Recognizing molecules with drug-like properties

Exploration of structure-activity relationships for the SARS-CoV-2 macrodomain from shape-based fragment linking and active learning

The macrodomain of severe acute respiratory syndrome coronavirus 2 nonstructural protein 3 is required for viral pathogenesis and is an emerging antiviral target. We previously performed an x-ray crystallography-based fragment screen and found submicromolar inhibitors by fragment linking. However, these compounds had poor membrane permeability and liabilities that complicated optimization. Here, we developed a shape-based virtual screening pipeline-FrankenROCS. We screened the Enamine high-throughput collection of 2.1 million compounds, selecting 39 compounds for testing, with the most potent binding with a 130 μM median inhibitory concentration (IC50). We then paired FrankenROCS with an active learning algorithm (Thompson sampling) to efficiently search the Enamine REAL database of 22 billion molecules, testing 32 compounds with the most potent binding with a 220 μM IC50. Further optimization led to analogs with IC50 values better than 10 μM. This lead series has improved membrane permeability and is poised for optimization. FrankenROCS is a scalable method for fragment linking to exploit synthesis-on-demand libraries.

Non-nutritive sweeteners in food-drug interactions: An overview of current evidence

Food-drug interactions occur when the presence of foods interferes with the absorption, distribution, metabolism, or excretion of pharmaceuticals. Specific compounds within foods, like certain phytochemicals from grapefruit, have been known to precipitate food-drug interactions for decades, leading to guidance from physicians and pharmacists about patients' dietary restrictions while taking certain drugs. Although approved by the Food and Drug Administration, high-intensity non-nutritive sweeteners (NNS) share qualities with drugs that suggest the potential for similar interactions. In this minireview, we have reviewed 5 of the most popular NNS, including saccharin, aspartame, acesulfame potassium, sucralose, and stevia, and detail their drug-like qualities, regulatory status, pharmacokinetics, and primary research articles containing evidence of NNS interacting with drug absorption, distribution, metabolism, and excretion. Although studies varied widely in concentration ranges for NNS, model systems, and methods, all NNS included in this review were found to have known interactions with mediators of absorption, distribution, metabolism, and excretion from studies conducted after their Food and Drug Administration approval or generally recognized as safe designation. We have highlighted essential gaps in the literature and recommend the scientific community actively research NNS as food additives that may interact with drugs. SIGNIFICANCE STATEMENT: Food-drug interactions are a growing concern in Western societies where polypharmacy and ultraprocessed foods and beverages are increasingly common. High-intensity non-nutritive sweeteners bear structural similarities to pharmaceuticals, and evidence suggests they interact with mediators of drug pharmacokinetics. This minireview highlights the interactions uncovered thus far and serves as a call to action for the scientific community to establish rigorous, consistent testing that will enable updated safety guidelines for consumers.

N-Glycoside of Indolo[2,3-a]pyrrolo[3,4-c]carbazole LCS1269 Exerts Anti-Glioblastoma Effects by G2 Cell Cycle Arrest and CDK1 Activity Modulation: Molecular Docking Studies, Biological Investigations, and ADMET Prediction

Background/Objectives: Indolo[2,3-a]pyrrolo[3,4-c]carbazole scaffold is successfully used as an efficient structural motif for the design and development of different antitumor agents. In this study, we investigated the anti-glioblastoma therapeutic potential of glycosylated indolocarbazole analog LCS1269 utilizing in vitro, in vivo, and in silico approaches. Methods: Cell viability was estimated by an MTT assay. The distribution of cell cycle phases was monitored using flow cytometry. Mitotic figures were visualized by fluorescence microscopy. Quantitative RT-PCR was used to evaluate the gene expression. The protein expression was assessed by Western blotting. Molecular docking and computational ADMET were approved for the probable protein target simulations and predicted pharmacological assessments, respectively. Results: Our findings clearly suggest that LCS1269 displayed a significant cytotoxic effect against diverse glioblastoma cell lines and patient-derived glioblastoma cultures as well as strongly suppressed xenograft growth in nude mice. LCS1269 exhibited more potent anti-proliferative activity toward glioblastoma cell lines and patient-derived glioblastoma cultures compared to conventional drug temozolomide. We further demonstrated that LCS1269 treatment caused the severe G2 phase arrest of cell cycle in a dose-dependent manner. Mechanistically, we proposed that LCS1269 could affect the CDK1 activity both by targeting active site of this enzyme and indirectly, in particular through the modulation of the Wee1/Myt1 and FOXM1/Plk1 signaling pathways, and via p21 up-regulation. LCS1269 also showed favorable pharmacological characteristics in in silico ADME prediction in comparison with staurosporine, rebeccamycin, and becatecarin as reference drugs. Conclusions: Further investigations of LCS1269 as an anti-glioblastoma medicinal agent could be very promising.

Analysis of the Antibiotic-Potentiating Activity, Absorption, Distribution, Metabolism, and Excretion (ADME) and the Molecular Docking Properties of Phytol Against Multi-Drug-Resistant (MDR) Strains

Background: Phytol is a diterpene from the long-chain unsaturated acyclic alcohols, known for its diverse biological effects, including antimicrobial and anti-inflammatory activities. Present in essential oils, phytol is a promising candidate for various applications in the pharmaceutical and biotechnological sectors. This study aimed to evaluate the in vitro antibacterial and drug-potentiating effects of phytol against multidrug-resistant bacteria and to evaluate its in silico properties: ADME and molecular docking. Methods: The in vitro antibacterial activity of phytol and the phytol combined with conventional drugs was evaluated by microdilution tests against standard and resistant bacterial strains. Finally, the SwissADME platform was employed to analyse the physicochemical and pharmacokinetic characteristics of phytol. Results: Phytol significantly reduced the Minimum Inhibitory Concentration (MIC) of norfloxacin and gentamicin required to inhibit multidrug-resistant strains of Escherichia coli and Staphylococcus aureus, respectively. Additionally, ADME analysis revealed that phytol exhibits low toxicity and favourable pharmacokinetic properties; in addition, it is revealed through molecular docking that phytol showed a relevant affinity with the proteins 6GJ1 and 5KDR, however, with values lower than the drugs gentamicin and ampicillin. Conclusions: Collectively, these findings suggest that phytol holds potential as an effective adjuvant in combating antimicrobial resistance.

Discovery of novel MLK4 inhibitors against colorectal cancer through computational approaches

Colorectal cancer (CRC) is a significant health issue globally, affecting approximately 10 % of the world's population. The prevalence of CRC highlights the need for effective treatments and prevention strategies. The current therapeutic option, such as chemotherapy, has significant side effects. Thus, this study investigated the anticancer properties of Sanguinarine derivatives, an alkaloid found in traditional herbs via chemoinformatic approaches. Six Sanguinarine derivatives were discovered through virtual screening and molecular docking to determine their binding affinities against the mixed lineage kinase (MLK4) protein which is responsible for CRC. All the compounds were found to be more effective than standard drug used for colorectal cancer treatment, with Sanguinarine derivative 11 showing the highest affinity. The stability of the drug was confirmed through molecular dynamics simulations at 500 ns. This suggests that compound 11 has a higher chance of replacing 5-Fluorouracil, which is currently a widely used chemotherapy drug. Before molecular dynamics simulations, the pharmacokinetic and chemical properties of Sanguinarine derivatives were determined using pkCSM server and DFT method, respectively. The results support that compound 11 is a good drug candidate, as evidenced by Lipinski's Rule of Five. Therefore, compound 11 is recommended for further analysis via in vivo and in vitro studies to confirm its efficacy and safety.

Extensive exploration of structure activity relationships for the SARS-CoV-2 macrodomain from shape-based fragment merging and active learning

The macrodomain contained in the SARS-CoV-2 non-structural protein 3 (NSP3) is required for viral pathogenesis and lethality. Inhibitors that block the macrodomain could be a new therapeutic strategy for viral suppression. We previously performed a large-scale X-ray crystallography-based fragment screen and discovered a sub-micromolar inhibitor by fragment linking. However, this carboxylic acid-containing lead had poor membrane permeability and other liabilities that made optimization difficult. Here, we developed a shape-based virtual screening pipeline - FrankenROCS - to identify new macrodomain inhibitors using fragment X-ray crystal structures. We used FrankenROCS to exhaustively screen the Enamine high-throughput screening (HTS) collection of 2.1 million compounds and selected 39 compounds for testing, with the most potent compound having an IC50 value equal to 130 μM. We then paired FrankenROCS with an active learning algorithm (Thompson sampling) to efficiently search the Enamine REAL database of 22 billion molecules, testing 32 compounds with the most potent having an IC50 equal to 220 μM. Further optimization led to analogs with IC50 values better than 10 μM, with X-ray crystal structures revealing diverse binding modes despite conserved chemical features. These analogs represent a new lead series with improved membrane permeability that is poised for optimization. In addition, the collection of 137 X-ray crystal structures with associated binding data will serve as a resource for the development of structure-based drug discovery methods. FrankenROCS may be a scalable method for fragment linking to exploit ever-growing synthesis-on-demand libraries.

Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank, aiming to obtain a new and virtual screening compounds database with drug-like properties. Ultimately, a compounds database consisting of 26,316 compounds is obtained by this method. To evaluate the potential of this compounds database, a series of tests are performed, including chemical space, ADME properties, compound fragmentation, and synthesizability analysis. As a result, it is proved that the database is equipped with good drug-like properties and a relatively new backbone, its potential in virtual screening is further tested. Finally, a series of seedling compounds with completely new backbones are obtained through docking and binding free energy calculations.

Natural Isatin Derivatives Against Black Fungus: In Silico Studies

During this coronavirus pandemic, when a lot of people are already severely afflicted with SARS-CoV-19, the dispersion of black fungus is making it worse, especially in the Indian subcontinent. Considering this situation, the idea for an in silico study to identify the potential inhibitor against black fungal infection is envisioned and computational analysis has been conducted with isatin derivatives that exhibit considerable antifungal activity. Through this in silico study, several pharmacokinetics properties like absorption, distribution, metabolism, excretion, and toxicity (ADMET) are estimated for various derivatives. Lipinski rules have been used to observe the drug likeliness property, and to study the electronic properties of the molecules, quantum mechanism was analyzed using the density functional theory (DFT). After applying molecular docking of the isatin derivatives with sterol 14-alpha demethylase enzyme of black fungus, a far higher docking affinity score has been observed for the isatin sulfonamide-34 (derivative 1) than the standard fluconazole. Lastly, molecular dynamic (MD) simulation has been performed for 100 ns to examine the stability of the proposed drug complex by estimating Root Mean Square Deviation (RMSD), Radius of gyration (Rg), Solvent accessible surface area (SASA), Root Mean Square Fluctuation (RMSF), as well as hydrogen bond. Listed ligands have precisely satisfied every pharmacokinetics requirement for a qualified drug candidate and they are non-toxic, non-carcinogenic, and have high stability. This natural molecule known as isatin derivative 1 has shown the potential of being a drug for fungal treatment. However, the impact of the chemicals on living cells requires more investigation and research.

Mechanistic inhibition of gastric cancer-associated bacteria Helicobacter pylori by selected phytocompounds: A new cutting-edge computational approach

Background

Helicobacter pylori (H. pylori) is a persistent bacterial inhabitant in the stomachs of approximately half the global populace. This bacterium is directly linked to chronic gastritis, leading to a heightened risk of duodenal and gastric ulcer diseases, and is the predominant risk factor for gastric cancer - the second most common cause of cancer-related deaths globally. The increasing prevalence of antibiotic resistance necessitates the exploration of innovative treatment alternatives to mitigate the H. pylori menace.

Methods

Initiating our study, we curated a list of thirty phytochemicals based on previous literature and subjected them to molecular docking studies. Subsequently, eight phytocompounds-Glabridin, Isoliquiritin, Sanguinarine, Liquiritin, Glycyrrhetic acid, Beta-carotin, Diosgenin, and Sarsasapogenin-were meticulously chosen based on superior binding scores. These were further subjected to an extensive computational analysis encompassing ADMET profiling, drug-likeness evaluation, principal component analysis (PCA), and molecular dynamic simulations (MDs) in comparison with the conventional drug, Mitomycin.

Results

The natural compounds investigated demonstrated superior docking affinities to H. pylori targets compared to the standard Mitomycin. Notably, the phytocompounds Diosgenin and Sarsasapogenin stood out due to their exceptional binding affinities and pharmacokinetic properties, including favorable ADMET profiles.

Conclusion

Our comprehensive and technologically-advanced approach showcases the potential of identified phytocompounds as pioneering therapeutic agents against H. pylori-induced gastric malignancies. In light of our promising in silico results, we recommend these natural compounds as potential candidates for advancing H. pylori-targeted drug development. Given their potential, we strongly advocate for subsequent in vitro and in vivo studies to validate their therapeutic efficacy against this formidable gastrointestinal bacterium.

Novel computational and drug design strategies for inhibition of monkeypox virus and Babesia microti: molecular docking, molecular dynamic simulation and drug design approach by natural compounds

Background

The alarming increase in tick-borne pathogens such as human Babesia microti is an existential threat to global public health. It is a protozoan parasitic infection transmitted by numerous species of the genus Babesia. Second, monkeypox has recently emerged as a public health crisis, and the virus has spread around the world in the post-COVID-19 period with a very rapid transmission rate. These two novel pathogens are a new concern for human health globally and have become a significant obstacle to the development of modern medicine and the economy of the whole world. Currently, there are no approved drugs for the treatment of this disease. So, this research gap encourages us to find a potential inhibitor from a natural source.

Methods and materials

In this study, a series of natural plant-based biomolecules were subjected to in-depth computational investigation to find the most potent inhibitors targeting major pathogenic proteins responsible for the diseases caused by these two pathogens.

Results

Among them, most of the selected natural compounds are predicted to bind tightly to the targeted proteins that are crucial for the replication of these novel pathogens. Moreover, all the molecules have outstanding ADMET properties such as high aqueous solubility, a higher human gastrointestinal absorption rate, and a lack of any carcinogenic or hepatotoxic effects; most of them followed Lipinski's rule. Finally, the stability of the compounds was determined by molecular dynamics simulations (MDs) for 100 ns. During MDs, we observed that the mentioned compounds have exceptional stability against selected pathogens.

Conclusion

These advanced computational strategies reported that 11 lead compounds, including dieckol and amentoflavone, exhibited high potency, excellent drug-like properties, and no toxicity. These compounds demonstrated strong binding affinities to the target enzymes, especially dieckol, which displayed superior stability during molecular dynamics simulations. The MM/PBSA method confirmed the favorable binding energies of amentoflavone and dieckol. However, further in vitro and in vivo studies are necessary to validate their efficacy. Our research highlights the role of Dieckol and Amentoflavone as promising candidates for inhibiting both monkeypox and Babesia microti, demonstrating their multifaceted roles in the control of these pathogens.

Impact of Applicability Domains to Generative Artificial Intelligence

Molecular generative artificial intelligence is drawing significant attention in the drug design community, with several experimentally validated proof of concepts already published. Nevertheless, generative models are known for sometimes generating unrealistic, unstable, unsynthesizable, or uninteresting structures. This calls for methods to constrain those algorithms to generate structures in drug-like portions of the chemical space. While the concept of applicability domains for predictive models is well studied, its counterpart for generative models is not yet well-defined. In this work, we empirically examine various possibilities and propose applicability domains suited for generative models. Using both public and internal data sets, we use generative methods to generate novel structures that are predicted to be actives by a corresponding quantitative structure-activity relationships model while constraining the generative model to stay within a given applicability domain. Our work looks at several applicability domain definitions, combining various criteria, such as structural similarity to the training set, similarity of physicochemical properties, unwanted substructures, and quantitative estimate of drug-likeness. We assess the structures generated from both qualitative and quantitative points of view and find that the applicability domain definitions have a strong influence on the drug-likeness of generated molecules. An extensive analysis of our results allows us to identify applicability domain definitions that are best suited for generating drug-like molecules with generative models. We anticipate that this work will help foster the adoption of generative models in an industrial context.

Drug and Anti-Viral Peptide Design to Inhibit the Monkeypox Virus by Restricting A36R Protein

Most recently, monkeypox virus (MPXV) has emanated as a global public health threat. Unavailability of effective medicament against MPXV escalates demand for new therapeutic agent. In this study, in silico strategies were conducted to identify novel drug against the A36R protein of MPXV. The A36R protein of MPXV is responsible for the viral migration, adhesion, and vesicle trafficking to the host cell. To block the A36R protein, 4893 potential antiviral peptides (AVPs) were retrieved from DRAMP and SATPdb databases. Finally, 57 sequences were screened based on peptide filtering criteria, which were then modeled. Likewise, 31 monkeypox virus A36R protein sequences were collected from NCBI protein database to find consensus sequence and to predict 3D protein model. The refined and validated models of the A36R protein and AVP peptides were used to predict receptor-ligand interactions using DINC 2 server. Three peptides that showed best interactions were SATPdb10193, SATPdb21850, and SATPdb26811 with binding energies -6.10, -6.10, and -6.30 kcal/mol, respectively. Small molecules from drug databases were also used to perform virtual screening against the A36R protein. Among databases, Enamine-HTSC showed strong affinity with docking scores ranging from -8.8 to 9.8 kcal/mol. Interaction of target protein A36R with the top 3 peptides and the most probable drug (Z55287118) examined by molecular dynamic (MD) simulation. Trajectory analyses (RMSD, RMSF, SASA, and Rg) confirmed the stable nature of protein-ligand and protein-peptide complexes. This work suggests that identified top AVPs and small molecules might interfere with the function of the A36R protein of MPXV.

Rings in Clinical Trials and Drugs: Present and Future

We present a comprehensive analysis of all ring systems (both heterocyclic and nonheterocyclic) in clinical trial compounds and FDA-approved drugs. We show 67% of small molecules in clinical trials comprise only ring systems found in marketed drugs, which mirrors previously published findings for newly approved drugs. We also show there are approximately 450 000 unique ring systems derived from 2.24 billion molecules currently available in synthesized chemical space, and molecules in clinical trials utilize only 0.1% of this available pool. Moreover, there are fewer ring systems in drugs compared with those in clinical trials, but this is balanced by the drug ring systems being reused more often. Furthermore, systematic changes of up to two atoms on existing drug and clinical trial ring systems give a set of 3902 future clinical trial ring systems, which are predicted to cover approximately 50% of the novel ring systems entering clinical trials.

Comparative Analyses of Medicinal Chemistry and Cheminformatics Filters with Accessible Implementation in Konstanz Information Miner (KNIME)

High-throughput virtual screening (HTVS) is, in conjunction with rapid advances in computer hardware, becoming a staple in drug design research campaigns and cheminformatics. In this context, virtual compound library design becomes crucial as it generally constitutes the first step where quality filtered databases are essential for the efficient downstream research. Therefore, multiple filters for compound library design were devised and reported in the scientific literature. We collected the most common filters in medicinal chemistry (PAINS, REOS, Aggregators, van de Waterbeemd, Oprea, Fichert, Ghose, Mozzicconacci, Muegge, Egan, Murcko, Veber, Ro3, Ro4, and Ro5) to facilitate their open access use and compared them. Then, we implemented these filters in the open platform Konstanz Information Miner (KNIME) as a freely accessible and simple workflow compatible with small or large compound databases for the benefit of the readers and for the help in the early drug design steps.

Biological and Cheminformatics Studies of Newly Designed Triazole Based Derivatives as Potent Inhibitors against Mushroom Tyrosinase

A series of nine novel 1,2,4-triazole based compounds were synthesized through a multistep reaction pathway and their structures were scrutinized by using spectral methods such as FTIR, LC-MS, 1H NMR, and 13C NMR. The synthesized derivatives were screened for inhibitory activity against the mushroom tyrosinase and we found that all the synthesized compounds demonstrated decent inhibitory activity against tyrosinase. However, among the series of compounds, N-(4-fluorophenyl)-2-(5-(2-fluorophenyl)-4-(4-fluorophenyl)-4H-1,2,4-triazol-3-ylthio) acetamide exhibited more prominent activity when accompanied with the standard drug kojic acid. Furthermore, the molecular docking studies identified the interaction profile of all synthesized derivatives at the active site of tyrosinase. Based on these results, N-(4-fluorophenyl)-2-(5-(2-fluorophenyl)-4-(4-fluorophenyl)-4H-1,2,4-triazol-3-ylthio) acetamide could be used as a novel scaffold to design some new drugs against melanogenesis.

Photoenzymatic Synthesis of α-Tertiary Amines by Engineered Flavin-Dependent "Ene"-Reductases

α-Tertiary amines are a common motif in pharmaceutically important molecules but are challenging to prepare using asymmetric catalysis. Here, we demonstrate engineered flavin-dependent 'ene'-reductases (EREDs) can catalyze radical additions into oximes to prepare this motif. Two different EREDs were evolved into competent catalysts for this transformation with high levels of stereoselectivity. Mechanistic studies indicate that the oxime contributes to the enzyme templated charge-transfer complex formed between the substrate and cofactor. These products can be further derivatized to prepare a variety of motifs, highlighting the versatility of ERED photoenzymatic catalysis for organic synthesis.

Combining Cell Envelope Stress Reporter Assays in a Screening Approach to Identify BAM Complex Inhibitors

The development of new antibiotics is particularly problematic in Gram-negative bacteria due to the presence of the outer membrane (OM), which serves as a permeability barrier. Recently, the β-barrel assembly machine (BAM), located in the OM and responsible for β-barrel type OM protein (OMP) assembly, has been validated as a novel target for antibiotics. Here, we identified potential BAM complex inhibitors using a screening approach that reports on cell envelope σE and Rcs stress in Escherichia coli. Screening a library consisting of 316 953 compounds yielded five compounds that induced σE and Rcs stress responses, while not inducing the intracellular heat-shock response. Two of the five compounds (compounds 2 and 14) showed the characteristics of known BAM complex inhibitors: synergy with OMP biogenesis mutants, decrease in the abundance of various OMPs, and loss of OM integrity. Importantly, compound 2 also inhibited BAM-dependent OMP folding in an in vitro refolding assay using purified BAM complex reconstituted in proteoliposomes.

Design, synthesis and in vitro biological evaluation of a novel class of anti-adenovirus agents based on 3-amino-1,2-propanediol

Nowadays there is not an effective drug for the treatment of infections caused by human adenovirus (HAdV) which supposes a clinical challenge, especially for paediatric and immunosuppressed patients. Here, we describe the design, synthesis and biological evaluation as anti-adenovirus agents of a new library (57 compounds) of diester, monoester and triazole derivatives based on 3-amino-1,2-propanediol skeleton. Seven compounds (17, 20, 26, 34, 44, 60 and 66) were selected based on their high anti-HAdV activity at low micromolar concentration (IC₅₀ from 2.47 to 5.75 µM) and low cytotoxicity (CC₅₀ from 28.70 to >200 µM). In addition, our mechanistic assays revealed that compounds 20 and 44 might be targeting specifically the HAdV DNA replication process, and compound 66 would be targeting HAdV E1A mRNA transcription. For compounds 17, 20, 34 and 60, the mechanism of action seems to be associated with later steps after HAdV DNA replication.

Investigating the binding affinity, molecular dynamics, and ADMET properties of 2,3-dihydrobenzofuran derivatives as an inhibitor of fungi, bacteria, and virus protein

Background

2,3-Dihydrobenzofurans (DHB) have proposed as advantages structures, and used as chemical entresol to design small compound libraries. The present study illustrates to explore 2,3-dihydrobenzofurans(DHB) in comparison to selected some derivatives drugs by using molecular docking and molecular dynamics, as well as ADMET studies. The online database “Molinspiration online server” was used to detect the physicochemical pharmacokinetics and drug likeness score of DHB drugs. For estimation of molecular docking, six pathogens, such as Aspergillus niger (PDB id: 1kum), Candida albicans (3dra), Escherichia coli (6og7), Salmonella typhi (4k6l), Influenza (1ru7), and Hepatitis C (4tyd), were chosen due to close biological studies.

Results

From Molinspiration online server has showed that DHB did not violate the “Lipinski five rule” as drugs, leading compound for molecular docking exhibited the potential interaction to the active residue. The binding affinity of DHB2 (−7.00 kcal/mol) against 3dra was higher than DHB8 (−6.40 kcal/mol) and DHB (5.70 kcal/mol) for compounds. The results of molecular docking show that the compounds mentioned in this study are not equally effective against pathogens, such as fungi, viruses, and bacteria. However, DHB2, DHB3, and DHB 8 compounds can work against almost given pathogens which results are derived from auto dock vina in terms of binding affinity around 6.00 kcal/mol, and Fire Dock has values from about 38.0 to 42.0 kcal/mol. To explore the dynamic nature of the interaction, 50 ns molecular dynamics (MD) simulation was performed on the selected protein-DHB complexes. Thus, DHB 8 has greater potential to interact for further for fungi.

Conclusion

Finding from this study can play an effective role as a drug in any biological system. This study as well recommends to researchers to synthesize these DHBs for evaluation of its biological activity.

Graphical abstract

Synthesis, Structure, Carbohydrate Enzyme Inhibition, Antioxidant Activity, In Silico Drug-Receptor Interactions and Drug-Like Profiling of the 5-Styryl-2-Aminochalcone Hybrids

The 2-amino-5-(3/4-fluorostyryl)acetophenones were prepared and reacted with benzaldehyde derivatives to afford the corresponding 5-styryl-2-aminochalcone hybrids. The trans geometry of the styryl and α,β-unsaturated carbonyl arms, and the presence of NH…O intramolecular hydrogen bond were validated using 1H-NMR and X-ray data. The 2-amino-5-styrylacetophenones and their 5-styryl-2-aminochalcone derivatives were screened in vitro for their capability to inhibit α-glucosidase and/or α-amylase activities. Their antioxidant properties were evaluated in vitro through the 2,2-diphenyl-1-picrylhydrazyl (DPPH) and nitric oxide (NO) free radical scavenging assays. Kinetic studies of the most active derivatives from each series against α-glucosidase and/or α-amylase activities have been performed supported by molecular docking studies to determine plausible protein-ligand interactions on a molecular level. The key aspects of the pharmacokinetics of these compounds, i.e., absorption, distribution, metabolism, and excretion have also been simulated at theoretical level. The most active compounds from each series, namely, 2a and 3e, were evaluated for cytotoxicity against the normal monkey kidney cells (Vero cells) and the adenocarcinomic human epithelial (A549) cell line to establish their safety profile at least in vitro.

In Vitro Enzymatic and Kinetic Studies, and In Silico Drug-Receptor Interactions, and Drug-Like Profiling of the 5-Styrylbenzamide Derivatives as Potential Cholinesterase and β-Secretase Inhibitors with Antioxidant Properties

The 5-(styryl)anthranilamides were transformed into the corresponding 5-styryl-2-(p-tolylsulfonamido)benzamide derivatives. These 5-styrylbenzamide derivatives were evaluated through enzymatic assays in vitro for their capability to inhibit acetylcholinesterase (AChE), butyrylcholinesterase (BChE), and β-secretase (BACE-1) activities as well as for antioxidant potential. An in vitro cell-based antioxidant activity assay involving lipopolysaccharides (LPS)-induced reactive oxygen species (ROS) production revealed that compounds 2a and 3b have the capability of scavenging free radicals. The potential of the most active compound, 5-styrylbenzamide (2a), to bind copper (II) or zinc (II) ions has also been evaluated spectrophotometrically. Kinetic studies of the most active derivatives from each series against the AChE, BChE, and β-secretase activities have been performed. The experimental results are complemented with molecular docking studies into the active sites of these enzymes to predict the hypothetical protein–ligand binding modes. Their drug likeness properties have also been predicted.

A FabG inhibitor targeting an allosteric binding site inhibits several orthologs from Gram-negative ESKAPE pathogens

The spread of antibiotic resistance within the ESKAPE group of human pathogenic bacteria poses severe challenges in the treatment of infections and maintenance of safe hospital environments. This motivates efforts to validate novel target proteins within these species that could be pursued as potential targets for antibiotic development. Genetic data suggest that the enzyme FabG, which is part of the bacterial fatty acid biosynthetic system FAS-II, is essential in several ESKAPE pathogens. FabG catalyzes the NADPH dependent reduction of 3-keto-acyl-ACP during fatty acid elongation, thus enabling lipid supply for production and maintenance of the cell envelope. Here we report on small-molecule screening on the FabG enzymes from A. baumannii and S. typhimurium to identify a set of µM inhibitors, with the most potent representative (1) demonstrating activity against six FabG-orthologues. A co-crystal structure with FabG from A. baumannii (PDB:6T65) confirms inhibitor binding at an allosteric site located in the subunit interface, as previously demonstrated for other sub-µM inhibitors of FabG from P. aeruginosa. We show that inhibitor binding distorts the oligomerization interface in the FabG tetramer and displaces crucial residues involved in the interaction with the co-substrate NADPH. These observations suggest a conserved allosteric site across the FabG family, which can be potentially targeted for interference with fatty acid biosynthesis in clinically relevant ESKAPE pathogens.

2-Thiopyrimidine/chalcone hybrids: design, synthesis, ADMET prediction, and anticancer evaluation as STAT3/STAT5a inhibitors

A novel 2-thiopyrimidine/chalcone hybrid was designed, synthesised, and evaluated for their cytotoxic activities against three different cell lines, K-562, MCF-7, and HT-29. The most active cytotoxic derivatives were 9d, 9f, 9n, and 9p (IC50=0.77-1.74 µM, against K-562 cell line), 9a and 9r (IC50=1.37-3.56 µM against MCF-7 cell line), and 9a, 9l, and 9n (IC50=2.10 and 2.37 µM against HT-29 cell line). Compounds 9a, 9d, 9f, 9n, and 9r were further evaluated for their cytotoxicity against normal fibroblast cell line WI38. Moreover, STAT3 and STAT5a inhibitory activities were determined for the most active derivatives 9a, 9d, 9f, 9n, and 9r. Dual inhibitory activity was observed in compound 9n (IC50=113.31 and 50.75 µM, against STAT3 and STAT5a, respectively). Prediction of physicochemical properties, drug likeness score, pharmacokinetic and toxic properties was detected.

The putative binding site and SAR rationalization of small molecules against glucagon-like peptide-1 receptor using homology model and crystal structures: a comparative study

Glucagon-like peptide-1 (GLP-1) is involved in glucose-stimulated insulin secretion and weight regulating actions through the activation of the GLP-1 receptor (GLP-1R). Clinical effectiveness of GLP-1 mimetics is effective in improving glucose control in patients. Thus, identifying and developing orally active small-molecule agonists are highly desirable. This study summarizes the structure-function relationship of hGLP-1R through computational approaches and search of small molecule GLP-1R agonists. We carried out mutation guided data-driven study, for developing the GLP-1R model to explore and validate the putative site for quinoxaline analogues. The developed GLP-1R homology model was subjected to 500 ns MD simulation for validation. Various snapshots were considered to identify the best structure of GLP-1R based on correlation between experimental pEC₅₀ and various theoretical parameters (docking score, MM-GBSA ΔG bind, WM/MM ΔG bind). The putative binding site (Sitemap and WaterMap has been predicted and it matched well with the available data. Excellent correlation (R² =0.94), between pEC50 and WM/MM ΔG bind for the snapshot at 350 ns was observed after including induced-fit docking results of the most potent molecule. Enrichment calculation indicates better AUC (=0.75) for predicted complex structure. A comparison of the developed GLP-1R model with the available crystal structure shows excellent similarities and it was used for virtual screening to find small molecule agonists. The good correlation of our model with crystal structures of GLP-1R may help to understand the structure-function relationship of other secretin families.

Ester and amide derivatives of rhodamine B exert cytotoxic effects on different human tumor cell lines

Three esters of rhodamine B (1–3) differing in their alkyl chain lengths as well as several rhodamine B amides (4–9) were synthesized in good yields and tested for their cytotoxicity in SRB assays employing several human tumor cell lines. The rhodamine B esters were unselective but showed cytotoxicity of as low as EC50 = 0.15 ± 0.02 µM. The rhodamine B amides were slightly less cytotoxic but showed good selectivity against MCF-7 and A2780 tumor cell lines. Especially a morpholinyl derivative 4 was ~20 time more cytotoxic for MCF-7 than for nonmalignant NIH 3T3 cells.

Structure-Based Discovery of Dual-Target Hits for Acetylcholinesterase and the α7 Nicotinic Acetylcholine Receptors: In Silico Studies and In Vitro Confirmation

Despite extensive efforts in the development of drugs for complex neurodegenerative diseases, treatment often remains challenging or ineffective, and hence new treatment strategies are necessary. One approach is the design of multi-target drugs, which can potentially address the complex nature of disorders such as Alzheimer's disease. We report a method for high throughput virtual screening aimed at identifying new dual target hit molecules. One of the identified hits, N,N-dimethyl-1-(4-(3-methyl-[1,2,4]triazolo[4,3-a]pyrimidin-6-yl)phenyl)ethan-1-amine (Ý;mir-2), has dual-activity as an acetylcholinesterase (AChE) inhibitor and as an α7 nicotinic acetylcholine receptor (α7 nAChR) agonist. Using computational chemistry methods, parallel and independent screening of a virtual compound library consisting of 3,848,234 drug-like and commercially available molecules from the ZINC15 database, resulted in an intersecting set of 57 compounds, that potentially possess activity at both of the two protein targets. Based on ligand efficiency as well as scaffold and molecular diversity, 16 of these compounds were purchased for in vitro validation by Ellman's method and two-electrode voltage-clamp electrophysiology. Ý;mir-2 was shown to exhibit the desired activity profile (AChE IC₅₀ = 2.58 ± 0.96 µM; α7 nAChR activation = 7.0 ± 0.9% at 200 µM) making it the first reported compound with this particular profile and providing further evidence of the feasibility of in silico methods for the identification of novel multi-target hit molecules.

Phenotypic Screening of Chemical Libraries Enriched by Molecular Docking to Multiple Targets Selected from Glioblastoma Genomic Data

Like most solid tumors, glioblastoma multiforme (GBM) harbors multiple overexpressed and mutated genes that affect several signaling pathways. Suppressing tumor growth of solid tumors like GBM without toxicity may be achieved by small molecules that selectively modulate a collection of targets across different signaling pathways, also known as selective polypharmacology. Phenotypic screening can be an effective method to uncover such compounds, but the lack of approaches to create focused libraries tailored to tumor targets has limited its impact. Here, we create rational libraries for phenotypic screening by structure-based molecular docking chemical libraries to GBM-specific targets identified using the tumor's RNA sequence and mutation data along with cellular protein-protein interaction data. Screening this enriched library of 47 candidates led to several active compounds, including 1 (IPR-2025), which (i) inhibited cell viability of low-passage patient-derived GBM spheroids with single-digit micromolar IC50 values that are substantially better than standard-of-care temozolomide, (ii) blocked tube-formation of endothelial cells in Matrigel with submicromolar IC50 values, and (iii) had no effect on primary hematopoietic CD34+ progenitor spheroids or astrocyte cell viability. RNA sequencing provided the potential mechanism of action for 1, and mass spectrometry-based thermal proteome profiling confirmed that the compound engages multiple targets. The ability of 1 to inhibit GBM phenotypes without affecting normal cell viability suggests that our screening approach may hold promise for generating lead compounds with selective polypharmacology for the development of treatments of incurable diseases like GBM.

Beware of the generic machine learning-based scoring functions in structure-based virtual screening

Machine learning-based scoring functions (MLSFs) have attracted extensive attention recently and are expected to be potential rescoring tools for structure-based virtual screening (SBVS). However, a major concern nowadays is whether MLSFs trained for generic uses rather than a given target can consistently be applicable for VS. In this study, a systematic assessment was carried out to re-evaluate the effectiveness of 14 reported MLSFs in VS. Overall, most of these MLSFs could hardly achieve satisfactory results for any dataset, and they could even not outperform the baseline of classical SFs such as Glide SP. An exception was observed for RFscore-VS trained on the Directory of Useful Decoys-Enhanced dataset, which showed its superiority for most targets. However, in most cases, it clearly illustrated rather limited performance on the targets that were dissimilar to the proteins in the corresponding training sets. We also used the top three docking poses rather than the top one for rescoring and retrained the models with the updated versions of the training set, but only minor improvements were observed. Taken together, generic MLSFs may have poor generalization capabilities to be applicable for the real VS campaigns. Therefore, it should be quite cautious to use this type of methods for VS.

Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?

The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.

Off-Pocket Activity Cliffs: A Puzzling Facet of Molecular Recognition

While accurate quantitative prediction of ligand-protein binding affinity remains an elusive goal, high-affinity ligands to therapeutic targets are being designed through heuristic optimization of ligand-protein contacts. However, herein, through large-scale data mining and analyses, we demonstrate that a ligand's binding can also be strongly affected through modifying its solvent-exposed portion that does not make contacts with the protein, thus resulting in "off-pocket activity cliffs" (OAC). We then exposed the roots of the OAC phenomenon by means of molecular dynamics (MD) simulations and MD data analyses. We expect OAC to extend our knowledge of molecular recognition and enhance the drug designer's toolkit.

Designing of promising medicinal scaffolds for Alzheimer's disease through enzyme inhibition, lead optimization, molecular docking and dynamic simulation approaches

In the designed research work, a series of 2-furoyl piperazine based sulfonamide derivatives were synthesized as therapeutic agents to target the Alzheimer's disease. The structures of the newly synthesized compounds were characterized through spectral analysis and their inhibitory potential was evaluated against butyrylcholinesterase (BChE). The cytotoxicity of these sulfonamides was also ascertained through hemolysis of bovine red blood cells. Furthermore, compounds were inspected by Lipinki Rule and their binding profiles against BChE were discerned by molecular docking. The protein fluctuations in docking complexes were recognized by dynamic simulation. From our in vitro and in silico results 5c, 5j and 5k were identified as promising lead compounds for the treatment of targeted disease.

Exposing Small-Molecule Nanoentities by a Nuclear Magnetic Resonance Relaxation Assay

Small molecules can self-assemble in aqueous solution into a wide range of nanoentity types and sizes (dimers, n-mers, micelles, colloids, etc.), each having their own unique properties. This has important consequences in the context of drug discovery including issues related to nonspecific binding, off-target effects, and false positives and negatives. Here, we demonstrate the use of the spin-spin relaxation Carr-Purcell-Meiboom-Gill NMR experiment, which is sensitive to molecular tumbling rates and can expose larger aggregate species that have slower rotational correlations. The strategy easily distinguishes lone-tumbling molecules versus nanoentities of various sizes. The technique is highly sensitive to chemical exchange between single-molecule and aggregate states and can therefore be used as a reporter when direct measurement of aggregates is not possible by NMR. Interestingly, we found differences in solution behavior for compounds within structurally related series, demonstrating structure-nanoentity relationships. This practical experiment is a valuable tool to support drug discovery efforts.

Identification of Small Molecules Exhibiting Oxacillin Synergy through a Novel Assay for Inhibition of vraTSR Expression in Methicillin-Resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) strains that are resistant to all forms of penicillin have become an increasingly common and urgent problem threatening human health. They are responsible for a wide variety of infectious diseases ranging from minor skin abscesses to life-threatening severe infections. The vra operon that is conserved among S. aureus strains encodes a three-component signal transduction system (vraTSR) that is responsible for sensing and responding to cell wall stress. We developed a novel and multifaceted assay to identify compounds that potentiate the activity of oxacillin, essentially restoring efficacy of oxacillin against MRSA, and performed high-throughput screening (HTS) to identify oxacillin potentiators. HTS of 13,840 small-molecule compounds from an antimicrobial-focused Life Chemicals library, using the MRSA cell-based assay, identified three different inhibitor scaffolds. Checkerboard assays for synergy with oxacillin, reverse transcriptase PCR (RT-PCR) assays against vraR expression, and direct confirmation of interaction with VraS by surface plasmon resonance (SPR) further verified them to be viable hit compounds. A subsequent structure-activity relationship (SAR) study of the best scaffold with diverse analogs was utilized to improve potency and provides a strong foundation for further development.

Discovery of Clinical Candidate (1R,4r)-4-((R)-2-((S)-6-Fluoro-5H-imidazo[5,1-a]isoindol-5-yl)-1-hydroxyethyl)cyclohexan-1-ol (Navoximod), a Potent and Selective Inhibitor of Indoleamine 2,3-Dioxygenase 1

A novel class of 5-substituted 5H-imidazo[5,1-a]isoindoles are described as potent inhibitors of indoleamine 2,3-dioxygenase 1 (IDO1). A structure-based drug design approach was used to elaborate the 5H-imidazo[5,1-a]isoindole core and to improve potency and pharmacological properties. Suitably placed hydrophobic and polar functional groups in the lead molecule allowed improvement of IDO1 inhibitory activity while minimizing off-target liabilities. Structure-activity relationship studies focused on optimizing IDO1 inhibition potency and a pharmacokinetic profile amenable to oral dosing while controlling CYP450 and hERG inhibitory properties.

Studies of Jatrogossone A as a Reactive Oxygen Species Inducer in Cancer Cellular Models

Natural products continue to provide a platform to study biological systems. A bioguided study of cancer cell models led us to a new member of the jatrophane natural products from Jatropha gossypiifolia, which was independently identified and characterized as jatrogossone A (1). Purification and structure elucidation was performed by column chromatography and high-performance liquid chromatography-mass spectrometry and NMR techniques, and the structure was confirmed via X-ray crystallography. The unique molecular scaffold of jatrogossone A prompted an evaluation of its mode of action. Cytotoxicity assays demonstrated that jatrogossone A displays selective antiproliferative activity against cancer cell models in the low micromolar range with a therapeutic window. Jatrogossone A (1) affects mitochondrial membrane potential (ΔΨm) in a time- and dose-dependent manner. This natural product induces radical oxygen species (ROS) selectively in cancer cellular models, with minimal ROS induction in noncancerous cells. Compound 1 induces ROS in the mitochondria, as determined by colocalization studies, and it induces mitophagy. It promotes also in vitro cell death by causing cell arrest at the G2/M stage, caspase (3/7) activation, and PARP-1 cleavage. The combined findings provide a potential mechanism by which 1 relies on upregulation of mitochondrial ROS to potentiate cytotoxic effects through intracellular signaling.

Mechanisms of Specific versus Nonspecific Interactions of Aggregation-Prone Inhibitors and Attenuators

A common source of false positives in drug discovery is ligand self-association into large colloidal assemblies that nonspecifically inhibit target proteins. However, the mechanisms of aggregation-based inhibition (ABI) and ABI-attenuation by additives, such as Triton X-100 (TX) and human serum albumin (HSA), are not fully understood. Here, we investigate the molecular basis of ABI and ABI-attenuation through the lens of NMR and coupled thermodynamic cycles. We unexpectedly discover a new class of aggregating ligands that exhibit negligible interactions with proteins but act as competitive sinks for the free inhibitor, resulting in bell-shaped dose-response curves. TX attenuates ABI by converting inhibitory, protein-binding aggregates into nonbinding coaggregates, whereas HSA minimizes nonspecific ligand interactions by functioning as a reservoir for free inhibitor and preventing self-association. Hence, both TX and HSA are useful tools to minimize false positives arising from nonspecific binding but at the cost of potentially introducing false negatives due to suppression of specific interactions.

Computational advances in combating colloidal aggregation in drug discovery

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.

Inhibition of Oncogenic Kinases: An In Vitro Validated Computational Approach Identified Potential Multi-Target Anticancer Compounds

Tumorigenesis in humans is a multistep progression that imitates genetic changes leading to cell transformation and malignancy. Oncogenic kinases play a central role in cancer progression, rendering them putative targets for the design of anti-cancer drugs. The presented work aims to identify the potential multi-target inhibitors of oncogenic receptor tyrosine kinases (RTKs) and serine/threonine kinases (STKs). For this, chemoinformatics and structure-based virtual screening approaches were combined with an in vitro validation of lead hits on both cancerous and non-cancerous cell lines. A total of 16 different kinase structures were screened against ~739,000 prefiltered compounds using diversity selection, after which the top hits were filtered for promising pharmacokinetic properties. This led to the identification of 12 and 9 compounds against RTKs and STKs, respectively. Molecular dynamics (MD) simulations were carried out to better comprehend the stability of the predicted hit kinase-compound complexes. Two top-ranked compounds against each kinase class were tested in vitro for cytotoxicity, with compound F34 showing the most promising inhibitory activity in HeLa, HepG2, and Vero cell lines with IC50 values of 145.46 μM, 175.48 μM, and 130.52 μM, respectively. Additional docking of F34 against various RTKs was carried out to support potential multi-target inhibition. Together with reliable MD simulations, these results suggest the promising potential of identified multi-target STK and RTK scaffolds for further kinase-specific anti-cancer drug development toward combinatorial therapies.

Beyond the Flavour: The Potential Druggability of Chemosensory G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) belong to the largest class of drug targets. Approximately half of the members of the human GPCR superfamily are chemosensory receptors, including odorant receptors (ORs), trace amine-associated receptors (TAARs), bitter taste receptors (TAS2Rs), sweet and umami taste receptors (TAS1Rs). Interestingly, these chemosensory GPCRs (csGPCRs) are expressed in several tissues of the body where they are supposed to play a role in biological functions other than chemosensation. Despite their abundance and physiological/pathological relevance, the druggability of csGPCRs has been suggested but not fully characterized. Here, we aim to explore the potential of targeting csGPCRs to treat diseases by reviewing the current knowledge of csGPCRs expressed throughout the body and by analysing the chemical space and the drug-likeness of flavour molecules.

Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (-)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening.

Exploration of synthetic multifunctional amides as new therapeutic agents for Alzheimer's disease through enzyme inhibition, chemoinformatic properties, molecular docking and dynamic simulation insights

A new series of multifunctional amides has been synthesized having moderate enzyme inhibitory potentials and mild cytotoxicity. 2-Furyl(1-piperazinyl)methanone (1) was coupled with 3,5-dichloro-2-hydroxybenzenesulfonyl chloride (2) to form {4-[(3,5-dichloro-2-hydroxyphenyl)sulfonyl]-1-piperazinyl}(2-furyl)methanone (3). Different elecrophiles were synthesized by the reaction of various un/substituted anilines (4a-o) with 2-bromoacetylbromide (5), 2‑bromo‑N-(un/substituted-phenyl)acetamides (6a-o). Further, equimolar ratios of 3 and 6a-o were allowed to react in the presence of K₂CO₃ in acetonitrile to form desired multifunctional amides (7a-o). The structural confirmation of all the synthesized compounds was carried out by their EI-MS, IR, ¹H NMR and ¹³C NMR spectral data. Enzyme inhibition activity was performed against acetyl and butyrylcholinestrase enzymes, whereby 7e showed very good activity having IC₅₀ value of 5.54 ± 0.03 and 9.15 ± 0.01 μM, respectively, relative to eserine, a reference standard. Hemolytic activity of the molecules was checked to asertain their cytotoxicity towards red blood cell membrance and it was observed that most of the compounds were not toxic up to certain range. Moreover, chemoinformatic protepties and docking simulation results also showed the significance of 7e as compared to other compounds. Based on in vitro and in silico analysis 7e could be used as a template for the development of new drugs against Alzheimer's disease.

Identification of inhibitors of Tartrate-resistant acid phosphatase (TRAP/ACP5) activity by small-molecule screening

Tartrate-resistant acid phosphatase (TRAP/ACP5) occurs as two isoforms-TRAP 5a with low enzymatic activity due to a loop interacting with the active site and the more active TRAP isoform 5b generated upon proteolytic cleavage of this loop. TRAP has been implicated in several diseases, including cancer. Thus, this study set out to identify small-molecule inhibitors of TRAP activity. A microplate-based enzymatic assay for TRAP 5b was applied in a screen of 30,315 compounds, resulting in the identification of 90 primary hits. After removal of promiscuous compounds, unwanted groups, and false positives by orthogonal assays and three-concentration validation, the properties of 52 compounds were further investigated to better understand their mechanism of action. Full-concentration-response curves for these compounds were established under different enzyme concentrations and (pre)incubation times to remove compounds with inconsistent results and low potencies. Full-concentration-response curves were also performed for both isoforms, to examine isoform prevalence. Filtering led to six prioritized compounds, representing different clusters. One of these, CBK289001 or (6S)-6-[3-(2H-1,3-benzodioxol-5-yl)-1,2,4-oxadiazol-5-yl]-N-(propan-2-yl)-1H,4H,5H,6H,7H-imidazo[4,5-c]pyridine-5-carboxamide, demonstrated efficacy in a migration assay and IC₅₀ values from 4 to 125 μm. Molecular docking studies and analog testing were performed around CBK289001 to provide openings for further improvement toward more potent blockers of TRAP activity.

Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation

The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process, is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This chapter focuses on the application of computational molecular filters, applied either prescreening or postscreening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.