Virtual Screening Process Research Articles

Overcoming beta-lactam resistance in Pseudomonas aeruginosa by targeting metallo-beta-lactamase VIM-1: a one-microsecond molecular dynamics simulation study

Pseudomonas aeruginosa (P. aeruginosa) is a Gram-negative opportunistic pathogen with a high resistance to beta-lactam antibiotics, mainly due to the production of metallo-beta-lactamase VIM-1 (MBL-VIM-1) enzyme. This study aimed to identify new inhibitors targeting MBL-VIM-1 to restore the efficacy of beta-lactam antibiotics. Extensive screening of natural compounds from the COCONUT database was performed to identify the structural analogs of the existing inhibitor of the MBL-VIM-1 protein. The virtual screening process selected four top-performing compounds (CNP0390322, CNP03905695, CNP0079056, and CNP0338283) that exhibited promising docking scores. These compounds were then subjected to re-docking and one-microsecond molecular dynamics (MD) simulations to assess their binding stability and interactions within the MBL-VIM-1 active site. Finally, post-MD simulation calculations were employed to estimate the interaction strengths and compare the efficacy of these compounds against the reference inhibitor. The findings highlighted that these four potent MBL-VIM-1 inhibitors show superior binding affinity and stability, suggesting their potential to combat antibiotic resistance in P. aeruginosa. The identified compounds offer a promising avenue for developing novel therapeutics to restore the efficacy of beta-lactam antibiotics against resistant bacterial strains. Therefore, further in vitro and in vivo studies are warranted to validate their potential.

DDDR-15. UTILIZING DEEP DOCKING AND ARTIFICIAL INTELLIGENCE FOR THE DISCOVERY OF NOVEL PARP1-SELECTIVE INHIBITORS FOR USE AGAINST BRAIN TUMORS

Abstract Brain tumors, including primary brain tumors and central nervous system (CNS) metastases, remain among the most challenging malignancies to treat, with therapeutic options often limited by the inability of drugs to penetrate the blood-brain barrier. Poly(ADP-ribose) polymerase (PARP) is a key enzyme in DNA repair, and inhibition of PARP1 specifically drives synthetic lethality in BRCA-mutated disease. While they’ve achieved commercial success, first generation PARP inhibitors are limited in their utility as they cannot readily pass through the blood-brain barrier and have adverse side effects, likely driven by the collateral inhibition of PARP2. Development of a PARP1-selective, CNS penetrant inhibitor could reduce toxicity, while providing a new therapeutic option for brain tumors. Traditional drug discovery methods are time-consuming and costly, necessitating innovative approaches. Here, we describe the application of Deep Docking, an advanced artificial intelligence (AI) approach, to discover a novel, PARP1-selective inhibitor for use against brain tumors. Deep Docking utilizes deep learning to accelerate the prediction of the binding affinity of a large number of compounds to target proteins, streamlining the virtual screening process and allowing for rapid docking of billions of compounds against the PARP1 protein. Additionally, state-of-the-art generative algorithms and deep learning techniques for predicting drug-like properties such as metabolism, permeability, and safety profiles, can be combined to rapidly perform hit-to-lead optimization. We will present preliminary Deep Docking screening results from billions of compounds, identifying several with predicted high binding affinities for PARP-1. Validating in vitro and in vivo data, including PARP1 selectivity, metabolic stability, pharmacokinetic profile, CNS penetration and safety profile, will be described. Application of the Deep Docking AI platform is being used to significantly accelerate the discovery of a selective PARP-1 inhibitor for brain tumors. This approach will not only improve the efficiency of drug discovery but also enhance the specificity and efficacy of potential therapeutics.

Discovery of novel CDK4/6 inhibitors from fungal secondary metabolites

The development of targeted therapies for breast cancer, particularly those focusing on cyclin-dependent kinases 4/6 (CDK4/6), has significantly improved patient outcomes. However, the currently approved CDK4/6 inhibitors are associated with various side effects, underscoring the need for novel compounds with enhanced efficacy and safety profiles. This study aimed to identify potential CDK4/6 inhibitors from MeFSAT, a database of fungal secondary metabolites using an in-silico screening approach. The virtual screening process incorporated drug-likeness filters, ADME and toxicity predictions, consensus molecular docking, and 200 ns molecular dynamics simulations. Out of 411 initial compounds, two molecules demonstrated favorable binding interactions and stability with the CDK4/6 protein complex. The MTT assay showed that MSID000025 had dose-dependent cytotoxicity against MCF7 breast cancer cells. This suggests that MSID000025 could be a good candidate CDK4/6 inhibitor for treating breast cancer. Our study highlights the potential of fungal secondary metabolites as a source of novel compounds for drug discovery. It provides a framework for identifying CDK4/6 inhibitors with improved therapeutic properties.

IDENTIFICATION OF ANTIBACTERIAL AGENTS AGAINST KLEBSIELLA PNEUMONIAE TARGETING THE CTX-M-15 PROTEIN USING INTEGRATED STRUCTURE MODEL-BASED VIRTUAL SCREENING METHODS

The spread of carbapenem-resistant Gram-negative bacteria, particularly those having the bla CTX-M-15 gene, has stimulated a global challenge for β-lactam antimicrobial effectiveness. This study aims to identify commercially available compounds that demonstrate strong antibacterial properties with high efficacy and low toxicity to combat the increasing problem of antibiotic resistance. Virtual screening and molecular docking techniques were employed to generate a pharmacophore model based on the molecular structure. Eleven potential compounds were selected based on their IC50 values. Subsequently, a molecular docking approach was employed to select the best three small molecules for further comprehensive investigation. Moreover, absorption, distribution, metabolism, and excretion (ADME) and toxicity analysis ensured that it can be used as a drug without the effect of health. Finally, molecular dynamic (MD) simulations and generalized Born model and solvent accessibility (MMGB/SA) were carried out to evaluate the stability of these compounds against the receptor. Three selected compounds, specifically ZINCCID (ZINC94211493, ZINC20528448, and ZINC04331046), have exhibited strong binding affinities of -8.4, -8.1, and -7.7 kcal/mol, respectively. This has been predicted as a potential competitive inhibitor targeting CTX-M-15. However, further assessment through experimental lab studies is required to evaluate the efficacy of these compounds. Initially, a structure-based model was constructed, and subsequently, molecular docking, MD simulation, ADMET analysis, and MMGB/SA were performed. Through the A-to-Z virtual screening process, the top three natural compounds were identified as potential lead molecules in the development of novel drugs targeting CTX-M-15 for combating Klebsiella infections. Keywords: Klebsiella; Structure-based; Molecular docking; Pharmacophore modeling; CTX-M-15 protein; ADMET; MMGB/SA, MD simulation.

Ligand-based pharmacophore modeling, virtual screening, and molecular dynamics simulations of Pfhsp90 fingerprints in Plasmodium malaria treatment

The World Health Organization (WHO) documents malaria as one of the leading causes of high morbidity and mortality worldwide. The disease affects millions and kills thousands of people annually. Efforts to reduce the global burden of malaria have prompted the WHO to recommend prevention strategies such as using antimalarial drugs. However, these strategies have been ineffective because of antimalarial drug resistance. The only efficacious malaria treatment is Artemisinin-based Combination Therapy (ACT). However, the extended ACT clearance times, linked to the emergence of artemisinin monotherapy resistance recorded most recently in Africa and the Great Mekong region, pose a danger to its efficacy. Therefore, better efficacious antimalarial drugs are required. Since P. falciparum heat shock protein 90 (PfHsp90) is a well-characterized malaria drug target, this study uses it to discover more efficacious antimalarial drugs. An in silico approach was used to discover PfHsp90 inhibitors with pharmacological properties against Plasmodium malaria using a molecular dynamics simulation (MDS) and hierarchical virtual screening. Geldanamycin (GDM), a well-known anti-PfHsp90 compound, was used to identify PfHsp90 inhibitors with pharmacological properties against Plasmodium malaria by screening it against the ZINC20 database via the ZINCPHARMER web server. This virtual screening process resulted in 17 hits. These ZINCPHARMER hits were subjected to drug-likeness and pharmacokinetics properties analysis in the SwissADME web server, and nine of them satisfied the requirements. The nine ZINC20 compounds were docked with PfHsp90 using the PyRx software version 0.8 to understand their interactions. From the molecular docking results, ZINC09060002 (−8.2 kcal/mol), ZINC72133064 (−7.8 kcal/mol), ZINC72163401 (−7.7 kcal/mol), ZINC72358537 (−8.1 kcal/mol), and ZINC72358557 (−7.6 kcal/mol) had better binding affinities to PfHsp90 than GDM (−7.5 kcal/mol). The stability of these molecularly docked protein–inhibitor complexes was assessed through MDS using GROMACS 2022. ZINC72163401, ZINC72358537, and ZINC72358557 formed stable complexes with PfHsp90. The lead compounds were subjected to in vitro validation for their inhibitory capability. They showed promising inhibition of parasite growth with IC50 values ranging between 200 and 400 ng/mL. In this regard, the three PfHsp90 inhibitors can be further developed as potential antimalarial drugs. However, further structural optimization studies and clinical (in vivo) tests are necessary to ascertain the antimalarial activity of these compounds in humans.

Understanding the stability and dynamics of influenza a H5N1 polymerase PB2 CAP-Binding domain in complex with natural compounds for antiviral drug discovery

Influenza A virus, particularly the H5N1 strain, poses a significant threat to public health due to its ability to cause severe respiratory illness and its high mortality rate. Traditional antiviral drugs targeting influenza A virus have faced challenges such as drug resistance and limited efficacy. Therefore, new antiviral compounds are needed to be discovered and developed. This study concentrated on examining the stability and behavior of the H5N1 polymerase PB2 CAP-binding domain when interacting with natural compounds, aiming to identify potential candidates for antiviral drug discovery. Through the virtual screening process, four lead compounds, ZINC000096095464, ZINC000044404209, ZINC000001562130, and ZINC000059779788, were selected, and these compounds showed binding energies −9.6, −9.4, −9.3, and −9.2 kcal/mol, respectively. When complexed with PB2, the ligand showed acceptable binding stability due to significant bond formation. However, during the 200ns MD simulation analysis, three (ZINC000096095464, ZINC000044404209, and ZINC000059779788) showed significant stability, which was proven by the trajectory analysis. The Rg-RMSD-based FEL plot showed significant structural stability due to stable conformers. The free-binding energy calculation also validates the stability of these complexes. This study offers valuable insights into the stability and dynamics of the H5N1 polymerase PB2 CAP-binding domain in complexes with natural compounds. These findings highlight the potential of these natural compounds as antiviral agents against the H5N1 influenza virus. Furthermore, this research contributes to the broader field of influenza virus treatment by demonstrating the effectiveness of computational methods in predicting and evaluating the stability and dynamics of potential drug candidates.

Exploring stevioside binding affinity with various proteins and receptors actively involved in the signaling pathway and a future candidate for diabetic patients.

Introduction and Background: Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels and is one of the main global health concerns. Synthetic sugar substrate has many side effects such as leukemia, bladder cancer, hepatotoxicity, breast cancer, headache, and brain toxicity. The WHO and FDA has recently banned some of the synthetic sugar alternatives due to their carcinogenic effects. Objective and Methodology: Therefore, the main objective of the current study was to investigate the safety and binding affinity of Stevioside with Glucose Transpoter-4 (GLUT-4), Akt, Insulin Receptor (IR) and Insulin Receptor Substrate-1 (IRS-1) to confirmed that Stevioside is one the potent natural sweetener/drug for diabetes. This study delves into the molecular interaction between Stevioside and key diabetic proteins: GLUT-4, Akt, IR and IRS-1. A precise molecular docking approach was used to simulate the binding affinity of Stevioside to these proteins. The pharmacokinetic properties of the molecule should be taken into consideration as important variables throughout the virtual screening process. Results: The result of active site analysis of GLUT-4, Akt, IR and IRS-1 showed a zone of 2158.359 Ǻ2, 579.259 Ǻ2, 762.651 Ǻ2, and 152.167 Ǻ2 and a volume of 2765.094 Ǻ³, 355.567 Ǻ³, 686.806 Ǻ³, and 116.874 Ǻ³, respectively. Docking analysis of the Stevioside compound showed the highest docking energy with scores of -9.9 with GLUT-4, -6.7 with Akt, -8.0 with IR and -8.8 with IRS-1. Studies indicated that it remains undigested by stomach acids and enzymes and is not absorbed in the upper small intestine. Further, tests revealed no hepatotoxicity, AMES toxicity, or skin sensitivity, making it a promising candidate for safe consumption as drug metabolism. Conclusion and Recommendations: Instead of other sugar alternatives, Stevioside will help diabetic patients with a lower chance of infections, lowered blood pressure/blood sugar, and increased glucose uptake in diabetic muscles. Stevioside is a natural sweetener, and the current study recommends its usage in various dietary products for diabetic patients.

Marine fungal diversity unlocks potent antivirals against monkeypox through methyltransferase inhibition revealed by molecular dynamics and free energy landscape

The escalating threat posed by the Monkeypox virus (MPXV) to global health necessitates the urgent discovery of effective antiviral agents, as there are currently no specific drugs available for its treatment, and existing inhibitors are hindered by toxicity and poor pharmacokinetic profiles. This study aimed to identify potent MPXV inhibitors by screening a diverse library of small molecule compounds derived from marine fungi, focusing on the viral protein VP39, a key methyltransferase involved in viral replication. An extensive virtual screening process identified four promising compounds—CMNPD15724, CMNPD28811, CMNPD30883, and CMNPD18569—alongside a control molecule. Rigorous evaluations, including re-docking, molecular dynamics (MD) simulations, and hydrogen bond analysis, were conducted to assess their inhibitory potential against MPXV VP39. CMNPD15724 and CMNPD30883, in particular, demonstrated a superior binding affinity and stable interactions within the target protein's active site throughout the MD simulations, suggesting a capacity to overcome the limitations associated with sinefungin. The stability of these VP39-compound complexes, corroborated by MD simulations, provided crucial insights into the dynamic behavior of these interactions. Furthermore, Principal Component Analysis (PCA) based free energy landscape assessments offered a detailed understanding of the dynamic conformational changes and energetic profiles underlying these compounds' functional disruption of VP39. These findings establish CMNPD15724, CMNPD28811, CMNPD30883, and CMNPD18569 as promising MPXV inhibitors and highlight marine fungi as a valuable source of novel antiviral agents. These compounds represent potential candidates for further experimental validation, advancing the development of safer and more effective therapeutic options to combat this emerging viral infection.

Exploring Natural Compounds as Promising Matrix Metalloproteinase-2 inhibitors for Cancer Management: A Biocomputational Study

Background: Matrix metalloproteinase-2 (MMP2) plays a role in breaking down the components of the extracellular matrix, which allows cancer cells to advance and invade. Therefore, the inhibition of MMP2 shows potential as a promising strategy for treating cancer.Methods: This study employed computational screening to identify MMP2 inhibitors from a collection of 2,405 natural compounds. GLXC-26716, the co-crystal ligand of MMP2, served as the positive control. Virtual screening was performed using PyRx 8.0 software to find molecules that might inhibit the active site of MMP2.Results: The virtual screening process has identified five potential candidates: ZINC000000001412, ZINC000001612328, ZINC000001614079, ZINC000000119988, and ZINC0000000047553. These candidates were selected based on their strong binding affinities and interactions with MMP2. These compounds, which adhere to Lipinski's Rule of Five and have significant physicochemical properties, show promise as MMP2 inhibitors.Conclusion: The finding of this study indicates a preliminary investigation into an innovative approach for managing cancer that inhibits the invasion and dissemination of cancer cells.Keywords: Matrix Metalloproteinase-2; Natural compounds; Cancer; Virtual screening

Discovery of Potent Selective HDAC6 Inhibitors with 5-Phenyl-1H-indole Fragment: Virtual Screening, Rational Design, and Biological Evaluation.

Among the HDACs family, histone deacetylase 6 (HDAC6) has attracted extensive attention due to its unique structure and biological functions. Numerous studies have shown that compared with broad-spectrum HDACs inhibitors, selective HDAC6 inhibitors exert ideal efficacy in tumor treatment with insignificant toxic and side effects, demonstrating promising clinical application prospect. Herein, we carried out rational drug design by integrating a deep learning model, molecular docking, and molecular dynamics simulation technology to construct a virtual screening process. The designed derivatives with 5-phenyl-1H-indole fragment as Cap showed desirable cytotoxicity to the various tumor cell lines, all of which were within 15 μM (ranging from 0.35 to 14.87 μM), among which compound 5i had the best antiproliferative activities against HL-60 (IC50 = 0.35 ± 0.07 μM) and arrested HL-60 cells in the G0/G1 phase. In addition, 5i exhibited better isotype selective inhibitory activities due to the potent potency against HDAC6 (IC50 = 5.16 ± 0.25 nM) and the reduced inhibitory activities against HDAC1 (selective index ≈ 124), which was further verified by immunoblotting results. Moreover, the representative binding conformation of 5i on HDAC6 was revealed and the key residues contributing 5i's binding were also identified via decomposition free-energy analysis. The discovery of lead compound 5i also indicates that virtual screening is still a beneficial tool in drug discovery and can provide more molecular skeletons with research potential for drug design, which is worthy of widespread application.

High-throughput virtual screening of Streptomyces spp. metabolites as antiviral inhibitors against the Nipah virus matrix protein

Nipah virus (NiV) remains a significant global concern due to its impact on both the agricultural industry and human health, resulting in substantial economic and health consequences. Currently, there is no cure or commercially available vaccine for the virus. Therefore, it is crucial to prioritize the discovery of new and effective treatment options to prevent its continued spread. Streptomyces spp. are rich sources of metabolites known for their bioactivity against certain diseases; however, their potential as antiviral drugs against the Nipah virus remain unexplored. In this study, 6524 Streptomyces spp. metabolites were screened through in silico methods for their inhibitory effects against the Nipah virus matrix (NiV-M) protein, which assists in virion assembly of Nipah virus. Different computer-aided tools were utilized to carry out the virtual screening process: ADMET profiling revealed 913 compounds with excellent safety and efficacy profiles, molecular docking predicted the binding poses and associated docking scores of the ligands in their respective targets, MD simulations confirmed the binding stability of the top ten highest-scoring ligands in a 100 ns all-atom simulation, PCA elucidated simulation convergence, and MMPB(GB)SA calculations estimated the binding energies of the final candidate compounds and determined the key residues crucial for complex formation. Using in silico methods, we identified six metabolites targeting the main substrate-binding site and five targeting the dimerization site that exhibited excellent stability and strong binding affinity. We recommend testing these compounds in the next stages of drug development to confirm their effectiveness as therapeutic agents against Nipah virus.

In silico and in vitro study of bioactive compounds from Allium sativum with PTEN: A novel target and promising source for cancer diagnostic potentials

IntroductionCancer remains a significant global health issue, necessitating innovative management strategies. The tumor suppressor protein PTEN, due to its pivotal role in cancer development, is a promising target for therapeutic intervention. Materials and MethodsWe used dried Allium sativum (garlic) seed extract to discover phytocompounds that could inhibit PTEN in cancer. We conducted qualitative tests to determine the phytochemical composition of the extract and employed a DPPH radical scavenging assay to evaluate its antioxidant activity. Computational methods were used to explore the impact of Allium sativum and its constituents on PTEN. This involved predicting and validating 3D structures using the SWISS model and ITASSER, performing docking analysis with natural compounds from the Asinex database, and assessing the drug-likeness and ADMET characteristics of selected compounds using online tools. Molecular dynamics simulations (MDS) were conducted to examine protein–ligand interactions, and the results were analyzed to understand complex stability and fluctuations. ResultsA study on Allium sativum revealed the presence of diverse phytochemicals in its seeds and leaves. The leaves contained alkaloids and saponins, while the methanol extract of the leaves exhibited the highest flavonoid and phenolic contents. This methanol extract demonstrated potent antioxidant activity and significantly inhibited cytotoxicity. Through computer-aided drug design, we identified potential anticancer drugs derived from natural botanicals that target the PTEN protein. The virtual screening process led to the discovery of three lead compounds exhibiting high binding affinities and interactions with PTEN. Molecular docking and MDS indicated the strong binding energy and favorable drug-like properties of these compounds. While the protein exhibited some flexibility, the ligand–protein complex remained stable. ConclusionThis study contributes to the expanding literature on cancer drug discovery and emphasizes the importance of investigating natural plant compounds in the search for novel and effective cancer treatments.

Discovery of Novel Spike Inhibitors against SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the current coronavirus disease pandemic. With the rapid evolution of variant strains, finding effective spike protein inhibitors is a logical and critical priority. Angiotensin-converting enzyme 2 (ACE2) has been identified as the functional receptor for SARS-CoV-2 viral entry, and thus related therapeutic approaches associated with the spike protein-ACE2 interaction show a high degree of feasibility for inhibiting viral infection. Our computer-aided drug design (CADD) method meticulously analyzed more than 260,000 compound records from the United States National Cancer Institute (NCI) database, to identify potential spike inhibitors. The spike protein receptor-binding domain (RBD) was chosen as the target protein for our virtual screening process. In cell-based validation, SARS-CoV-2 pseudovirus carrying a reporter gene was utilized to screen for effective compounds. Ultimately, compounds C2, C8, and C10 demonstrated significant antiviral activity against SARS-CoV-2, with estimated EC50 values of 8.8 μM, 6.7 μM, and 7.6 μM, respectively. Using the above compounds as templates, ten derivatives were generated and robust bioassay results revealed that C8.2 (EC50 = 5.9 μM) exhibited the strongest antiviral efficacy. Compounds C8.2 also displayed inhibitory activity against the Omicron variant, with an EC50 of 9.3 μM. Thus, the CADD method successfully discovered lead compounds binding to the spike protein RBD that are capable of inhibiting viral infection.

Computational drug repurposing effort for identifying novel hits for the treatment of diseases such as endometriosis, uterine fibroids, and prostate cancer.

This research aimed to identify potential drug compounds from the ZINC15 molecule database that could effectively treat GnRH1R-related diseases. The study utilized molecular docking and molecular dynamics methods to achieve this goal, which is crucial in drug repurposing research. The virtual screening process involved analyzing known drug compounds using molecular docking. Additionally, molecular dynamics simulations and MM-GBSA were employed to evaluate the stability of the complexes and determine the interactions between the compounds and protein structure. As a result, this study provides significant insights for treating diseases such as endometriosis, uterine fibroids, and prostate cancer related to GnRH1R. The study also involved designing new drugs and identifying necessary molecular scaffolds.

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.

Classifying Beta-Secretase 1 Inhibitor Activity for Alzheimer’s Drug Discovery with LightGBM

This study explores the utilization of LightGBM, a gradient-boosting framework, to classify the inhibitory activity of beta-secretase 1 inhibitors, addressing the challenges of Alzheimer's disease drug discovery. The study aims to enhance classification performance by focusing on overcoming the limitations of traditional statistical models and conventional machine-learning techniques in handling complex molecular datasets. By sourcing a dataset of 7298 compounds from the ChEMBL database and calculating molecular descriptors for each compound as features, we employed LightGBM in conjunction with a set of carefully selected molecular descriptors to achieve a nuanced analysis of compound activities. The model's efficiency was benchmarked against traditional machine-learning algorithms, revealing LightGBM's superior accuracy (84.93%), precision (87.14%), sensitivity (89.93%), specificity (77.63%), and F1-score (88.17%) in classifying beta-secretase 1 inhibitor activity. The study underscores the critical role of molecular descriptors in understanding drug efficacy, highlighting LightGBM's potential in streamlining the virtual screening process. Conclusively, the findings advocate for LightGBM's adoption in computational drug discovery, offering a promising avenue for advancing Alzheimer's disease therapeutic development by facilitating the identification of potential drug candidates with enhanced precision and reliability.

Optimization of binding affinities in chemical space with generative pre-trained transformer and deep reinforcement learning.

The key challenge in drug discovery is to discover novel compounds with desirable properties. Among the properties, binding affinity to a target is one of the prerequisites and usually evaluated by molecular docking or quantitative structure activity relationship (QSAR) models. In this study, we developed SGPT-RL, which uses a generative pre-trained transformer (GPT) as the policy network of the reinforcement learning (RL) agent to optimize the binding affinity to a target. SGPT-RL was evaluated on the Moses distribution learning benchmark and two goal-directed generation tasks, with Dopamine Receptor D2 (DRD2) and Angiotensin-Converting Enzyme 2 (ACE2) as the targets. Both QSAR model and molecular docking were implemented as the optimization goals in the tasks. The popular Reinvent method was used as the baseline for comparison. The results on the Moses benchmark showed that SGPT-RL learned good property distributions and generated molecules with high validity and novelty. On the two goal-directed generation tasks, both SGPT-RL and Reinvent were able to generate valid molecules with improved target scores. The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal. Further analysis shows that SGPT-RL learned conserved scaffold patterns during exploration. The superior performance of SGPT-RL in the ACE2 task indicates that it can be applied to the virtual screening process where molecular docking is widely used as the criteria. Besides, the scaffold patterns learned by SGPT-RL during the exploration process can assist chemists to better design and discover novel lead candidates.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Exploring plant-derived small molecules as inhibitors of Marburg virus RNA binding protein activity

The search for antiviral medications is greatly influenced by the hunt for potent inhibitors of viral proteins. To find possible inhibitors of the RNA binding activity of the Marburg virus VP35 protein, we used a thorough in silico drug discovery approach in this investigation. A comprehensive virtual screening process, followed by a detailed MMGBSA analysis, led to the discovery of four potential inhibitory compounds viz. Kudzuisoflavone A, Miquelianin, Rutin, and Protopseudohypericin. They were identified from an extensive library of phytomolecules derived from three medicinal plants: Adiantum capillus-veneris, Hypericum perforatum, and Pueraria montana. In molecular dynamics (MD) simulations, all these compounds showed steady binding to the target protein and favourable interactions. Notably, the free binding energies of all the selected compounds were better than the myricetin, a well-known blocker of the VP35 protein of the Ebola virus. Overall, this investigation offers insightful information about the molecular interactions and binding dynamics of the identified inhibitors’ binding to the VP35 protein of the Marburg virus. The findings highlight the potential of three particular medicinal plants as sources of key chemicals for the creation of brand-new Marburg virus antiviral drugs. More experimental validation and optimization of the identified inhibitors are necessary in order to transform these findings into effective medicines for treating Marburg virus infections. Communicated by Ramaswamy H. Sarma

Molecular Docking Approaches in the Discovery and Development of Natural-based Functional Foods: An Opinion Study

Backgrounds and Aims: Functional foods or nutraceuticals are innovative foods that contain substances that may improve health or prevent disease, as long as the concentration is safe and high enough to provide the desired benefit. Nutrients, dietary fiber, phytochemicals, other compounds, and probiotics are a few examples of added functional ingredients. Food can be deemed "functional" if it positively impacts specific bodily processes that go beyond nutritional effects to promote health and well-being and/or prevent the development of chronic diseases. Therefore, this paper aims to elaborate opinions based on the latest evidence to introduce the molecular docking simulation approach in the discovery and development of functional foods. Results and Conclusions: In functional food research, the computer-based approach allows for the virtual screening of bioactive compounds in foods. Instead of performing extensive laboratory experiments, researchers can simulate the interactions between bioactive compounds and molecular targets using molecular docking simulations. This virtual screening process helps identify bioactive compounds in foods that have the potential to interact with specific targets, providing insights into their mechanisms of action and potential health benefits. Now the technology field continues to receive significant attention from several researchers so technological progress never goes out. This technological advancement can be integrated between functional food and digital platforms. This platform may contain personalized recommendations, track health parameters, and empower individuals to make informed choices and monitor progress.