Computational methods in drug discovery

AI-based prediction of protein-ligand binding affinity and discovery of potential natural product inhibitors against ERK2

Determination of protein-ligand binding affinity (PLA) is a key technological tool in hit discovery and lead optimization, which is critical to the drug development process. PLA can be determined directly by experimental methods, but it is time-consuming and costly. In recent years, deep learning has been widely applied to PLA prediction, the key of which lies in the comprehensive and accurate representation of proteins and ligands. In this study, we proposed a multi-modal deep learning model based on the early fusion strategy, called DeepLIP, to improve PLA prediction by integrating multi-level information, and further used it for virtual screening of extracellular signal-regulated protein kinase 2 (ERK2), an ideal target for cancer treatment. Experimental results from model evaluation showed that DeepLIP achieved superior performance compared to state-of-the-art methods on the widely used benchmark dataset. In addition, by combining previously developed machine learning models and molecular dynamics simulation, we screened three novel hits from a drug-like natural product library. These compounds not only had favorable physicochemical properties, but also bound stably to the target protein. We believe they have the potential to serve as starting molecules for the development of ERK2 inhibitors.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

BACKGROUND

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment.

METHODS

The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023.

RESULTS

Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion.

CONCLUSION

By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

Identification of ligands binding to MB327-PAM-1, a binding pocket relevant for resensitization of nAChRs

Desensitization of nicotinic acetylcholine receptors (nAChRs) can be induced by overstimulation with acetylcholine (ACh) caused by an insufficient degradation of ACh after poisoning with organophosphorus compounds (OPCs). Currently, there is no generally applicable treatment for OPC poisoning that directly targets the desensitized nAChR. The bispyridinium compound MB327, an allosteric modulator of nAChR, has been shown to act as a resensitizer of nAChRs, indicating that drugs binding directly to nAChRs can have beneficial effects after OPC poisoning. However, MB327 also acts as an inhibitor of nAChRs at higher concentrations and can thus not be used for OPC poisoning treatment. Consequently, novel, more potent resensitizers are required. To successfully design novel ligands, the knowledge of the binding site is of utmost importance. Recently, we performed in silico studies to identify a new potential binding site of MB327, MB327-PAM-1, for which a more affine ligand, UNC0646, has been described. In this work, we performed ligand-based screening approaches to identify novel analogs of UNC0646 to help further understand the structure-affinity relationship of this compound class. Furthermore, we used structure-based screenings and identified compounds representing four new chemotypes binding to MB327-PAM-1. One of these compounds, cycloguanil, is the active metabolite of the antimalaria drug proguanil and shows a higher affinity towards MB327-PAM-1 than MB327. Furthermore, cycloguanil can reestablish the muscle force in soman-inhibited rat muscles. These results can act as a starting point to develop more potent resensitizers of nAChR and to close the gap in the treatment after OPC poisoning.

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.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Describing the ligandin properties of Plasmodium falciparum and vivax glutathione transferase towards bromosulfophthalein from empirical and computational modelling viewpoints

Research has spotlighted glutathione transferase (GST) as a promising target for antimalarial drug development due to its pivotal role in cellular processes, including metabolizing toxins and managing oxidative stress. This interest arises from GST's potential to combat multidrug resistance in existing antimalarial drugs. Plasmodium falciparum GST (PfGST) and Plasmodium vivax GST (PvGST) are key targets; inhibiting them not only disrupt detoxification but also reduce their antioxidant capacity, a critical feature for potent antimalarials. Bromosulfophthalein (BSP), a clinical liver function dye, emerged as a potent cytosolic GST inhibitor. This study explored BSP's inhibitory properties on PfGST and PvGST, showcasing its binding capabilities through empirical and computational analyses. The study revealed BSP's ability to significantly inhibit GST activity, altering the proteins' structures and stability. Specifically, BSP binding induced spectral changes and impacted the proteins' thermal stability, reducing their melting temperatures. Computational simulations highlighted BSP's strong binding to PfGST and PvGST at their dimer interface, stabilized by various interactions, including hydrogen bonds and van der Waals forces. Notably, BSP's binding altered the proteins' compactness and conformational dynamics, suggesting a potential non-competitive, allosteric inhibition mechanism. This study provided novel insights into BSP's candidacy as an antimalarial drug by targeting PfGST and PvGST. Its ability to disrupt crucial functions of these enzymes' positions BSP as a promising candidate for further drug development in combating malariaCommunicated by Ramaswamy H. Sarma.

Cryo-electron microscopy-based drug design

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations

Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives. Quantum chemical and physicochemical descriptors have been investigated using density functional theory (DFT) and MM2 force fields, respectively, to develop 2D-QSAR models, while CoMSIA and CoMFA descriptors were used to build 3D-QSAR models. The robustness and predictive power of the reliable models were verified, via several validation methods, leading to the design of 6 new drug-candidates. Afterwards, 2 ligands were carefully selected using virtual screening methods, taking into account the applicability domain, synthetic accessibility, and Lipinski's criteria. Molecular docking and pharmacophore modelling studies were performed to examine potential interactions with aromatase (PDB ID: 3EQM). Finally, the ADMET properties were investigated in order to select potential drug-candidates against cervical cancer for experimental in vitro and in vivo testing.

Integrating pharmacophore model and deep learning for activity prediction of molecules with BRCA1 gene

In this paper, we propose a novel approach for predicting the activity/inactivity of molecules with the BRCA1 gene by combining pharmacophore modeling and deep learning techniques. Initially, we generated 3D pharmacophore fingerprints using a pharmacophore model, which captures the essential features and spatial arrangements critical for biological activity. These fingerprints served as informative representations of the molecular structures. Next, we employed deep learning algorithms to train a predictive model using the generated pharmacophore fingerprints. The deep learning model was designed to learn complex patterns and relationships between the pharmacophore features and the corresponding activity/inactivity labels of the molecules. By utilizing this integrated approach, we aimed to enhance the accuracy and efficiency of activity prediction. To validate the effectiveness of our approach, we conducted experiments using a dataset of known molecules with BRCA1 gene activity/inactivity from diverse sources. Our results demonstrated promising predictive performance, indicating the successful integration of pharmacophore modeling and deep learning. Furthermore, we utilized the trained model to predict the activity/inactivity of unknown molecules extracted from the ChEMBL database. The predictions obtained from the ChEMBL database were assessed and compared against experimentally determined values to evaluate the reliability and generalizability of our model. Overall, our proposed approach showcased significant potential in accurately predicting the activity/inactivity of molecules with the BRCA1 gene, thus enabling the identification of potential candidates for further investigation in drug discovery and development processes.

Integrating computational methods guided the discovery of phytochemicals as potential Pin1 inhibitors for cancer: pharmacophore modeling, molecular docking, MM-GBSA calculations and molecular dynamics studies

Pin1 is a pivotal player in interactions with a diverse array of phosphorylated proteins closely linked to critical processes such as carcinogenesis and tumor suppression. Its axial role in cancer initiation and progression, coupled with its overexpression and activation in various cancers render it a potential candidate for the development of targeted therapeutics. While several known Pin1 inhibitors possess favorable enzymatic profiles, their cellular efficacy often falls short. Consequently, the pursuit of novel Pin1 inhibitors has gained considerable attention in the field of medicinal chemistry. In this study, we employed the Phase tool from Schrödinger to construct a structure-based pharmacophore model. Subsequently, 449,008 natural products (NPs) from the SN3 database underwent screening to identify compounds sharing pharmacophoric features with the native ligand. This resulted in 650 compounds, which then underwent molecular docking and binding free energy calculations. Among them, SN0021307, SN0449787 and SN0079231 showed better docking scores with values of -9.891, -7.579 and -7.097 kcal/mol, respectively than the reference compound (-6.064 kcal/mol). Also, SN0021307, SN0449787 and SN0079231 exhibited lower free binding energies (-57.12, -49.81 and -46.05 kcal/mol, respectively) than the reference ligand (-37.75 kcal/mol). Based on these studies, SN0021307, SN0449787, and SN0079231 showed better binding affinity that the reference compound. Further the validation of these findings, molecular dynamics simulations confirmed the stability of the ligand-receptor complex for 100 ns with RMSD ranging from 0.6 to 1.8 Å. Based on these promising results, these three phytochemicals emerge as promising lead compounds warranting comprehensive biological screening in future investigations. These compounds hold great potential for further exploration regarding their efficacy and safety as Pin1 inhibitors, which could usher in new avenues for combating cancer.

Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods

We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.

Identification of novel inhibitors against VP40 protein of Marburg virus by integrating molecular modeling and dynamics approaches

Marburg virus (MV) is a highly etiological agent of haemorrhagic fever in humans and has spread across the world. Its outbreaks caused a 23-90% human death rate. However, there are currently no authorized preventive or curative measures yet. VP40 is the MV matrix protein, which builds protein shell underneath the viral envelope and confers hallmark filamentous. VP40 alone is able to induce assembly and budding of filamentous virus-like particles (VLPs), which resemble authentic virions. As a result, this research is credited with clarifying the function of VP40 and leading to the discovery of new therapeutic targets effective in combating MV disease (MVD). Virtual screening, molecular docking and molecular dynamics (MD) simulation were used to find the putative active chemicals based on a 3D pharmacophore model of the protein's active site cavity. Initially, andrographidine-C, a potent inhibitor was selected for the development of the pharmacophore model. Later, a library of 30,000 compounds along with the andrographidine-C was docked against VP40 protein. Three best hits including avanafil, diuvaretin and macrourone were subjected to further MD simulation analysis, as these compounds had better binding affinities as compared to andrographidine-C. Furthermore, throughout the 100 ns simulations, the back bone of VP40 protein in presence of avanafil, diuvaretin and macrourone remained stable which was further validated by MM-PBSA analysis. Additionally, all of these compounds depict maximum drug-like properties. The predicted drugs based on the ligand, avanafil, diuvaretin and macrourone could be exploited and developed as an alternative or complementary therapy for the treatment of MVD.Communicated by Ramaswamy H. Sarma.

Data insights from a Moroccan phytochemical database (MPDB) derived from aromatic & medicinal plants

The geographical location of Morocco and the diversity of its topography ensure a high variability of climate conditions, ranging from humid to Saharan, and extending through subhumid, arid, and semi-arid stages. This variability offers a high floristic diversity, while the medical use of these phytochemicals has not been fully explored. Advanced computer-aided drug discovery utilizes chemical biology to accelerate the study of phytochemicals at the molecular level and discover novel therapeutic pathways. Currently, there is no online resource for phytochemicals in Morocco. Therefore, it is of interest to describe the Moroccan Phytochemicals Database (MPDB), accessible, featuring over 600 phytochemicals derived from journal articles and other reports. The web interface of the database, which is simple and easy to use, provides each phytochemical's reference, plant sources, 3D structures, and all related information. Furthermore, we provide direct links to commercially available analogs from Mcule. In addition, we provide the results of the first virtual screening against cardiovascular targets. We present these data to facilitate further exploration and exploitation of Morocco's rich phytochemical resources, and to contribute to the global understanding and application of these compounds in the medical and scientific communities.

Scipion-Chem: An Open Platform for Virtual Drug Screening

Virtual drug screening (VDS) tackles the problem of drug discovery by computationally reducing the number of potential pharmacological molecules that need to be tested experimentally to find a new drug. To do so, several approaches have been developed through the years, typically focusing on either the physicochemical characteristics of the receptor structure (structure-based virtual screening) or those of the potential ligands (ligand-based virtual screening). Scipion is a workflow engine well suited for structural studies of biological macromolecules. Here, we present Scipion-chem, a new branch oriented to VDS. A total of 11 plugins have already been integrated from the most common programs used in the field. They can be used through the Scipion graphical user interface to execute and analyze typical VDS tasks. In addition, we have developed several consensus protocols that combine results from the different integrated programs to generate more robust predictions. Backstage, Scipion also facilitates the interoperability of those different software packages while tracking all of the intermediate files, parameters, and user decisions. In summary, in this article, we present Scipion-chem. This accessible, interoperable, and traceable platform provides the user with all of the tools to carry out a successful VDS workflow. Scipion-chem is openly available at https://github.com/scipion-chem.

Identification of paeoniflorin from Paeonia lactiflora pall. As an inhibitor of tryptophan 2,3-dioxygenase and assessment of its pharmacological effects on depressive mice

ETHNOPHARMACOLOGICAL RELEVANCE

The radix of Paeonia lactiflora Pall. (PaeR) is a traditional Chinese medicine (TCM) clinically used for treating depression. Although it has been established that PaeR can protect the liver and alleviate depressive-like behaviors, its bioactive chemicals and antidepressant mechanism remain unclear. Our pilot study showed that PaeR reduced the expression of the L-tryptophan- catabolizing enzyme tryptophan 2,3-dioxygenase (TDO) in the livers of stress-induced depression-like mice.

AIM OF THE STUDY

This study aimed to screen potential TDO inhibitors from PaeR and investigate the potential therapeutic use of TDO inhibition for treating depression.

MATERIALS AND METHODS

Molecular docking, magnetic ligand fishing, and secrete-pair dual luminescence assay were conducted for in vitro ligand discovery and high-throughput screening of TDO inhibitors. Stable TDO overexpression was achieved in HepG2 cell lines to evaluate the TDO inhibitory activities of drugs in vitro by RT-PCR and Western blot analyses of TDO at mRNA and protein levels. In vivo validation of TDO inhibitory potency and evaluation of TDO inhibition as a potential therapeutic strategy for major depressive disorder (MDD) were performed using mice subjected to "3 + 1″ combined stresses for at least 30 days to induce depression-like behaviors. A well-known TDO inhibitor, LM10, was evaluated in parallel.

RESULTS

The PaeR extract significantly ameliorated depressive-like behaviors of stressed mice, attributed to inhibition of TDO expression and tryptophan modulation metabolism. After a comprehensive analysis of molecular docking, ligand fishing, and luciferase assay, paeoniflorin was screened as a TDO inhibitor from the PaeR extract. This compound, structurally different from LM10, potently inhibited human and mouse TDO in cell- and animal-based assays. The effects of TDO inhibitors on MDD symptoms were evaluated in a stress-induced depression-like mouse model. In mice, both inhibitors had beneficial effects on stress-induced depressive-like behavioral despair and unhealthy physical status. Moreover, both inhibitors increased the liver serotonin/tryptophan ratio and decreased the kynurenine/tryptophan ratio after oral administration, demonstrating in vivo inhibition of TDO activity. Our data substantiated the potential of TDO inhibition as a therapeutic strategy to improve behavioral activity and decrease despair symptoms in major depressive disorder.

CONCLUSIONS

This study introduced a hitherto undocumented comprehensive screening strategy to identify TDO inhibitors in PaeR extract. Our findings also highlighted the potential of PaeR as a source of antidepressant constituents and pinpointed the inhibition of TDO as a promising therapeutic approach for managing major depressive disorder.

Approaches to increase the validity of gene family identification using manual homology search tools

Identifying homologs is an important process in the analysis of genetic patterns underlying traits and evolutionary relationships among species. Analysis of gene families is often used to form and support hypotheses on genetic patterns such as gene presence, absence, or functional divergence which underlie traits examined in functional studies. These analyses often require precise identification of all members in a targeted gene family. Manual pipelines where homology search and orthology assignment tools are used separately are the most common approach for identifying small gene families where accurate identification of all members is important. The ability to curate sequences between steps in manual pipelines allows for simple and precise identification of all possible gene family members. However, the validity of such manual pipeline analyses is often decreased by inappropriate approaches to homology searches including too relaxed or stringent statistical thresholds, inappropriate query sequences, homology classification based on sequence similarity alone, and low-quality proteome or genome sequences. In this article, we propose several approaches to mitigate these issues and allow for precise identification of gene family members and support for hypotheses linking genetic patterns to functional traits.

Structural insight into crystal structure of helicase domain of DDX53

DEAD box helicase proteins are a family of RNA helicases that participate in various RNA metabolisms such as RNA unwinding, RNA processing, and RNPase activities. A particular DEAD box protein, the DDX53 protein, is primarily expressed in cancer cells and plays a crucial role in tumorigenesis. Numerous studies have revealed that DDX53 interacts with various microRNA and Histone deacetylases. However, its molecular structure and the detailed binding interaction between DDX53 and microRNA or HDAC is still unclear. In this study, we used X-ray crystallography to investigate the 3D structure of the hlicase C-terminal domain of DDX53, and successfully determined its crystal structure at a resolution of 1.97 Å. Subsequently, a functional analysis of RNA was conducted by examining the binding properties thereof with DDX53 by transmission electron microscopy and computing-based molecular docking simulation. The defined 3D model of DDX53 not only provides a structural basis for the fundamental understanding of DDX53 but is also expected to contribute to the field of anti-cancer drug discovery such as structure-based drug discovery and computer-aided drug design.

Natural polymers as potential P-glycoprotein inhibitors: Pre-ADMET profile and computational analysis as a proof of concept to fight multidrug resistance in cancer

P-glycoprotein (P-gp) is known as the "multidrug resistance protein" because it contributes to tumor resistance to several different classes of anticancer drugs. The effectiveness of such polymers in treating cancer and delivering drugs has been shown in a wide range of in vitro and in vivo experiments. The primary objective of the present study was to investigate the inhibitory effects of several naturally occurring polymers on P-gp efflux, as it is known that P-gp inhibition can impede the elimination of medications. The objective of our study is to identify polymers that possess the potential to inhibit P-gp, a protein involved in drug resistance, with the aim of enhancing the effectiveness of anticancer drug formulations. The ADMET profile of all the selected polymers (Agarose, Alginate, Carrageenan, Cyclodextrin, Dextran, Hyaluronic acid, and Polysialic acid) has been studied, and binding affinities were investigated through a computational approach using the recently released crystal structure of P-gp with PDB ID: 7O9W. The advanced computational study was also done with the help of molecular dynamics simulation. The aim of the present study is to overcome MDR resulting from the activity of P-gp by using such polymers that can inhibit P-gp when used in formulations. The docking scores of native ligand, Agarose, Alginate, Carrageenan, Chitosan, Cyclodextrin, Dextran, Hyaluronic acid, and Polysialic acid were found to be -10.7, -8.5, -6.6, -8.7, -8.6, -24.5, -6.7, -8.3, and -7.9, respectively. It was observed that, Cyclodextrin possess multiple properties in drug delivery science and here also demonstrated excellent binding affinity. We propose that drug efflux-related MDR may be prevented by the use of Agarose, Carregeenan, Chitosan, Cyclodextrin, Hyaluronic acid, and/or Polysialic acid in the administration of anticancer drugs.

Molecular Modeling Unveils the Effective Interaction of B-RAF Inhibitors with Rare B-RAF Insertion Variants

The Food and Drug Administration (FDA) has approved MAPK inhibitors as a treatment for melanoma patients carrying a mutation in codon V600 of the BRAF gene exclusively. However, BRAF mutations outside the V600 codon may occur in a small percentage of melanomas. Although these rare variants may cause B-RAF activation, their predictive response to B-RAF inhibitor treatments is still poorly understood. We exploited an integrated approach for mutation detection, tumor evolution tracking, and assessment of response to treatment in a metastatic melanoma patient carrying the rare p.T599dup B-RAF mutation. He was addressed to Dabrafenib/Trametinib targeted therapy, showing an initial dramatic response. In parallel, in-silico ligand-based homology modeling was set up and performed on this and an additional B-RAF rare variant (p.A598_T599insV) to unveil and justify the success of the B-RAF inhibitory activity of Dabrafenib, showing that it could adeptly bind both these variants in a similar manner to how it binds and inhibits the V600E mutant. These findings open up the possibility of broadening the spectrum of BRAF inhibitor-sensitive mutations beyond mutations at codon V600, suggesting that B-RAF V600 WT melanomas should undergo more specific investigations before ruling out the possibility of targeted therapy.

A Variational Ansatz for Taylorized Imaginary Time Evolution

Being able to predict molecular properties and interactions is of utmost interest for academia as well as industry. But the vast complexity of strongly correlated molecular systems limits the performance of classical algorithms. In contrast, quantum computation has the potential to be a game changer in the field of molecular simulations. Despite the hope in quantum computation, the capabilities of current quantum computers are still insufficient for handling molecular systems of interest. In this paper, we propose a variational ansatz for today's noisy quantum computers to calculate the ground state with the help of imaginary time evolution. Although the imaginary time evolution operator is not unitary, it can be implemented on a quantum computer by a linear decomposition and subsequent Taylor series expansion. This has the advantage that only a set of shallow circuits needs to be computed on a quantum computer. The parallel nature of this algorithm can be exploited to speed-up simulations even further, if a privileged access to quantum computers is granted.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

In silico structure-based design of peptides or proteins as therapeutic tools for obesity or diabetes mellitus: A protocol for systematic review and meta analysis

BACKGROUND

In silico studies using dynamic simulation or molecular docking have boosted the screening and identification of molecules and/or targets in studies aimed at treating diseases such as obesity and diabetes mellitus, optimizing the development of new drugs. This study aims to describe a systematic review protocol on peptides and proteins evaluated in silico as potential therapeutic agents for obesity or diabetes mellitus.

METHODS

This protocol followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols and was registered in the International Prospective Register of Systematic Reviews database (number: CRD42022355540). The databases to be searched will be PubMed, ScienceDirect, Scopus, Web of Science, virtual health library, and EMBASE. It will be included in silico studies that evaluate the simulation by dynamics or molecular docking of proteins or peptides involved in treating obesity or diabetes mellitus. Two independent reviewers will select studies, extract data, and assess methodological quality using the adapted Strengthening the reporting of empirical simulation studies. A narrative synthesis of the included studies will be performed for the systematic reviews.

RESULTS

This protocol contemplates the production of 2 systematic reviews to be developed focusing on obesity or diabetes mellitus.

CONCLUSION

The reviews will enable knowledge of peptides and proteins involved in research treating these diseases and will emphasize the importance of in silico studies in this context and for the development of future studies.

A QSAR Study for Antileishmanial 2-Phenyl-2,3-dihydrobenzofurans †

Leishmaniasis, a parasitic disease that represents a threat to the life of millions of people around the globe, is currently lacking effective treatments. We have previously reported on the antileishmanial activity of a series of synthetic 2-phenyl-2,3-dihydrobenzofurans and some qualitative structure-activity relationships within this set of neolignan analogues. Therefore, in the present study, various quantitative structure-activity relationship (QSAR) models were created to explain and predict the antileishmanial activity of these compounds. Comparing the performance of QSAR models based on molecular descriptors and multiple linear regression, random forest, and support vector regression with models based on 3D molecular structures and their interaction fields (MIFs) with partial least squares regression, it turned out that the latter (i.e., 3D-QSAR models) were clearly superior to the former. MIF analysis for the best-performing and statistically most robust 3D-QSAR model revealed the most important structural features required for antileishmanial activity. Thus, this model can guide decision-making during further development by predicting the activity of potentially new leishmanicidal dihydrobenzofurans before synthesis.

Few-shot learning via graph embeddings with convolutional networks for low-data molecular property prediction

Graph neural networks and convolutional architectures have proven to be pivotal in improving the prediction of molecular properties in drug discovery. However, this is fundamentally a low data problem that is incompatible with regular deep learning approaches. Contemporary deep networks require large amounts of training data, which severely limits the prediction of new molecular entities from limited available data. In this paper, we address the challenge of low data in molecular property prediction by: (1) defining a set of deep learning architectures that accept compound chemical structures in the form of molecular graphs, (2) creating a few-shot learning strategy across graph neural networks and convolutional neural networks to leverage the rich information of graph embeddings, and (3) proposing a two-module meta-learning framework to learn from task-transferable knowledge and predict molecular properties on few-shot data. Furthermore, we conduct multiple experiments on two benchmark multiproperty datasets to demonstrate a superior performance over conventional graph-based baselines. ROC-AUC results for 10-shot experiments show an average improvement of $$+11.37\%$$ + 11.37 % on Tox21 and $$+0.53\%$$ + 0.53 % on SIDER, which are representative small-sized biological datasets for molecular property prediction.

QuantumTox: Utilizing quantum chemistry with ensemble learning for molecular toxicity prediction

Molecular toxicity prediction plays an important role in drug discovery, which is directly related to human health and drug fate. Accurately determining the toxicity of molecules can help weed out low-quality molecules in the early stage of drug discovery process and avoid depletion later in the drug development process. Nowadays, more and more researchers are starting to use machine learning methods to predict the toxicity of molecules, but these models do not fully exploit the 3D information of molecules. Quantum chemical information, which provides stereo structural information of molecules, can influence their toxicity. To this end, we propose QuantumTox, the first application of quantum chemistry in the field of drug molecule toxicity prediction compared to existing work. We extract the quantum chemical information of molecules as their 3D features. In the downstream prediction phase, we use Gradient Boosting Decision Tree and Bagging ensemble learning methods together to improve the accuracy and generalization of the model. A series of experiments on various tasks show that our model consistently outperforms the baseline model and that the model still performs well on small datasets of less than 300.

An overview on monkeypox virus: Pathogenesis, transmission, host interaction and therapeutics

Orthopoxvirus is one of the most notorious genus amongst the Poxviridae family. Monkeypox (MP) is a zoonotic disease that has been spreading throughout Africa. The spread is global, and incidence rates are increasing daily. The spread of the virus is rapid due to human-to-human and animals-to-human transmission. World Health Organization (WHO) has declared monkeypox virus (MPV) as a global health emergency. Since treatment options are limited, it is essential to know the modes of transmission and symptoms to stop disease spread. The information from host-virus interactions revealed significantly expressed genes that are important for the progression of the MP infection. In this review, we highlighted the MP virus structure, transmission modes, and available therapeutic options. Furthermore, this review provides insights for the scientific community to extend their research work in this field.

Machine learning in antibacterial discovery and development: A bibliometric and network analysis of research hotspots and trends

Machine learning (ML) methods are used in cheminformatics processes to predict the activity of an unknown drug and thus discover new potential antibacterial drugs. This article conducts a bibliometric study to analyse the contributions of leading authors, universities/organisations and countries in terms of productivity, citations and bibliographic linkage. A sample of 1596 Scopus documents for the period 2006-2022 is the basis of the study. In order to develop the analysis, bibliometrix R-Tool and VOSviewer software were used. We determined essential topics related to the application of ML in the field of antibacterial development (Computer model in antibacterial drug design, and Learning algorithms and systems for forecasting). We identified obsolete and saturated areas of research. At the same time, we proposed emerging topics according to the various analyses carried out on the corpus of published scientific literature (Title, abstract and keywords). Finally, the applied methodology contributed to building a broader and more specific "big picture" of ML research in antibacterial studies for the focus of future projects.

Design of Novel Phosphatidylinositol 3-Kinase Inhibitors for Non-Hodgkin's Lymphoma: Molecular Docking, Molecular Dynamics, and Density Functional Theory Studies on Gold Nanoparticles

Non-Hodgkin's lymphomas are a diverse collection of lymphoproliferative cancers that are much less predictable than Hodgkin's lymphomas with a far greater tendency to metastasize to extranodal sites. A quarter of non-Hodgkin's lymphoma cases develop at extranodal sites and the majority of them involve nodal and extranodal sites. The most common subtypes include follicular lymphoma, chronic/small lymphocytic leukaemia, mantel cell lymphoma, and marginal zone lymphoma. Umbralisib is one of the latest PI3Kδ inhibitors in clinical trials for several hematologic cancer indications. In this study, new umbralisib analogues were designed and docked to the active site of PI3Kδ, the main target of the phosphoinositol-3-kinase/Akt/mammalian target of the rapamycin pathway (PI3K/AKT/mTOR). This study resulted in eleven candidates, with strong binding to PI3Kδ with a docking score between -7.66 and -8.42 Kcal/mol. The docking analysis of ligand-receptor interactions between umbralisib analogues bound to PI3K showed that their interactions were mainly controlled by hydrophobic interactions and, to a lesser extent, by hydrogen bonding. In addition, the MM-GBSA binding free energy was calculated. Analogue 306 showed the highest free energy of binding with -52.22 Kcal/mol. To identify the structural changes and the complexes' stability of proposed ligands, molecular dynamic simulation was used. Based on this research finding, the best-designed analogue, analogue 306, formed a stable ligand-protein complex. In addition, pharmacokinetics and toxicity analysis using the QikProp tool demonstrated that analogue 306 had good absorption, distribution, metabolism, and excretion properties. Additionally, it has a promising predicted profile in immune toxicity, carcinogenicity, and cytotoxicity. In addition, analogue 306 had stable interactions with gold nanoparticles that have been studied using density functional theory calculations. The best interaction with gold was observed at the oxygen atom number 5 with -29.42 Kcal/mol. Further in vitro and in vivo investigations are recommended to be carried out to verify the anticancer activity of this analogue.

SiteRadar: Utilizing Graph Machine Learning for Precise Mapping of Protein-Ligand-Binding Sites

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Thermal Titration Molecular Dynamics (TTMD): Not Your Usual Post-Docking Refinement

Molecular docking is one of the most widely used computational approaches in the field of rational drug design, thanks to its favorable balance between the rapidity of execution and the accuracy of provided results. Although very efficient in exploring the conformational degrees of freedom available to the ligand, docking programs can sometimes suffer from inaccurate scoring and ranking of generated poses. To address this issue, several post-docking filters and refinement protocols have been proposed throughout the years, including pharmacophore models and molecular dynamics simulations. In this work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of protein-ligand unbinding kinetics, to the refinement of docking results. TTMD evaluates the conservation of the native binding mode throughout a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints. The protocol was successfully applied to retrieve the native-like binding pose among a set of decoy poses of drug-like ligands generated on four different pharmaceutically relevant biological targets, including casein kinase 1δ, casein kinase 2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease.

Unveiling the Efficacy of Sesquiterpenes from Marine Sponge Dactylospongia elegans in Inhibiting Dihydrofolate Reductase Using Docking and Molecular Dynamic Studies

Dihydrofolate reductase (DHFR) is a crucial enzyme that maintains the levels of 5,6,7,8-tetrahydrofolate (THF) required for the biological synthesis of the building blocks of DNA, RNA, and proteins. Over-activation of DHFR results in the progression of multiple pathological conditions such as cancer, bacterial infection, and inflammation. Therefore, DHFR inhibition plays a major role in treating these illnesses. Sesquiterpenes of various types are prime metabolites derived from the marine sponge Dactylospongia elegans and have demonstrated antitumor, anti-inflammation, and antibacterial capacities. Here, we investigated the in silico potential inhibitory effects of 87 D. elegans metabolites on DHFR and predicted their ADMET properties. Compounds were prepared computationally for molecular docking into the selected crystal structure of DHFR (PDB: 1KMV). The docking scores of metabolites 34, 28, and 44 were the highest among this series (gscore values of -12.431, -11.502, and -10.62 kcal/mol, respectively), even above the co-crystallized inhibitor SRI-9662 score (-10.432 kcal/mol). The binding affinity and protein stability of these top three scored compounds were further estimated using molecular dynamic simulation. Compounds 34, 28, and 44 revealed high binding affinity to the enzyme and could be possible leads for DHFR inhibitors; however, further in vitro and in vivo investigations are required to validate their potential.

Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development

The discovery and advances of medicines may be considered as the ultimate relevant translational science effort that adds to human invulnerability and happiness. But advancing a fresh medication is a quite convoluted, costly, and protracted operation, normally costing USD ~2.6 billion and consuming a mean time span of 12 years. Methods to cut back expenditure and hasten new drug discovery have prompted an arduous and compelling brainstorming exercise in the pharmaceutical industry. The engagement of Artificial Intelligence (AI), including the deep-learning (DL) component in particular, has been facilitated by the employment of classified big data, in concert with strikingly reinforced computing prowess and cloud storage, across all fields. AI has energized computer-facilitated drug discovery. An unrestricted espousing of machine learning (ML), especially DL, in many scientific specialties, and the technological refinements in computing hardware and software, in concert with various aspects of the problem, sustain this progress. ML algorithms have been extensively engaged for computer-facilitated drug discovery. DL methods, such as artificial neural networks (ANNs) comprising multiple buried processing layers, have of late seen a resurgence due to their capability to power automatic attribute elicitations from the input data, coupled with their ability to obtain nonlinear input-output pertinencies. Such features of DL methods augment classical ML techniques which bank on human-contrived molecular descriptors. A major part of the early reluctance concerning utility of AI in pharmaceutical discovery has begun to melt, thereby advancing medicinal chemistry. AI, along with modern experimental technical knowledge, is anticipated to invigorate the quest for new and improved pharmaceuticals in an expeditious, economical, and increasingly compelling manner. DL-facilitated methods have just initiated kickstarting for some integral issues in drug discovery. Many technological advances, such as "message-passing paradigms", "spatial-symmetry-preserving networks", "hybrid de novo design", and other ingenious ML exemplars, will definitely come to be pervasively widespread and help dissect many of the biggest, and most intriguing inquiries. Open data allocation and model augmentation will exert a decisive hold during the progress of drug discovery employing AI. This review will address the impending utilizations of AI to refine and bolster the drug discovery operation.

In silico studies on recreational drugs: 3D quantitative structure activity relationship prediction of classified and de novo designer benzodiazepines

Currently, increasing availability and popularity of designer benzodiazepines (DBZDs) constitutes a primary threat to public health. To assess this threat, the biological activity/potency of DBZDs was investigated using in silico studies. Specific Quantitative Structure Activity Relationship (QSAR) models were developed in Forge™ for the prediction of biological activity (IC50 ) on the γ-aminobutyric acid A receptor (GABA-AR) of previously identified classified and unclassified DBDZs. A set of new potential ligands resulting from scaffold hopping studies conducted with MOE® was also evaluated. Two generated QSAR models (i.e. 3D-field QSAR and RVM) returned very good performance statistics (r2  = 0.98 [both] and q2  = 0.75 and 0.72, respectively). The DBZDs predicted to be the most active were flubrotizolam, clonazolam, pynazolam and flucotizolam, consistently with what reported in literature and/or drug discussion fora. The scaffold hopping studies strongly suggest that replacement of the pendant phenyl moiety with a five-membered ring could increase biological activity and highlight the existence of a still unexplored chemical space for DBZDs. QSAR could be of use as a preliminary risk assessment model for (newly) identified DBZDs, as well as scaffold hopping for the creation of computational libraries that could be used by regulatory bodies as support tools for scheduling procedures.

A Computational Study on Selected Alkaloids as SARS-CoV-2 Inhibitors: PASS Prediction, Molecular Docking, ADMET Analysis, DFT, and Molecular Dynamics Simulations

Despite treatments and vaccinations, it remains difficult to develop naturally occurring COVID-19 inhibitors. Here, our main objective is to find potential lead compounds from the retrieved alkaloids with antiviral and other biological properties that selectively target the main SARS-CoV-2 protease (M^pro), which is required for viral replication. In this work, 252 alkaloids were aligned using Lipinski's rule of five and their antiviral activity was then assessed. The prediction of activity spectrum of substances (PASS) data was used to confirm the antiviral activities of 112 alkaloids. Finally, 50 alkaloids were docked with M^pro. Furthermore, assessments of molecular electrostatic potential surface (MEPS), density functional theory (DFT), and absorption, distribution, metabolism, excretion, and toxicity (ADMET) were performed, and a few of them appeared to have potential as candidates for oral administration. Molecular dynamics simulations (MDS) with a time step of up to 100 ns were used to confirm that the three docked complexes were more stable. It was found that the most prevalent and active binding sites that limit M^pro'sactivity are PHE294, ARG298, and GLN110. All retrieved data were compared to conventional antivirals, fumarostelline, strychnidin-10-one (L-1), 2,3-dimethoxy-brucin (L-7), and alkaloid ND-305B (L-16) and were proposed as enhanced SARS-CoV-2 inhibitors. Finally, with additional clinical or necessary study, it may be able to use these indicated natural alkaloids or their analogs as potential therapeutic candidates.

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

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

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

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

Computational Exploration and Characterization of Potential Calcium Sensitizing Mutations in Cardiac Troponin C

Calcium-dependent heart muscle contraction is regulated by the cardiac troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium binding subunit (cNTnC). cNTnC contains one calcium binding site (site II), and altered calcium binding in this site has been studied for decades. It has been previously shown that cNTnC mutants, which increase calcium sensitization may have therapeutic benefits, such as restoring cardiac muscle contractility and functionality post-myocardial infarction events. Here, we computationally characterized eight mutations for their potential effects on calcium binding affinity in site II of cNTnC. We utilized two distinct methods to estimate calcium binding: adaptive steered molecular dynamics (ASMD) and thermodynamic integration (TI). We observed a sensitizing trend for all mutations based on the employed ASMD methodology. The TI results showed excellent agreement with experimentally known calcium binding affinities in wild-type cNTnC. Based on the TI results, five mutants were predicted to increase calcium sensitivity in site II. This study presents an interesting comparison of the two computational methods, which have both been shown to be valuable tools in characterizing the impacts of calcium sensitivity in mutant cNTnC systems.

The FMO2 analysis of the ligand-receptor binding energy: the Biscarbene-Gold(I)/DNA G-Quadruplex case study

In this work, the ab initio fragment molecular orbital (FMO) method was applied to calculate and analyze the binding energy of two biscarbene-Au(I) derivatives, [Au(9-methylcaffein-8-ylidene)₂]⁺ and [Au(1,3-dimethylbenzimidazol-2-ylidene)₂]⁺, to the DNA G-Quadruplex structure. The FMO2 binding energy considers the ligand-receptor complex as well as the isolated forms of energy-minimum state of ligand and receptor, providing a better description of ligand-receptor affinity compared with simple pair interaction energies (PIE). Our results highlight important features of the binding process of biscarbene-Au(I) derivatives to DNA G-Quadruplex, indicating that the total deformation-polarization energy and desolvation penalty of the ligands are the main terms destabilizing the binding. The pair interaction energy decomposition analysis (PIEDA) between ligand and nucleobases suggest that the main interaction terms are electrostatic and charge-transfer energies supporting the hypothesis that Au(I) ion can be involved in π-cation interactions further stabilizing the ligand-receptor complex. Moreover, the presence of polar groups on the carbene ring, as C = O, can improve the charge-transfer interaction with K⁺ ion. These findings can be employed to design new powerful biscarbene-Au(I) DNA-G quadruplex binders as promising anticancer drugs. The procedure described in this work can be applied to investigate any ligand-receptor system and is particularly useful when the binding process is strongly characterized by polarization, charge-transfer and dispersion interactions, properly evaluated by ab initio methods.

The effect of Mg2+ on Ca2+ binding to cardiac troponin C in hypertrophic cardiomyopathy associated TNNC1 variants

Cardiac troponin C (cTnC) is the critical Ca2+ -sensing component of the troponin complex. Binding of Ca2+ to cTnC triggers a cascade of conformational changes within the myofilament that culminate in force production. Hypertrophic cardiomyopathy (HCM)-associated TNNC1 variants generally induce a greater degree and duration of Ca2+ binding, which may underly the hypertrophic phenotype. Regulation of contraction has long been thought to occur exclusively through Ca2+ binding to site II of cTnC. However, work by several groups including ours suggest that Mg2+ , which is several orders of magnitude more abundant in the cell than Ca2+ , may compete for binding to the same cTnC regulatory site. We previously used isothermal titration calorimetry (ITC) to demonstrate that physiological concentrations of Mg2+ may decrease site II Ca2+ -binding in both N-terminal and full-length cTnC. Here, we explore the binding of Ca2+ and Mg2+ to cTnC harbouring a series of TNNC1 variants thought to be causal in HCM. ITC and thermodynamic integration (TI) simulations show that A8V, L29Q and A31S elevate the affinity for both Ca2+ and Mg2+ . Further, L48Q, Q50R and C84Y that are adjacent to the EF hand binding motif of site II have a more significant effect on affinity and the thermodynamics of the binding interaction. To the best of our knowledge, this work is the first to explore the role of Mg2+ in modifying the Ca2+ affinity of cTnC mutations linked to HCM. Our results indicate a physiologically significant role for cellular Mg2+ both at baseline and when elevated on modifying the Ca2+ binding properties of cTnC and the subsequent conformational changes which precede cardiac contraction.

A Scaffold-based Deep Generative Model Considering Molecular Stereochemical Information

Designing molecules with specific scaffolds can facilitate the discovery and optimization of lead compounds. Some scaffold-based molecular generation models have been developed using deep-learning methods based on specific scaffolds, although incorporating scaffold generalization is expected to achieve scaffold hopping. Moreover, most of the existing models focus on the 2D shape of the scaffold and overlook the stereochemical properties of the compound, especially for natural products. In this study, we optimized the scaffold-based molecular generation model designed by Lim et al. (Chemical Science 2020, 11, 1153-1164). Real-time ultrafast shape recognition with pharmacophore constraints (USRCAT) was introduced into the model to search for molecules similar to the 3D conformation and pharmacophore of the input scaffold sourced from the training set; the searched molecules were then used as new scaffolds to execute scaffold hopping. The optimized model could generate new molecules with the same chirality as the input scaffold. Furthermore, the probability distribution of the molecular structure and various physicochemical properties were analyzed to evaluate the model's generation capability. We thus believe that the optimized model can provide a basis for medicinal chemists to explore a wider chemical space toward optimization of the lead compounds and to screen the virtual compound library.

Actives-Based Receptor Selection Strongly Increases the Success Rate in Structure-Based Drug Design and Leads to Identification of 22 Potent Cancer Inhibitors

Computer-aided drug design, an important component of the early stages of the drug discovery pipeline, routinely identifies large numbers of false positive hits that are subsequently confirmed to be experimentally inactive compounds. We have developed a methodology to improve true positive prediction rates in structure-based drug design and have successfully applied the protocol to twenty target systems and identified the top three performing conformers for each of the targets. Receptor performance was evaluated based on the area under the curve of the receiver operating characteristic curve for two independent sets of known actives. For a subset of five diverse cancer-related disease targets, we validated our approach through experimental testing of the top 50 compounds from a blind screening of a small molecule library containing hundreds of thousands of compounds. Our methods of receptor and compound selection resulted in the identification of 22 novel inhibitors in the low μM-nM range, with the most potent being an EGFR inhibitor with an IC₅₀ value of 7.96 nM. Additionally, for a subset of five independent target systems, we demonstrated the utility of Gaussian accelerated molecular dynamics to thoroughly explore a target system's potential energy surface and generate highly predictive receptor conformations.

Lead/Drug Discovery from Natural Resources

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

Molecular Dynamics Simulation of Ligands from Anredera cordifolia (Binahong) to the Main Protease (M pro) of SARS-CoV-2

COVID-19 in Indonesia is considered to be entering the endemic phase, and the population is expected to live side by side with the SARS-CoV 2 viruses and their variants. In this study, procyanidin, oleic acid, methyl linoleic acid, and vitexin, four compounds from binahong leaves-tropical/subtropical plant, were examined for their interactions with the major protease (Mpro) of the SARS-CoV 2 virus. Molecular dynamics simulation shows that procyanidin and vitexin have the best docking scores of -9.132 and -8.433, respectively. Molecular dynamics simulation also shows that procyanidin and vitexin have the best Root Mean Square Displacement (RMSD) and Root Mean Square Fluctuation (RMSF) performance due to dominant hydrogen, hydrophobic, and water bridge interactions. However, further strain energy calculation obtained from ligand torsion analyses, procyanidin and vitexin do not conform as much as quercetin as ligand control even though these two ligands have good performance in terms of interaction with the target protein.

Progressive alignment of crystals: reproducible and efficient assessment of crystal structure similarity

During in silico crystal structure prediction of organic molecules, millions of candidate structures are often generated. These candidates must be compared to remove duplicates prior to further analysis (e.g. optimization with electronic structure methods) and ultimately compared with structures determined experimentally. The agreement of predicted and experimental structures forms the basis of evaluating the results from the Cambridge Crystallographic Data Centre (CCDC) blind assessment of crystal structure prediction, which further motivates the pursuit of rigorous alignments. Evaluating crystal structure packings using coordinate root-mean-square deviation (RMSD) for N molecules (or N asymmetric units) in a reproducible manner requires metrics to describe the shape of the compared molecular clusters to account for alternative approaches used to prioritize selection of molecules. Described here is a flexible algorithm called Progressive Alignment of Crystals (PAC) to evaluate crystal packing similarity using coordinate RMSD and introducing the radius of gyration (R _g) as a metric to quantify the shape of the superimposed clusters. It is shown that the absence of metrics to describe cluster shape adds ambiguity to the results of the CCDC blind assessments because it is not possible to determine whether the superposition algorithm has prioritized tightly packed molecular clusters (i.e. to minimize R _g) or prioritized reduced RMSD (i.e. via possibly elongated clusters with relatively larger R _g). For example, it is shown that when the PAC algorithm described here uses single linkage to prioritize molecules for inclusion in the superimposed clusters, the results are nearly identical to those calculated by the widely used program COMPACK. However, the lower R _g values obtained by the use of average linkage are favored for molecule prioritization because the resulting RMSDs more equally reflect the importance of packing along each dimension. It is shown that the PAC algorithm is faster than COMPACK when using a single process and its utility for biomolecular crystals is demonstrated. Finally, parallel scaling up to 64 processes in the open-source code Force Field X is presented.