Ligand-based Virtual Screening Research Articles

Exploring novel natural compound-based therapies for Duchenne muscular dystrophy management: insights from network pharmacology, QSAR modeling, molecular dynamics, and free energy calculations

Muscular dystrophies encompass a heterogeneous group of rare neuromuscular diseases characterized by progressive muscle degeneration and weakness. Among these, Duchenne muscular dystrophy (DMD) stands out as one of the most severe forms. The present study employs an integrative approach combining network pharmacology, quantitative structure-activity relationship (QSAR) modeling, molecular dynamics (MD) simulations, and free energy calculations to identify potential therapeutic targets and natural compounds for DMD. Upon analyzing the GSE38417 dataset, it was found that individuals with DMD exhibited 290 upregulated differentially expressed genes (DEGs) compared to healthy controls. By utilizing gene ontology (GO) and protein-protein interaction (PPI) network analysis, this study provides insights into the functional roles of the identified DEGs, identifying ten hub genes that play a critical role in the pathology of DMD. These key genes include DMD, TTN, PLEC, DTNA, PKP2, SLC24A, FBXO32, SNTA1, SMAD3, and NOS1. Furthermore, through the use of ligand-based pharmacophore modeling and virtual screening, three natural compounds were identified as potential inhibitors. Among these, compounds 3874518 and 12314417 have demonstrated significant promise as an inhibitor of the SMAD3 protein, a crucial factor in the fibrotic and inflammatory mechanisms associated with DMD. The therapeutic potential of the compounds was further supported by molecular dynamics simulation and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) analysis. These findings suggest that the compounds are viable candidates for experimental validation against DMD.

Groenlandicine enhances cisplatin sensitivity in cisplatin-resistant osteosarcoma cells through the BAX/Bcl-2/Caspase-9/Caspase-3 pathway

Groenlandicine is a protoberberine alkaloid isolated from Coptidis Rhizoma, a widely used traditional Chinese medicine known for its various biological activities. This study aims to validate groenlandicine’s effect on both cisplatin-sensitive and cisplatin-resistant osteosarcoma (OS) cells, along with exploring its potential molecular mechanism.The ligand-based virtual screening (LBVS) method and molecular docking were employed to screen drugs. CCK-8 and FCM were used to measure the effect of groenlandicine on the OS cells transfected by lentivirus with over-expression or low-expression of TOP1. Cell scratch assay, CCK-8, FCM, and the EdU assay were utilized to evaluate the effect of groenlandicine on cisplatin-resistant cells. WB, immunofluorescence, and PCR were conducted to measure the levels of TOP1, Bcl-2, BAX, Caspase-9, and Caspase-3. Additionally, a subcutaneous tumor model was established in nude mice to verify the efficacy of groenlandicine.Groenlandicine reduced the migration and proliferation while promoting apoptosis in OS cells, effectively damaging them. Meanwhile, groenlandicine exhibited weak cytotoxicity in 293T cells. Combination with cisplatin enhanced tumor-killing activity, markedly activating BAX, cleaved-Caspase-3, and cleaved-Caspase-9, while inhibiting the Bcl2 pathway in cisplatin-resistant OS cells. Moreover, the level of TOP1, elevated in cisplatin-resistant OS cells, was down-regulated by groenlandicine both in vitro and in vivo. Animal experiments confirmed that groenlandicine combined with cisplatin suppressed OS growth with lower nephrotoxicity.Groenlandicine induces apoptosis and enhances the sensitivity of drug-resistant OS cells to cisplatin via the BAX/Bcl-2/Caspase-9/Caspase-3 pathway. Groenlandicine inhibits OS cells growth by down-regulating TOP1 level.Therefore, groenlandicine holds promise as a potential agent for reversing cisplatin resistance in OS treatment.

In silico identification and virtual screening to discover potent therapeutic Phytochemicals against CMT2A

Charcot-Marie-Tooth (CMT2A) neuropathies are a set of monogenic diseases that affect the peripheral nervous system. The pathogenesis of CMT2A, a disease caused by genetic mutations, is linked to impaired mitochondrial dynamics and axonal biology. Therapeutic options are still limited, with only a few drugs and other authorized or underdeveloped approaches. A ligand-based virtual screening methodology was used to identify the potential MFN promoters. The natural compound subset of the ZINC database (n = 559600) was obtained and filtered using a ligand-based virtual screening technique. The top 200 compounds were identified to have more than four features that matched the target compound. Pyrx software was used to analyze the molecular docking. Based on the number and type of important binding interactions and docking results, we selected top-20 compounds with the best binding affinities for the targeted protein. 3D-QSAR analyses were performed on potential ligands identified through molecular docking analyses to predict biological activity. The pEC50 and docking scores were used to identify the potential drugs. The ADMET analysis was used to assess the kinetic characteristics of the top two drugs. According to molecular dynamics simulation, the top compound ZINC000005313168 showed high conformational stability. Based on docking results and MD findings, the best in silico hits among the compounds investigated was ZINC000005313168. These findings suggest that the compound ZINC000005313168 could be used to treat CMT2A disease. Further in vivo and in vitro studies are required to consider the present analyses.

Identification of Novel Compounds Targeting the Liver X Receptor (LXR): In-silico Studies, Screening, Molecular Docking, and Chemico-pharmacokinetic Analysis

Studies have demonstrated the association between LXR activity dysregulation with many diseases, including atherosclerosis, diabetes and cancer. In recent years, several LXR agonists have surfaced, but none have been approved for human use due to adverse effects or unforeseen reasons. In this study, we first analysed the mRNA and protein expression of LXRs across tissues, network and pathway analysis, and reinterpreted their physiological function and disease association by utilizing multiple biological data repositories, including RNA-seq human protein atlas, DisGeNET, etc. Then, we performed ligand-based virtual screening, chemico-pharmacokinetic analysis, docking and simulation to identify potential new compounds. Our findings of mRNA, protein expression, network and disease enrichment analysis reveal diverse physiological functions of LXRs addressing the possibility of pharmacological manipulation with small molecules would provide therapeutic strategies for disease management. Evaluation of the docking and chemico-pharmacokinetic properties directed to the selection of LXR-623 and AZ876 as promising candidates for LXR-α and LXR-β for further in-silico investigation. Comprehensive screening for new ligands targeting LXRs based on the chemical structures of LXR-623 and AZ876, identified ZINC000005399501 and ZINC000021912941 with the highest binding affinity (−9.8 and −10.7 kcal/mol) for LXRα and LXRβ, respectively. Our results also supported in simulation study, along with favorable chemico-pharmacokinetic features.

Structural insight into the lead identification of a dual inhibitor of PDE1B and PDE10A: Integrating pharmacophore-based virtual screening, molecular docking, and structure-activity-relationship approaches

Schizophrenia is a chronic neuropsychiatric disorder affecting more than 1% of the world's population. Current antipsychotic treatments show inadequacy in mitigating the negative and cognitive symptoms of schizophrenia. In addition, these medications cause undesirable extrapyramidal side effects. According to the studies, inhibition of phosphodiesterase (PDE) 1B and PDE10A simultaneously can alleviate positive, negative, and cognitive symptoms of schizophrenia. Thus, this study aims to identify new dual inhibitors of PDE1B and PDE10A using ligand-based pharmacophore modelling, virtual screening, and molecular docking studies. Accordingly, the generated pharmacophore models of PDE1B and PDE10A comprised hydrogen bond acceptor, aromatic ring, and hydrophobic features. These features were essential for retrieving the active hits from the Universal Natural Product Database in the virtual screening. Additional filters were subsequently employed to identify potential hits that could be developed into central nervous system-active compounds. Hits meeting all the screening criteria were subjected to docking studies with PDE1B and PDE10A. Among these hits, UNPD167314 exhibited significant binding affinities for the target receptors. It occupied the P-clamp and displayed hydrophobic, aromatic, and hydrogen bond interactions with the active site residues of both receptors, thus selected as a lead compound for the design of potent and selective dual inhibitors. The structural modifications of UNPD167314 resulted in the design of 35 novel inhibitors. Out of 35, four compounds exhibited high and comparable binding affinities for both PDE1B and PDE10A, making them promising candidates for further evaluation and optimisation.

Use of the Novel Site-Directed Enzyme Enhancement Therapy (SEE-Tx) Drug Discovery Platform to Identify Pharmacological Chaperones for Glutaric Acidemia Type 1.

Allosteric regulators acting as pharmacological chaperones hold promise for innovative therapeutics since they target noncatalytic sites and stabilize the folded protein without competing with the natural substrate, resulting in a net gain of function. Exogenous allosteric regulators are typically more selective than active site inhibitors and can be more potent than competitive inhibitors when the natural substrate levels are high. To identify novel structure-targeted allosteric regulators (STARs) that bind to and stabilize the mitochondrial enzyme glutaryl-CoA dehydrogenase (GCDH), the computational site-directed enzyme enhancement therapy (SEE-Tx) technology was applied. SEE-Tx is an innovative drug discovery platform with the potential to identify drugs for treating protein misfolding disorders, such as glutaric acidemia type 1 (GA1) disease. Putative allosteric regulators were discovered using structure- and ligand-based virtual screening methods and validated using orthogonal biophysical and biochemical assays. The computational approach presented here could be used to discover allosteric regulators of other protein misfolding disorders.

Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint.

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), and their combinations, are frequently conducted in modern drug discovery campaigns. As a form of combination, an amalgamation of methods from ligand- and structure-based information, termed hybrid VS approaches, has been extensively investigated such as using interaction fingerprints (IFPs) in combination with machine learning (ML) models. This approach has the potential to prioritize active compounds in terms of protein-ligand binding and ligand structural characteristics, which is assumed to be difficult using either one of the approaches. Herein, we present an IFP, named the fragmented interaction fingerprint (FIFI), for hybrid VS approaches. FIFI is constructed from the extended connectivity fingerprint atom environments of a ligand proximal to the protein residues in the binding site. Each unique ligand substructure within each amino acid residue is encoded as a bit in FIFI while retaining sequence order. From the retrospective evaluation of activity prediction using a limited number and variety of active compounds for six biological targets, FIFI consistently showed higher prediction accuracy than that using previously proposed IFPs. For the same data sets, the screening performance of LBVS, SBVS sequential VS, parallel VS, and other hybrid VS approaches was investigated. Compared to these approaches, FIFI in combination with ML showed overall stable and high prediction accuracy, except for one target: the kappa opioid receptor, where the extended connectivity fingerprint combined with ML models showed better performance than other approaches by wide margins.

Discovering potential asthma therapeutics targeting hematopoietic prostaglandin D2 synthase: An integrated computational approach

Allergic asthma, a chronic inflammatory illness that affects millions worldwide, has serious economic and health consequences. Despite advances in therapy, contemporary treatments have poor efficacy and negative effects. This study investigates hematopoietic prostaglandin D2 synthase (HPGDS) as a potential target for novel asthma therapies. Targeting HPGDS may provide innovative treatment methods. A library of phytochemicals was used to find putative HPGDS inhibitors by structure-based and ligand-based virtual screening. Among the 2295 compounds screened, four compounds (ZINC208828240, ZINC95627530, ZINC14727536, and ZINC14711790) demonstrated strong binding affinities of −10.4, −10.3, −9.2, −9.1 kcal/mol respectively with key residues, suggesting their potential as a highly effective HPGDS inhibitor. Molecular dynamics (MD) simulations and Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) computations were further performed to evaluate the stability and binding affinity of the complexes. MD simulations and MMPBSA confirmed that compound ZINC14711790 showed high stability and binding affinity (binding energy −31.52 kcal/mol) than other compounds, including HQL-79, suggesting that this compound might be used as promising inhibitors to treat asthma. RMSD and RMSF analysis also revealed that ZINC14711790 exhibited strong dynamic stability. The findings of this study show the efficacy of ZINC14711790 as HPGDS inhibitors with high binding affinity, dynamic stability, and appropriate ADMET profile.

Discovery of BI-9787, a potent zwitterionic ketohexokinase inhibitor with oral bioavailability

Fructose metabolism by ketohexokinase (KHK) is implicated in a variety of metabolic disorders. KHK inhibition is a potential therapeutic strategy for the treatment of diseases including diabetes, non-alcoholic fatty liver disease, and non-alcoholic steatohepatitis. The first small-molecule KHK-inhibitors have entered clinical trials, but it remains unclear if systemic inhibition of KHK by small-molecules will eventually benefit patients. Here we report the discovery of BI-9787, a potent, zwitterionic KHK inhibitor characterized by high permeability and favorable oral rat pharmacokinetics. BI-9787 was identified by optimizing chemical starting points generated via a ligand-based virtual screening of Boehringer’s virtual library of synthetically accessible compounds (BICLAIM). It serves as a high-quality in vitro and in vivo tool compound for investigating the role of fructose metabolism in disease.

Ligand-Based Virtual Screening as a Path to New Chemotypes for Candidate PET Radioligands for Imaging Tauopathies.

Ligand-based virtual screening (LBVS) has rarely been tested as a method for discovering new structural scaffolds for PET radioligand development. This study used LBVS to discover potential chemotype leads for developing radioligands for PET imaging of tauopathies. ZINC12, a free database of over 12 million commercially available compounds, was searched to discover novel scaffolds based on similarities to four query compounds. Thirteen high-ranking hits were purchased and assayed for their ability to compete against three tritiated radioligands at their distinct binding sites in Alzheimer's disease brain tissue. Three hits were 2-substituted 6-methoxy naphthalenes. Synthetic elaboration of this new chemotype yielded three new ligands (25, 26, and 28) with high affinity for the [3H]6 (flortaucipur) neurofibrillary tangle binding site. Compound 28 showed remarkably high affinity (Ki, 7 nM) and other desirable properties for a candidate PET radioligand, including low topological polar surface area, moderate computed log D, and amenability for labeling with carbon-11. LBVS appears to be uniquely valuable for discovering new chemotypes for candidate PET radioligands.

Targeting COVID-19 and varicocele by blocking inflammasome: Ligand-based virtual screening

COVID-19 is a new generation of outbreaks that invade not only local emerging region, continental but also the whole globe. Varicocele on the other hand, is a testicular vascular disease that underlies 40 % of male infertility cases. Fortunately, the two diseases can be blocked through targeting one common target, NLRP3 inflammasome. Upon searching for similar drugs that gained FDA-approval in ChEMBL library along with examining their potential blockade of the receptor through docking using CB-DOCK-2, three potential approved drugs can be repurposed, ChEMBL 4297185, ChEMBL 1201749, ChEMBL 1200545 which had binding energy of −9.8 and −9.7 kcal/mol (stronger than the reference inhibitor, −9.3 kcal/mol). Also, ADME profile of the top 3 drugs showed better attributes. Also, the simulated proteins exhibited stable pattern with strong free binding energies. Among the potential inhibitor drugs ChEMBL 4297185 was found to remain inside the binding site of the protein during the 200 ns simulation time. Hence, it is anticipated to have the highest binding and thus inhibition potential against the protein. The suggested drugs, especially ChEMBL 4297185, are potentially repurposable toward treating COVID-19 and varicocele which deserve further experimental validation.

N,N-Disubstituted 4-sulfamoylbenzoic Cid Derivatives as Inhibitors of Cytosolic Phospholipase A2α: Synthesis, Aqueous Solubility, and Activity in a Vesicle and a Whole Blood Assay.

Cytosolic phospholipase A2α (cPLA2α) is the key enzyme that initiates the arachidonic acid cascade through which proinflammatory lipid mediators can be formed. Therefore, cPLA2α is considered an interesting target for the development of anti-inflammatory drugs. Although several effective inhibitors of the enzyme have been developed, none of them has yet reached clinical application. Recently, we have prepared new 4-sulfamoylbenzoic acid derivatives based on a cPLA2α inhibitor found in a ligand-based virtual screening. The most effective of these compounds were now subjected to further variations in which the substitution pattern on the sulfamoyl nitrogen atom was changed. The new compounds were tested in vitro in a vesicle assay for cPLA2α inhibition as well as for their water solubility, metabolic stability, and selectivity towards related enzymes. In addition, they were evaluated ex vivo in a whole blood assay in which metabolites of the arachidonic acid cascade formed after activation of cPLA2α were quantified using a combined online dilution/online solid phase extraction HPLC-MS method. Inhibitors with submicromolar inhibitory in vitro potency were found with favourable water solubility and selectivity. However, their efficacy did not match that of the highly effective, known, structurally related cPLA2a inhibitor giripladib, which was also tested as a reference. One advantage of some of the new compounds compared to giripladib was their significantly improved water solubility. When analyzing the substances in the ex vivo whole blood assay, it was found that the obtained inhibition data correlated better with the in vivo results when the phorbol ester 12-Otetradecanoylphorbol-13-acetate was used for activation of the enzyme in the blood cells instead of the calcium ionophore A23187. New compounds with good activity towards cPLA2α and reasonable physicochemical properties were identified. Overall, the results obtained could be helpful in the development of clinically applicable inhibitors of this enzyme.

Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors.

The fusion of traditional chemical descriptors with Graph Neural Networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from this integrative strategy vary significantly among different GNNs. Specifically, while GCN and SchNet demonstrate pronounced improvements by incorporating descriptors, SphereNet exhibits only marginal enhancement. Intriguingly, despite SphereNet's modest gain, all three models-GCN, SchNet, and SphereNet-achieve comparable performance levels when leveraging this combination strategy. This observation underscores a pivotal insight: sophisticated GNN architectures may be substituted with simpler counterparts without sacrificing efficacy, provided that they are augmented with descriptors. Furthermore, our analysis reveals a set of expert-crafted descriptors' robustness in scaffold-split scenarios, frequently outperforming the combined GNN-descriptor models. Given the critical importance of scaffold splitting in accurately mimicking real-world drug discovery contexts, this finding accentuates an imperative for GNN researchers to innovate models that can adeptly navigate and predict within such frameworks. Our work not only validates the potential of integrating descriptors with GNNs in advancing ligand-based virtual screening but also illuminates pathways for future enhancements in model development and application. Our implementation can be found at https://github.com/meilerlab/gnn-descriptor.

Ligand-Based Virtual Screening for Discovery of Indole Derivatives as Potent DNA Gyrase ATPase Inhibitors Active against Mycobacterium tuberculosis and Hit Validation by Biological Assays.

Mycobacterium tuberculosis is the single most important global infectious disease killer and a World Health Organization critical priority pathogen for development of new antimicrobials. M. tuberculosis DNA gyrase is a validated target for anti-TB agents, but those in current use target DNA breakage-reunion, rather than the ATPase activity of the GyrB subunit. Here, virtual screening, subsequently validated by whole-cell and enzyme inhibition assays, was applied to identify candidate compounds that inhibit M. tuberculosis GyrB ATPase activity from the Specs compound library. This approach yielded six compounds: four carbazole derivatives (1, 2, 3, and 8), the benzoindole derivative 11, and the indole derivative 14. Carbazole derivatives can be considered a new scaffold for M. tuberculosis DNA gyrase ATPase inhibitors. IC50 values of compounds 8, 11, and 14 (0.26, 0.56, and 0.08 μM, respectively) for inhibition of M. tuberculosis DNA gyrase ATPase activity are 5-fold, 2-fold, and 16-fold better than the known DNA gyrase ATPase inhibitor novobiocin. MIC values of these compounds against growth of M. tuberculosis H37Ra are 25.0, 3.1, and 6.2 μg/mL, respectively, superior to novobiocin (MIC > 100.0 μg/mL). Molecular dynamics simulations of models of docked GyrB:inhibitor complexes suggest that hydrogen bond interactions with GyrB Asp79 are crucial for high-affinity binding of compounds 8, 11, and 14 to M. tuberculosis GyrB for inhibition of ATPase activity. These data demonstrate that virtual screening can identify known and new scaffolds that inhibit both M. tuberculosis DNA gyrase ATPase activity in vitro and growth of M. tuberculosis bacteria.

In Silico and In Vitro Studies of Terpenes from the Fabaceae Family Using the Phenotypic Screening Model against the SARS-CoV-2 Virus.

In 2019, the emergence of the seventh known coronavirus to cause severe illness in humans triggered a global effort towards the development of new drugs and vaccines for the SARS-CoV-2 virus. These efforts are still ongoing in 2024, including the present work where we conducted a ligand-based virtual screening of terpenes with potential anti-SARS-CoV-2 activity. We constructed a Quantitative Structure-Activity Relationship (QSAR) model from compounds with known activity against SARS-CoV-2 with a model accuracy of 0.71. We utilized this model to predict the activity of a series of 217 terpenes isolated from the Fabaceae family. Four compounds, predominantly triterpenoids from the lupane series, were subjected to an in vitro phenotypic screening in Vero CCL-81 cells to assess their inhibitory activity against SARS-CoV-2. The compounds which showed high rates of SARS-CoV-2 inhibition along with substantial cell viability underwent molecular docking at the SARS-CoV-2 main protease, papain-like protease, spike protein and RNA-dependent RNA polymerase. Overall, virtual screening through our QSAR model successfully identified compounds with the highest probability of activity, as validated using the in vitro study. This confirms the potential of the identified triterpenoids as promising candidates for anti-SARS-CoV-2 therapeutics.

Deriving general structure–activity/selectivity relationship patterns for different subfamilies of cyclin-dependent kinase inhibitors using machine learning methods

Cyclin-dependent kinases (CDKs) play essential roles in regulating the cell cycle and are among the most critical targets for cancer therapy and drug discovery. The primary objective of this research is to derive general structure–activity relationship (SAR) patterns for modeling the selectivity and activity levels of CDK inhibitors using machine learning methods. To accomplish this, 8592 small molecules with different binding affinities to CDK1, CDK2, CDK4, CDK5, and CDK9 were collected from Binding DB, and a diverse set of descriptors was calculated for each molecule. The supervised Kohonen networks (SKN) and counter propagation artificial neural networks (CPANN) models were trained to predict the activity levels and therapeutic targets of the molecules. The validity of models was confirmed through tenfold cross-validation and external test sets. Using selected sets of molecular descriptors (e.g. hydrophilicity and total polar surface area) we derived activity and selectivity maps to elucidate local regions in chemical space for active and selective CDK inhibitors. The SKN models exhibited prediction accuracies ranging from 0.75 to 0.94 for the external test sets. The developed multivariate classifiers were used for ligand-based virtual screening of 2 million random molecules of the PubChem database, yielding areas under the receiver operating characteristic curves ranging from 0.72 to 1.00 for the SKN model. Considering the persistent challenge of achieving CDK selectivity, this research significantly contributes to addressing the issue and underscores the paramount importance of developing drugs with minimized side effects.

Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery

Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen’s application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine.

Structure and Ligand Based Virtual Screening and MPO Topological Analysis of Triazolo Thiadiazepine-fused Coumarin Derivatives as Anti-Parkinson Drug Candidates.

Parkinson's disease (PD) is a debilitating condition that can cause locomotor problems in affected patients, such as tremors and body rigidity. PD therapy often includes the use of monoamine oxidase B (MAOB) inhibitors, particularly phenylhalogen compounds and coumarin-based semi-synthetic compounds. The objective of this study was to analyze the structural, pharmacokinetic, and pharmacodynamic profile of a series of Triazolo Thiadiazepine-fused Coumarin Derivatives (TDCDs) against MAOB, in comparison with the inhibitor safinamide. To achieve this goal, we utilized structure-based virtual screening techniques, including target prediction and absorption, distribution, metabolism, and excretion (ADME) prediction based on multi-parameter optimization (MPO) topological analysis, as well as ligand-based virtual screening techniques, such as docking and molecular dynamics. The findings indicate that the TDCDs exhibit structural similarity to other bioactive compounds containing coumarin and MAOB-binding azoles, which are present in the ChEMBL database. The topological analyses suggest that TDCD3 has the best ADME profile, particularly due to the alignment between low lipophilicity and high polarity. The coumarin and triazole portions make a strong contribution to this profile, resulting in a permeability with Papp estimated at 2.15 × 10-5cm/s, indicating high cell viability. The substance is predicted to be metabolically stable. It is important to note that this is an objective evaluation based on the available data. Molecular docking simulations showed that the ligand has an affinity energy of -8.075kcal/mol with MAOB and interacts with biological substrate residues such as Pro102 and Phe103. The results suggest that the compound has a safe profile in relation to the MAOB model, making it a promising active ingredient for the treatment of PD.

OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods. The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.

Identifying novel small molecule integrin agonists to mimic the favourable effects of the RGD domain of IGFBP-1 to treat cardiometabolic disease

Abstract Funding Acknowledgements Type of funding sources: Other. Main funding source(s): British Heart Foundation (BHF) 4-Year PhD Programme Background Insulin-like growth factor binding protein (IGFBP)-1 has previously been shown to be associated with reduced type 2 diabetes (T2D) progression and preferable cardiovascular profiles in patients. Our laboratory previously demonstrated that IGFBP-1 is an insulin sensitiser in mouse models, in addition to having favourable vascular effects, independently of IGFs. IGFBP-1 has an Arg-Gly-Asp (RGD) domain which is thought to facilitate the direct interaction with the heterodimeric cell-surface receptor, integrin α5β1, inducing downstream cell signalling. We then showed the positive effects of IGFBP-1 could be mimicked with an RGD-containing linear peptide, GRGDTP, however this peptide is not a suitable therapeutic as it is rapidly metabolised in vivo. To our knowledge, RGD-based therapeutics have not been explored for cardiometabolic disease. Purpose There are currently few therapeutics which have been specifically designed to address both insulin resistance and the high risk of macrovascular disease in T2D. Therefore, exploring RGD-based integrin agonists will be essential in developing new insulin sensitising therapeutics which prevent cardiovascular complications. Methods The ROCS high speed ligand-based virtual screening tool was used to screen commercially available small molecule libraries mimicking the chemical structure of the RGD sequence of IGFBP-1. Additionally, we performed in silico modelling to predict the binding interactions of these molecules with the integrin α5β1 headpiece to select candidates. We then used a LIVE/DEAD cell viability assay to initially profile any cytotoxic effects of our selected compounds in integrin-expressing human umbilical vein endothelial cells (HUVECs) and C2C12 skeletal muscle myotubes. We further assessed the effects of compounds on relevant HUVEC functions using a high-throughput wound closure assay. Results The structure of the RGD domain of IGFBP-1 can be mimicked with small molecules containing amino acid moieties. These molecules are predicted to bind with micromolar affinity to the integrin, promoting beneficial interactions for integrin agonism. We selected 17 structurally diverse small molecules with the best predicted binding interactions. Initial in vitro profiling demonstrates that the molecules are not cytotoxic. In preliminary analyses, three of the selected compounds show a trend towards inhibition of HUVEC migration and proliferation, while six show a trend towards increased migration and proliferation. Conclusion The RGD domain of IGFBP-1 can be mimicked with novel small molecules, of which we have selected 17 ligands. Our compounds do not affect cell viability in relevant cell types, so can be further profiled. Our initial findings indicate that the compounds have different functional effects on HUVECs which allows us to prioritise hits having favourable effects in the context of T2D, such as enhancing angiogenic properties of endothelial cells.