; C33 showed a good in silico pharmacokinetic features with acceptable level of stability and reactively better than our controls based on the quantum chemical descriptors analysis. However, there is an urgent need to carry out more research on these novel molecules for the fight against the disease.

Identification of Effective Anticancer G-Quadruplex-Targeting Chemotypes through the Exploration of a High Diversity Library of Natural Compounds

In the quest for selective G-quadruplex (G4)-targeting chemotypes, natural compounds have been thus far poorly explored, though representing appealing candidates due to the high structural diversity of their scaffolds. In this regard, a unique high diversity in-house library composed of ca. one thousand individual natural products was investigated. The combination of molecular docking-based virtual screening and the G4-CPG experimental screening assay proved to be useful to quickly and effectively identify-out of many natural compounds-five hit binders of telomeric and oncogenic G4s, i.e., Bulbocapnine, Chelidonine, Ibogaine, Rotenone and Vomicine. Biophysical studies unambiguously demonstrated the selective interaction of these compounds with G4s compared to duplex DNA. The rationale behind the G4 selective recognition was suggested by molecular dynamics simulations. Indeed, the selected ligands proved to specifically interact with G4 structures due to peculiar interaction patterns, while they were unable to firmly bind to a DNA duplex. From biological assays, Chelidonine and Rotenone emerged as the most active compounds of the series against cancer cells, also showing good selectivity over normal cells. Notably, the anticancer activity correlated well with the ability of the two compounds to target telomeric G4s.

In silico and gene expression analysis of the acute inflammatory response of gilthead seabream (Sparus aurata) after subcutaneous administration of carrageenin

Inflammation is one of the main causes of loss of homeostasis at both the systemic and molecular levels. The aim of this study was to investigate in silico the conservation of inflammation-related proteins in the gilthead seabream (Sparus aurata L.). Open reading frames of the selected genes were used as input in the STRING database for protein-protein interaction network analysis, comparing them with other teleost protein sequences. Proteins of the large yellow croaker (Larimichthys crocea L.) presented the highest percentages of identity with the gilthead seabream protein sequence. The gene expression profile of these proteins was then studied in gilthead seabream specimens subcutaneously injected with carrageenin (1%) or phosphate-buffered saline (control) by analyzing skin samples from the injected zone 12 and 24 h after injection. Gene expression analysis indicated that the mechanisms necessary to terminate the inflammatory response to carrageenin and recover skin homeostasis were activated between 12 and 24 h after injection (at the tested dose). The gene analysis performed in this study could contribute to the identification of the main mechanisms of acute inflammatory response and validate the use of carrageenin as an inflammation model to elucidate these mechanisms in fish.

Solid-Phase Extraction Embedded Dialysis (SPEED), an Innovative Procedure for the Investigation of Microbial Specialized Metabolites

Solid-phase extraction embedded dialysis (SPEED technology) is an innovative procedure developed to physically separate in-situ, during the cultivation, the mycelium of filament forming microorganisms, such as actinomycetes and fungi, and the XAD-16 resin used to trap the secreted specialized metabolites. SPEED consists of an external nylon cloth and an internal dialysis tube containing the XAD resin. The dialysis barrier selects the molecular weight of the trapped compounds, and prevents the aggregation of biomass or macromolecules on the XAD beads. The external nylon promotes the formation of a microbial biofilm, making SPEED a biofilm supported cultivation process. SPEED technology was applied to the marine Streptomyces albidoflavus 19-S21, isolated from a core of a submerged Kopara sampled at 20 m from the border of a saltwater pond. The chemical space of this strain was investigated effectively using a dereplication strategy based on molecular networking and in-depth chemical analysis. The results highlight the impact of culture support on the molecular profile of Streptomyces albidoflavus 19-S21 secondary metabolites.

Peptide-like and small-molecule inhibitors against Covid-19

Coronavirus disease strain (SARS-CoV-2) was discovered in 2019, and it is spreading very fast around the world causing the disease Covid-19. Currently, more than 1.6 million individuals are infected, and several thousand are dead across the globe because of Covid-19. Here, we utilized the in-silico approaches to identify possible protease inhibitors against SARS-CoV-2. Potential compounds were screened from the CHEMBL database, ZINC database, FDA approved drugs and molecules under clinical trials. Our study is based on 6Y2F and 6W63 co-crystallized structures available in the protein data bank (PDB). Seven hundred compounds from ZINC/CHEMBL databases and fourteen hundred compounds from drug-bank were selected based on positive interactions with the reported binding site. All the selected compounds were subjected to standard-precision (SP) and extra-precision (XP) mode of docking. Generated docked poses were carefully visualized for known interactions within the binding site. Molecular mechanics-generalized born surface area (MM-GBSA) calculations were performed to screen the best compounds based on docking scores and binding energy values. Molecular dynamics (MD) simulations were carried out on four selected compounds from the CHEMBL database to validate the stability and interactions. MD simulations were also performed on the PDB structure 6YF2F to understand the differences between screened molecules and co-crystallized ligand. We screened 300 potential compounds from various databases, and 66 potential compounds from FDA approved drugs. Cobicistat, ritonavir, lopinavir, and darunavir are in the top screened molecules from FDA approved drugs. The screened drugs and molecules may be helpful in fighting with SARS-CoV-2 after further studies.Communicated by Ramaswamy H. Sarma.

Exploring the new potential antiviral constituents of Moringa oliefera for SARS-COV-2 pathogenesis: An in silico molecular docking and dynamic studies

The interactions of two crucial proteins of COVID-19 have been investigated with potential antiviral compounds from Moringa oliefera using quantum chemical, molecular docking and dynamic methods. The results of the present investigation show that ellagic acid and apigenin possess the highest binding affinities of -7.1 and -6.5 Kcal.mol^-1against nsp9 and -6.9 and -7.1 Kcal.mol^-1 against nsp10, respectively. The dynamic behavior of individual proteins and their respective best docked ligand-protein complexes are also studied at 30 ns timescale. Both of these compounds also show the highest intestinal absorption and total clearance rate as compared to the other compounds under present investigation without any toxicity.

A comprehensive evaluation of the chemical profiles and biological properties of six geophytes from Turkey: Sources of bioactive compounds for novel nutraceuticals

Geophytes are gaining interest as sources of natural ingredients in nutraceutical and pharmaceutical area. In this sense, six bulbous plant species from Turkey are investigated, namely Hyacinthella campanulata K.Perss. & Wendelbo, Muscari neglectum Guss. ex Ten., Tulipa humilis herb., Iris stenophylla Hausskn. ex Baker, Galanthus elwesii Hook. f. and Crocus danfordiae Maw. with the aim to highlight their chemical compositions and biological properties. Polyphenolic profiles of the different plant parts (flower, bulb and leaf) of the six genotypes were evaluated using colorimetric methods as well LC-MS/MS. The antioxidant properties and enzymes inhibitory potential (α-amylase, α-glucosidase, tyrosinase and cholinesterases) of the extracts were determined. Overall, highest total phenolic content and total flavonoid content were observed in the leaf extracts of the studied species, except for M. neglectum (flower extract) and T. humilis (flower extract). LC-MS/MS analysis revealed the abundance of some phenolic compounds including quinic acid, hesperidin and chlorogenic acid in selective extracts. The extracts showed significant antioxidant potentials, with leaf extract of the I. stenophylla being more potent, which is linked to its high phenolic contents. All the extracts displayed notable anti-acetylcholinesterase (1.77 - 2.53 mg GALAE/g) and tyrosinase (54.9-67.20 mg KAE/g). Selective extracts have showed activity against butyrylcholinesterase, with bulb extract of M. neglectum (2.99 mg GALAE/g), I. stenophylla (2.53 mg GALAE/g) and G. elwesii (2.52 mg GALAE/g) showing highest activity. Modest activity was observed against α-amylase and α-glucosidase. The experimental data gathered herein is the first report on the phytochemical and biological attributes of these bulbous plant species which project them as potential sources of biologically active compounds for phytomedicines and nutraceuticals development.

Investigation of Alpinia calcarata constituent interactions with molecular targets of rheumatoid arthritis: docking, molecular dynamics, and network approach

Rheumatoid arthritis (RA) is a systemic autoimmune disorder that commonly affects multiple joints of the body. Currently, there is no permanent cure to the disease, but it can be managed with several potent drugs that cause serious side effects on prolonged use. Traditional remedies are considered promising for the treatment of several diseases, particularly chronic conditions, because they have lower side effects compared to synthetic drugs. In folklore, the rhizome of Alpinia calcarata Roscoe (Zingiberaceae) is used as a major ingredient of herbal formulations to treat RA. Phytoconstituents reported in A. calcarata rhizomes are diterpenoids, sesquiterpenoid, flavonoids, phytosterol, and volatile oils. The present study is intended to understand the molecular-level interaction of phytoconstituents present in A. calcarata rhizomes with RA molecular targets using computational approaches. A total of 30 phytoconstituents reported from the plant were used to carry out docking with 36 known targets of RA. Based on the docking results, 4 flavonoids were found to be strongly interacting with the RA targets. Further, molecular dynamics simulation confirmed stable interaction of quercetin with 6 targets (JAK3, SYK, MMP2, TLR8, IRAK1, and JAK1), galangin with 2 targets (IRAK1 and JAK1), and kaempferol (IRAK1) with one target of RA. Moreover, the presence of these three flavonoids was confirmed in the A. calcarata rhizome extract using LC-MS analysis. The computational study suggests that flavonoids present in A. calcarata rhizome may be responsible for RA modulatory activity. Particularly, quercetin and galangin could be potential development candidates for the treatment of RA. Investigation of Alpinia calcarata constituent interactions with molecular targets of rheumatoid arthritis: docking, molecular dynamics, and network approach.

A unique high-diversity natural product collection as a reservoir of new therapeutic leads

We review the successful application of computer-aided methods to screen a unique and high-diversity in house collection library composed of around 1000 individual natural products.

Informatics for Chemistry, Biology, and Biomedical Sciences

Informatics is growing across disciplines, impacting several areas of chemistry, biology, and biomedical sciences. Besides the well-established bioinformatics discipline, other informatics-based interdisciplinary fields have been evolving over time, such as chemoinformatics and biomedical informatics. Other related research areas such as pharmacoinformatics, food informatics, epi-informatics, materials informatics, and neuroinformatics have emerged more recently and continue to develop as independent subdisciplines. The goals and impacts of each of these disciplines have typically been separately reviewed in the literature. Hence, it remains challenging to identify commonalities and key differences. Herein, we discuss in context three major informatics disciplines in the natural and life sciences including bioinformatics, chemoinformatics, and biomedical informatics and briefly comment on related subdisciplines. We focus the discussion on the definitions, historical background, actual impact, main similarities, and differences and evaluate the dissemination and teaching of bioinformatics, chemoinformatics, and biomedical informatics.

Using X-ray Diffraction Techniques for Biomimetic Drug Development, Formulation, and Polymorphic Characterization

Drug development is a decades-long, multibillion dollar investment that often limits itself. To decrease the time to drug approval, efforts are focused on drug targets and drug formulation for optimal biocompatibility and efficacy. X-ray structural characterization approaches have catalyzed the drug discovery and design process. Single crystal X-ray diffraction (SCXRD) reveals important structural details and molecular interactions for the manifestation of a disease or for therapeutic effect. Powder X-ray diffraction (PXRD) has provided a method to determine the different phases, purity, and stability of biological drug compounds that possess crystallinity. Recently, synchrotron sources have enabled wider access to the study of noncrystalline or amorphous solids. One valuable technique employed to determine atomic arrangements and local atom ordering of amorphous materials is the pair distribution function (PDF). PDF has been used in the study of amorphous solid dispersions (ASDs). ASDs are made up of an active pharmaceutical ingredient (API) within a drug dispersed at the molecular level in an amorphous polymeric carrier. This information is vital for appropriate formulation of a drug for stability, administration, and efficacy purposes. Natural or biomimetic products are often used as the API or the formulation agent. This review profiles the deep insights that X-ray structural techniques and associated analytical methods can offer in the development of a drug.

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Colombian Contributions Fighting Leishmaniasis: A Systematic Review on Antileishmanials Combined with Chemoinformatics Analysis

Leishmaniasis is a parasitic morbid/fatal disease caused by Leishmania protozoa. Twelve million people worldwide are appraised to be currently infected, including ca. two million infections each year, and 350 million people in 88 countries are at risk of becoming infected. In Colombia, cutaneous leishmaniasis (CL) is a public health problem in some tropical areas. Therapeutics is based on traditional antileishmanial drugs, but this practice has several drawbacks for patients. Thus, the search for new antileishmanial agents is a serious need, but the lack of adequately funded research programs on drug discovery has hampered its progress. Some Colombian researchers have conducted different research projects focused on the assessment of the antileishmanial activity of naturally occurring and synthetic compounds against promastigotes and/or amastigotes. Results of such studies have separately demonstrated important hits and reasonable potential, but a holistic view of them is lacking. Hence, we present the outcome from a systematic review of the literature (under PRISMA guidelines) on those Colombian studies investigating antileishmanials during the last thirty-two years. In order to combine the general efforts aiming at finding a lead against Leishmania panamensis (one of the most studied and incident parasites in Colombia causing CL) and to recognize structural features of representative compounds, fingerprint-based analyses using conventional machine learning algorithms and clustering methods are shown. Abstraction from such a meta-description led to describe some function-determining molecular features and simplify the clustering of plausible isofunctional hits. This systematic review indicated that the Colombian efforts for the antileishmanials discovery are increasingly intensified, though improvements in the followed pathways must be definitively pursued. In this context, a brief discussion about scope, strengths and limitations of such advances and relationships is addressed.