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Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional highthroughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for "one drug-multiple targets". Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach. [ABSTRACT FROM AUTHOR]

Schistosomiasis is a parasitic neglected disease with praziquantel (PZQ) utilized as the main drug for treatment, despite its low effectiveness against early stages of the worm. To aid in the search for new drugs to tackle schistosomiasis, computer-aided drug design has been proved a helpful tool to enhance the search and initial identification of schistosomicidal compounds, allowing fast and cost-efficient progress in drug discovery. The combination of high-throughput in silico data followed by in vitro phenotypic screening assays allows the assessment of a vast library of compounds with the potential to inhibit a single or even several biological targets in a more time- and cost-saving manner. Here, we describe the molecular docking for in silico screening of predicted homology models of five protein kinases (JNK, p38, ERK1, ERK2, and FES) of Schistosoma mansoni against approximately 85,000 molecules from the Managed Chemical Compounds Collection (MCCC) of the University of Nottingham (UK). We selected 169 molecules predicted to bind to SmERK1, SmERK2, SmFES, SmJNK, and/or Smp38 for in vitro screening assays using schistosomula and adult worms. In total, 89 (52.6%) molecules were considered active in at least one of the assays. This approach shows a much higher efficiency when compared to using only traditional high-throughput in vitro screening assays, where initial positive hits are retrieved from testing thousands of molecules. Additionally, when we focused on compound promiscuity over selectivity, we were able to efficiently detect active compounds that are predicted to target all kinases at the same time. This approach reinforces the concept of polypharmacology aiming for “one drug-multiple targets”. Moreover, at least 17 active compounds presented satisfactory drug-like properties score when compared to PZQ, which allows for optimization before further in vivo screening assays. In conclusion, our data support the use of computer-aided drug design methodologies in conjunction with high-throughput screening approach.

Flavonoids are secondary metabolites that are widely found in various medicinal plants. They are known for their medicinal benefits and have been extensively used in healthcare industries and in the management of age-related diseases. This paper focuses on flavonoids from Oroxylum indicum , a significant medicinal tree in the practice of traditional Indian medicine. O. indicum has been utilized in a variety of polyherbal formulations for the management of musculoskeletal disorders, however the mechanism of action of its bioactive flavonoids remains unknown. The present study aimed to identify the flavonoids of O. indicum with the potential to target sclerostin, an antagonist of canonical Wnt signaling pathway for the treatment of bone-related disorders. Molecular docking, coarse-grained and molecular dynamics simulations were performed to screen the major flavonoids and investigate their interaction with sclerostin. Flavonoids with highest binding affinity and interacting with at least one of the amino acids of the PNAIG motif residues were selected from docking studies and subjected to further drug likeness and ADMET screening. Further screening from coarse-grained and molecular dynamic simulations results showed that baicalein, compared to other screened flavonoids, stably binds with the important residues of the LRP6 binding site of sclerostin, resulting in pronounced structural changes in the protein. These findings suggest that baicalein from O. indicum can potentially inhibit sclerostin and can elicit skeletal protective effects, providing an insight for further in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

Abstract Background The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity. Results We present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance. While our best result is achieved with a combination of molecular feature types (gene expression, microRNA and proteome), we show that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, we rank the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity. Conclusions We present promising results in applying deep learning to predicting combinational drug response. Our feature analysis indicates screening data involving more cell lines are needed for the models to make better use of molecular features.

SARS-CoV-2 must bind its principal receptor, ACE2, on the target cell to initiate infection. This interaction is largely driven by the receptor binding domain (RBD) of the viral Spike (S) protein. Accordingly, antiviral compounds that can block RBD/ACE2 interactions can constitute promising antiviral agents. To identify such molecules, we performed a virtual screening of the Selleck FDA approved drugs and the Selleck database of Natural Products using a multistep computational procedure. An initial set of candidates was identified from an ensemble docking process using representative structures determined from the analysis of four 3 μ s molecular dynamics trajectories of the RBD/ACE2 complex. Two procedures were used to construct an initial set of candidates including a standard and a pharmacophore guided docking procedure. The initial set was subsequently subjected to a multistep sieving process to reduce the number of candidates to be tested experimentally, using increasingly demanding computational procedures, including the calculation of the binding free energy computed using the MMPBSA and MMGBSA methods. After the sieving process, a final list of 10 candidates was proposed, compounds which were subsequently purchased and tested ex-vivo. The results identified estradiol cypionate and telmisartan as inhibitors of SARS-CoV-2 entry into cells. Our findings demonstrate that the methodology presented here enables the discovery of inhibitors targeting viruses for which high-resolution structures are available., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Hepatocellular carcinoma (HCC) is one of the most aggressive and life-threatening cancers. Although multiple treatment options are available, the prognosis of HCC patients is poor due to metastasis and drug resistance. Hence, discovering novel targets is essential for better therapeutic development for HCC. In this study, we used the cancer genome atlas (TCGA) dataset to analyze the expression of bromodomain-containing proteins in HCC, as bromodomains are emerging attractive therapeutic targets. Our analysis identified BRPF1 as the most highly upregulated gene in HCC among the 43 bromodomain-containing genes. Upregulation of BRPF1 was significantly associated with poorer patient survival. Therefore, targeting BRPF1 may be an approach for HCC treatment. Previously, several potential inhibitors of BRPF1 bromodomain have been discovered. However, due to the limited clinical success of the current inhibitors, we aim to search for new inhibitors with high affinity and specificity for the BRPF1 bromodomain. In this study, we utilized high-throughput virtual screening methods to screen synthetic and natural compound databases against the BRPF1 bromodomain. In addition, we used machine learning-based QSAR modeling to predict the IC50 values of the selected BRPF1 bromodomain inhibitors. Extensive MD simulations were used to calculate the binding free energies of BRPF1 bromodomain and inhibitor complexes. Using this approach, we identified four lead scaffolds with a similar or better binding affinity towards the BRPF1 bromodomain than the previously reported inhibitors. Overall, this study discovered some promising compounds that have the potential to act as potent BRPF1 bromodomain inhibitors. [Display omitted] • The bromodomain-PHD finger protein 1 (BRPF1) is highly mutated in hepatocellular carcinoma (HCC). • Targetting BRPF1 could be a novel therapeutic approach to treat HCC. • Virtual screening of BRPF1 inhibitors followed by QSAR modeling predicted the IC50 values of selected screened inhibitors. • MD simulation and MM/PBSA methods validated the screened inhibitors against BRPF1. • Four lead scaffolds were identified against the BRPF1 with similar or better than the previously reported inhibitors. [ABSTRACT FROM AUTHOR]

Objective/Background: Mycobacterium tuberculosis thymidine monophosphate kinase (mtTMPK) is a potential enzymatic target for the treatment of tuberculosis (TB). Materials and Methods: In this study, we performed pharmacophore-based in silico screening, targeting mtTMPK with a compound library of 461,383 chemicals. We evaluated the candidate compounds for inhibitory effects on the growth of the model mycobacteria, Mycobacterium smegmatis. Results: The compound KTP3 completely inhibited the growth of M. smegmatis at 100 μM. A similarity search and rescreening with the structure of compound KTP3 using a web-based database identified two similar compounds (KTPS1 and KTPS2) with improved potency. The KTP3 analogs, KTPS1 and KTPS2, exhibited strong growth inhibitory effects with half-maximal inhibitory concentration values of 8.04 μM and 17.1 μM, respectively, against M. smegmatis. Moreover, the most potent chemical compound, KTPS1, did not exhibit toxic effects on the model enterobacteria and several mammalian cells. Two active chemicals, KTPS1 and KTPS2, inhibited mtTMPK activity by 18% and 36%, respectively, suggesting that these compounds have off-target activities against Mycobacterium. Conclusion: Structural and biological information on these chemicals is likely to be useful for the development of novel antibiotics for the treatment of TB.

The pathophysiology of atrial fibrillation (AF) is broad, with components related to the unique and diverse cellular electrophysiology of atrial myocytes, structural complexity, and heterogeneity of atrial tissue, and pronounced disease-associated remodeling of both cells and tissue. A major challenge for rational design of AF therapy, particularly pharmacotherapy, is integrating these multiscale characteristics to identify approaches that are both efficacious and independent of ventricular contraindications. Computational modeling has long been touted as a basis for achieving such integration in a rapid, economical, and scalable manner. However, computational pipelines for AF-specific drug screening are in their infancy, and while the field is progressing quite rapidly, major challenges remain before computational approaches can fill the role of workhorse in rational design of AF pharmacotherapies. In this review, we briefly detail the unique aspects of AF pathophysiology that determine requirements for compounds targeting AF rhythm control, with emphasis on delimiting mechanisms that promote AF triggers from those providing substrate or supporting reentry. We then describe modeling approaches that have been used to assess the outcomes of drugs acting on established AF targets, as well as on novel promising targets including the ultra-rapidly activating delayed rectifier potassium current, the acetylcholine-activated potassium current and the small conductance calcium-activated potassium channel. Finally, we describe how heterogeneity and variability are being incorporated into AF-specific models, and how these approaches are yielding novel insights into the basic physiology of disease, as well as aiding identification of the important molecular players in the complex AF etiology.

MicroRNAs are recognized as key drivers in many cancers but targeting them with small molecules remains a challenge. We present RiboStrike, a deep-learning framework that identifies small molecules against specific microRNAs. To demonstrate its capabilities, we applied it to microRNA-21 (miR-21), a known driver of breast cancer. To ensure selectivity toward miR-21, we performed counter-screens against miR-122 and DICER. Auxiliary models were used to evaluate toxicity and rank the candidates. Learning from various datasets, we screened a pool of nine million molecules and identified eight, three of which showed anti-miR-21 activity in both reporter assays and RNA sequencing experiments. Target selectivity of these compounds was assessed using microRNA profiling and RNA sequencing analysis. The top candidate was tested in a xenograft mouse model of breast cancer metastasis, demonstrating a significant reduction in lung metastases. These results demonstrate RiboStrike's ability to nominate compounds that target the activity of miRNAs in cancer., Competing Interests: The work has been patented by the University of Central Florida (UCF) and the University of California San Francisco (UCSF) under the title of “Deep-Learning Based Methods for Virtual Screening of Molecules for Micro Ribonucleic Acid (miRNA) Drug Discovery” (application number: 63/309,132)., (© 2023 The Authors.)

Background: The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity. Results: We present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance. While our best result is achieved with a combination of molecular feature types (gene expression, microRNA and proteome), we show that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, we rank the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity. Conclusions: We present promising results in applying deep learning to predicting combinational drug response. Our feature analysis indicates screening data involving more cell lines are needed for the models to make better use of molecular features. [ABSTRACT FROM AUTHOR]

The pathophysiology of atrial fibrillation (AF) is broad, with components related to the unique and diverse cellular electrophysiology of atrial myocytes, structural complexity, and heterogeneity of atrial tissue, and pronounced disease-associated remodeling of both cells and tissue. A major challenge for rational design of AF therapy, particularly pharmacotherapy, is integrating these multiscale characteristics to identify approaches that are both efficacious and independent of ventricular contraindications. Computational modeling has long been touted as a basis for achieving such integration in a rapid, economical, and scalable manner. However, computational pipelines for AF-specific drug screening are in their infancy, and while the field is progressing quite rapidly, major challenges remain before computational approaches can fill the role of workhorse in rational design of AF pharmacotherapies. In this review, we briefly detail the unique aspects of AF pathophysiology that determine requirements for compounds targeting AF rhythm control, with emphasis on delimiting mechanisms that promote AF triggers from those providing substrate or supporting reentry. We then describe modeling approaches that have been used to assess the outcomes of drugs acting on established AF targets, as well as on novel promising targets including the ultra-rapidly activating delayed rectifier potassium current, the acetylcholine-activated potassium current and the small conductance calcium-activated potassium channel. Finally, we describe how heterogeneity and variability are being incorporated into AF-specific models, and how these approaches are yielding novel insights into the basic physiology of disease, as well as aiding identification of the important molecular players in the complex AF etiology. [ABSTRACT FROM AUTHOR]

Objective/Background: Mycobacterium tuberculosis thymidine monophosphate kinase (mtTMPK) is a potential enzymatic target for the treatment of tuberculosis (TB). Materials and Methods: In this study, we performed pharmacophore-based in silico screening, targeting mtTMPK with a compound library of 461,383 chemicals. We evaluated the candidate compounds for inhibitory effects on the growth of the model mycobacteria, Mycobacterium smegmatis. Results: The compound KTP3 completely inhibited the growth of M. smegmatis at 100 µM. A similarity search and rescreening with the structure of compound KTP3 using a web-based database identiied two similar compounds (KTPS1 and KTPS2) with improved potency. The KTP3 analogs, KTPS1 and KTPS2, exhibited strong growth inhibitory effects with half-maximal inhibitory concentration values of 8.04 µM and 17.1 µM, respectively, against M. smegmatis. Moreover, the most potent chemical compound, KTPS1, did not exhibit toxic effects on the model enterobacteria and several mammalian cells. Two active chemicals, KTPS1 and KTPS2, inhibited mtTMPK activity by 18% and 36%, respectively, suggesting that these compounds have off-target activities against Mycobacterium. Conclusion: Structural and biological information on these chemicals is likely to be useful for the development of novel antibiotics for the treatment of TB. [ABSTRACT FROM AUTHOR]

Altered molecular responses to insulin and growth factors ( GF) are responsible for late-life shortening diseases such as type-2 diabetes mellitus (T2 DM) and cancers. We have built a network of the signaling pathways that control S-phase entry and a specific type of senescence called geroconversion. We have translated this network into a Boolean model to study possible cell phenotype outcomes under diverse molecular signaling conditions. In the context of insulin resistance, the model was able to reproduce the variations of the senescence level observed in tissues related to T2 DM's main morbidity and mortality. Furthermore, by calibrating the pharmacodynamics of mTOR inhibitors, we have been able to reproduce the dose-dependent effect of rapamycin on liver degeneration and lifespan expansion in wild-type and HER2-neu mice. Using the model, we have finally performed an in silico prospective screen of the risk-benefit ratio of rapamycin dosage for healthy lifespan expansion strategies. We present here a comprehensive prognostic and predictive systems biology tool for human aging. [ABSTRACT FROM AUTHOR]

The Mycobacterium tuberculosis ( M. tuberculosis ) enoyl-acyl carrier protein reductase (mtInhA) is an attractive enzyme and a thoroughly studied target for tuberculosis therapy. In this study, to identify novel structure-activity relationships (SARs) of mtInhA inhibitors, a series of diphenyl ether derivatives were designed based on the matched molecular pair (MMP) method, and the binding energies of these compounds were subsequently estimated by in silico structure-based drug screening (SBDS) to provide more useful data. Consequently, the 10 unique candidate compounds (KEM1-KEM10) were identified and assessed for the inhibition of mtInhA enzymatic activity, in vitro antibiotic effects against model mycobacteria and toxicity level on both intestinal bacteria and mammalian cells. Among the compounds tested, phenyl group (KEM4) and 2-fluorobenzyl group (KEM7) substitutions produced preferable inhibitory effects on mtInhA enzymatic activity relative to those provided by a furyl group (KES4: base compound) at the terminal of the compound, and KEM7 inhibited the growth of the mycobacteria strain with a lower IC 50 value. Moreover, most of the candidate compounds exhibited neither inhibition of the growth of enterobacteria nor toxic effects on mammalian cells, though KEM10 exhibited toxicity against cultured MDCK cells. The structural and experimental information concerning these mtInhA inhibitors identified through MMP-based in silico screening will likely contribute to the lead optimisation of novel antibiotics for M. tuberculosis . [ABSTRACT FROM AUTHOR]

Epidemiological studies reveal that marijuana increases the risk of cardiovascular disease (CVD); however, little is known about the mechanism. Δ 9 -tetrahydrocannabinol (Δ 9 -THC), the psychoactive component of marijuana, binds to cannabinoid receptor 1 (CB1/CNR1) in the vasculature and is implicated in CVD. A UK Biobank analysis found that cannabis was an risk factor for CVD. We found that marijuana smoking activated inflammatory cytokines implicated in CVD. In silico virtual screening identified genistein, a soybean isoflavone, as a putative CB1 antagonist. Human-induced pluripotent stem cell-derived endothelial cells were used to model Δ 9 -THC-induced inflammation and oxidative stress via NF-κB signaling. Knockdown of the CB1 receptor with siRNA, CRISPR interference, and genistein attenuated the effects of Δ 9 -THC. In mice, genistein blocked Δ 9 -THC-induced endothelial dysfunction in wire myograph, reduced atherosclerotic plaque, and had minimal penetration of the central nervous system. Genistein is a CB1 antagonist that attenuates Δ 9 -THC-induced atherosclerosis., Competing Interests: Declaration of interests J.C.W. is a co-founder and SAB of Greenstone Biosciences, but the work was done independently. B.K.K. is a co-founder and SAB of ConfometRx, but the work was done independently., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and thus remarkably facilitate drug discovery and development. Conventional similarity- or docking-based computational methods for predicting CPIs rarely exploit latent features from currently available large-scale unlabeled compound and protein data and often limit their usage to relatively small-scale datasets. In the present study, we propose DeepCPI, a novel general and scalable computational framework that combines effective feature embedding (a technique of representation learning) with powerful deep learning methods to accurately predict CPIs at a large scale. DeepCPI automatically learns the implicit yet expressive low-dimensional features of compounds and proteins from a massive amount of unlabeled data. Evaluations of the measured CPIs in large-scale databases, such as ChEMBL and BindingDB, as well as of the known drug-target interactions from DrugBank, demonstrated the superior predictive performance of DeepCPI. Furthermore, several interactions among small-molecule compounds and three G protein-coupled receptor targets (glucagon-like peptide-1 receptor, glucagon receptor, and vasoactive intestinal peptide receptor) predicted using DeepCPI were experimentally validated. The present study suggests that DeepCPI is a useful and powerful tool for drug discovery and repositioning. The source code of DeepCPI can be downloaded from https://github.com/FangpingWan/DeepCPI., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)