A Review on Applications of Computational Methods in Drug Screening and Design

Computational identification of novel natural inhibitors against triple mutant DNA gyrase A in fluoroquinolone-resistant Salmonella Typhimurium

The rising resistance to fluoroquinolones in Salmonella Typhimurium poses a significant global health challenge. This computational research addresses the pressing need for new therapeutic drugs by utilizing various computational tools to identify potential natural compounds that can inhibit the triple mutant DNA gyrase subunit A enzyme, which is crucial in fluoroquinolone resistance. Initially, the three-dimensional structure of the wild-type DNA gyrase A protein was modeled using homology modeling, and followed by in silico mutagenesis to create the clinically relevant triple mutant (SER83PHE, ASP87GLY, ALA119SER) DNA gyrase A protein structure. The structural stability and integrity of the modeled protein were ensured through rigorous validation. Subsequently, a high-throughput virtual screening of a curated library of natural compounds was conducted to identify potential inhibitors against wild-type and triple-mutant proteins. The selected potent lead molecules comprehensively evaluated their physicochemical properties, ADME/T properties, and binding affinities via ADME/T assessment and molecular docking studies. The safest and most promising ligands were chosen for dynamics studies to analyze their dynamic behavior and protein stability before and after the binding of ligands. Our results showed that the natural compounds from the ChemDiv database, CID: 0407-0108, N039-0003, 1080-0568, and 0099-0261 have binding energies ranging from -4.32 to -5.69 kcal/mol and exhibit excellent physio-chemical properties, affinities, and are stable in their dynamic environments over 100 ns for both wild-type and triple mutant DNA gyrase A complexes. These compounds provide a promising alternative treatment for fluoroquinolone-resistant Salmonella Typhimurium infections.

Machine learning-based screening and molecular simulations for discovering novel PARP-1 inhibitors targeting DNA repair mechanisms for breast cancer therapy

Cancer remains one of the leading causes of death worldwide, with the rising incidence of breast cancer being a significant public health concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as a promising therapeutic target for breast cancer treatment due to its crucial role in DNA repair. This study aimed to discover novel, targeted, and non-toxic PARP-1 inhibitors using an integrated approach that combines machine learning-based screening, molecular docking simulations, and quantum mechanical calculations. We trained a widely used machine learning models, Random Forest, using bioactivity data from known PARP-1 inhibitors. After evaluating the performance, it was used to screen an FDA-approved drug library, successfully identifying Atazanavir, Brexpiprazole, Raltegravir, and Nisoldipine as potential PARP-1 inhibitors. These compounds were further validated through molecular docking and all-atom molecular dynamics simulations, highlighting their potential for breast cancer therapy. The binding free energies indicated that Atazanavir at - 41.86 kJ/mol and Brexpiprazole at - 45.44 kJ/mol exhibited superior binding affinity compared to the control drug at - 30.42 kJ/mol, highlighting their promise as candidates for breast cancer therapy. Subsequent optimized geometries and electron density mappings of the two molecular structures revealed a Gibbs free energy of - 2334.610 Ha for the first molecule and - 1682.278316 Ha for the second, confirming enhanced stability compared to the standard drug. This study not only highlights the efficacy of machine learning in drug discovery but also underscores the importance of quantum mechanics in validating molecular stability, setting a robust foundation for future pharmacological explorations. Additionally, this approach could revolutionize the drug repurposing process by significantly reducing the time and cost associated with traditional drug development methods. Our results establish a promising basis for subsequent research aimed at optimizing these PARP-1 inhibitors for clinical use, potentially offering more effective treatment options for breast cancer patients.

Subtractive genome mining in Xanthomonas citri pv. citri strain 306 for identifying novel drug target proteins coupled with in-depth protein-protein interaction and coevolution analysis - A leap towards prospective drug design

Citrus canker poses a serious threat to a highly significant citrus fruit crop, this disease caused by one of the most destructive bacterial plant pathogens Xanthomonas citri pv. citri (Xcc). Bacterial plant diseases significantly reduce crop yields worldwide, making it more difficult to supply the growing food demand. The high levels of antibiotic resistance in Xcc strains are diminishing the efficacy of current control measures, necessitating the exploration of novel therapeutic targets to address the escalating antimicrobial resistance trend. Genome subtraction approach along with protein-protein network and coevolution analysis were used to identify potential drug targets in Xcc stain 306. The study involved retrieving the Xcc proteome from the UniProt database, eliminating paralogous proteins using CD-HIT (80 % identity cutoff), and selecting nonhomologous proteins through BLASTp (e-value <0.005). Essential proteins were identified using BLAST against the DEG (e-value cutoff 0.00001). 750 essential proteins were identified that are nonhomologous to citrus plant. Subsequent analyses included metabolic pathway assessment, subcellular localization prediction, and druggability analysis. Protein network analysis, coevolution analysis, protein active site identification was also performed. In conclusion, this study identified eight potential drug targets (GlmU, CheA, RmlD, GspE, FleQ, RpoN, Shk, SecB), highlighting RpoN, FleQ, and SecB as unprecedented targets for Xcc. These findings may contribute to the development of novel antimicrobial agents in the future that can efficiently control citrus canker disease.

ISLRWR: A network diffusion algorithm for drug-target interactions prediction

Machine learning techniques and computer-aided methods are now widely used in the pre-discovery tasks of drug discovery, effectively improving the efficiency of drug development and reducing the workload and cost. In this study, we used multi-source heterogeneous network information to build a network model, learn the network topology through multiple network diffusion algorithms, and obtain compressed low-dimensional feature vectors for predicting drug-target interactions (DTIs). We applied the metropolis-hasting random walk (MHRW) algorithm to improve the performance of the random walk with restart (RWR) algorithm, forming the basis by which the self-loop probability of the current node is removed. Additionally, the propagation efficiency of the MHRW was improved using the improved metropolis-hasting random walk (IMRWR) algorithm, facilitating network deep sampling. Finally, we proposed a correction of the transfer probability of the entire network after increasing the self-loop rate of isolated nodes to form the ISLRWR algorithm. Notably, the ISLRWR algorithm improved the area under the receiver operating characteristic curve (AUROC) by 7.53 and 5.72%, and the area under the precision-recall curve (AUPRC) by 5.95 and 4.19% compared to the RWR and MHRW algorithms, respectively, in predicting DTIs performance. Moreover, after excluding the interference of homologous proteins (popular drugs or targets may lead to inflated prediction results), the ISLRWR algorithm still showed a significant performance improvement.

Therapeutic exploration potential of adenosine receptor antagonists through pharmacophore ligand-based modelling and pharmacokinetics studies against Parkinson disease

Parkinson's Disease (PD) is a neurodegenerative disorder that primarily affects persons aged 65 and older. It leads to a decline in motor function as a result of the buildup of abnormal protein deposits called Lewy bodies in the brain. Existing therapies exhibit restricted effectiveness and undesirable side effects. The objective was to discover potent medications that have demonstrated effectiveness in treating PD by employing computational methods. This work employed a comprehensive approach to evaluate 70 pyrimidine derivatives for their potential in treating PD. The evaluation involved the use of QSAR modelling, virtual screening, molecular docking, MD simulation, ADMET analysis, and antagonist inhibitor creation. Six compounds passed all the evaluation, while for MD simulation, carried out between the compound with best docking score and the reference drug, compound 57 was discovered to possess more stability compared to theophylline which is the reference drug, and it functions as a primary inhibitor of the adenosine A2A receptor. Additionally, the study determined that compound 57 satisfied the Rule of Five (Ro5) standards and exhibited robust physicochemical characteristics. The compound exhibited moderate to low levels of hERG inhibition. The conducted investigations highlighted promising outcomes of natural compounds that can orient towards the rational development of effective Parkinson's disease inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00305-9.

Computer-aided drug design approaches for the identification of potent inhibitors targeting elongation factor G of Mycobacterium tuberculosis

Elongation factor G (EF-G) is essential for protein synthesis in Mycobacterium tuberculosis (Mtb), positioning it as a promising target for anti-tubercular drug development. This study employs Structure-Based Drug Design (SBDD) to identify potential small molecule inhibitors that specifically target EF-G. Initially, binding hotspots on EF-G were pinpointed, and the binding modes of various compounds were analyzed. Through protein-protein interaction studies, several promising candidates were validated. Virtual screening and molecular docking techniques were utilized to evaluate the binding affinities and interactions of 20 candidate molecules with Mtb EF-G. Additionally, toxicity profiles of these compounds were assessed using predictive models, which indicated non-carcinogenic properties. To further refine the selection process, Support Vector Machine (SVM) and Random Forest models were applied to predict cell wall permeability. Notably, Asinex (8853) and Asinex (102619) emerged as top candidates, boasting high probability scores for effective permeability. Molecular docking and molecular dynamics (MD) simulations revealed that Asinex (8853), Asinex (102619), and Otava (79226) exhibited strong binding affinities and favorable conformations within the active site of Mtb EF-G. These findings suggest that these compounds have significant potential as inhibitors, warranting further investigation into their efficacy as novel anti-tubercular agents. Overall, this study emphasizes the value of Structure-Based Drug Design in identifying promising therapeutic candidates against tuberculosis by targeting essential bacterial factors like EF-G.

Development of Thermosensitive Mucoadhesive Gel Based Encapsulated Lipid Microspheres as Nose-to-Brain Rivastigmine Delivery System

Alzheimer's disease (ALZ) is a neurodegenerative disease that damages neuronal cells and causes decline in cognitive abilities. Administration of cholinesterase inhibitor compounds is the primary choice in the treatment of ALZ, one of which is rivastigmine (RVT). Several routes of administration of RVT are available, such as oral and transdermal. However, in the oral route, RVT has low bioavailability, undergoes first-pass metabolism, and the presence of the blood-brain barrier (BBB) reduces the therapeutic concentration of RVT. The transdermal route is nonselective target in the brain. This study aims to combine thermosensitive mucoadhesive gel (TG) and lipid microspheres (LM) as a drug delivery system to improve the efficacy of RVT. Combining these will prevent systemic side effects of RVT and increase drug concentration in the brain. LM was formulated with varying concentrations of Compritol polymer. The results of LM evaluation showed the values of particle size, PDI, and %EE and %DL were 8.519 μm, 0.018 ± 0.004, 72.54%, and 76.43%, respectively. The TG formulation can provide a liquid form at room temperature (25 °C) and a gel at nasal temperature (35 °C). Hemolytic and HET-CAM tests confirmed TG RVT LM's safety for use. Ex vivo studies showed controlled and sustained release of TG RVT LM, and in vivo studies showed TG RVT LM a higher pharmacokinetic profile in the brain than oral formulations and injections. The Cmax was found to be 7.05 ± 0.55 μg/cm3, Tmax was 24 h, and AUC0-24, which is related to the effectiveness of brain targeting, was 225.73 μg/cm3. In conclusion, this study shows the successful development of TG RVT LM, as evidenced by improved drug delivery to the brain, which is characterized by higher concentrations of RVT in the brain compared with oral and injectable RVT, this delivery system shows potential as a future treatment for Alzheimer's disease.

Drug Screening of Flavonoids as Potential VEGF Inhibitors Through Computational Docking and Cell Models

Vascular endothelial growth factor (VEGF), also known as VEGF-A, has been linked to various diseases, such as wet age-related macular degeneration (wAMD) and cancer. Even though there are VEGF inhibitors that are currently commercially available in clinical applications, severe adverse effects have been associated with these treatments. There is still a need to develop novel VEGF-based therapeutics against these VEGF-related diseases. Here, we established a series of VEGF-based computational docking analyses and cell models, such as a wound healing assay in HaCaT cells and an evaluation of NF-κB performance in macrophages, to screen a large library of flavonoid-type phytochemicals. Three flavonoids, namely, farrerol, ononin and (-)-epicatechin, were shown to express binding affinities to VEGF protein and inhibit VEGF-mediated biological activities. The investigation evidently suggested that the three flavonoids above could be considered potential anti-VEGF agents for the following drug development against VEGF-mediated diseases.

Validating Structural Predictions of Conjugated Macromolecules in Espaloma-Enabled Reproducible Workflows

We incorporated Espaloma forcefield parameterization into MoSDeF tools for performing molecular dynamics simulations of organic molecules with HOOMD-Blue. We compared equilibrium morphologies predicted for perylene and poly-3-hexylthiophene (P3HT) with the ESP-UA forcefield in the present work against prior work using the OPLS-UA forcefield. We found that, after resolving the chemical ambiguities in molecular topologies, ESP-UA is similar to GAFF. We observed the clustering/melting phase behavior to be similar between ESP-UA and OPLS-UA, but the base energy unit of OPLS-UA was found to better connect to experimentally measured transition temperatures. Short-range ordering measured by radial distribution functions was found to be essentially identical between the two forcefields, and the long-range ordering measured by grazing incidence X-ray scattering was qualitatively similar, with ESP-UA matching experiments better than OPLS-UA. We concluded that Espaloma offers promise in the automated screening of molecules that are from more complex chemical spaces.

Novel α-glucosidase Inhibitors Designed as Type 2 Diabetes Drugs by QSAR, Molecular Docking and Molecular Dynamics Simulation Methods

Diabetes mellitus is a globally prevalent disease of significant concern. Alpha-glucosidase has emerged as a prominent target for the treatment of type 2 diabetes. In this study, 39 α-glucosidase inhibitors (AGIs) of tetrahydrobenzo[b]thiophene-2-ylurea derivatives to establish a stable and valid Topomer CoMFA model, with a cross-validation coefficient (q2) of 0.766 and a non-cross-validation coefficient (r2) of 0.960. Subsequently, the ZINC15 database was used to screen the fragments, based on which 13 novel inhibitor molecules with theoretically potentially high activity were designed. Molecular docking and molecular dynamics simulations to understand the binding status of the inhibitor molecules to the target proteins showed that amino acids ASP215, GLN279 and ARG442 may form hydrogen bonds with the ligands and therefore enhance the inhibitory effect of the small molecules. Additionally, MM/PBSA calculations indicate that the newly designed molecules exhibit more stable binding modes. These molecules also demonstrate favorable ADMET properties with potential as AGIs. The findings would provide valuable guidance and a theoretical foundation for the design and development of novel AGIs.

Computer-aided Drug Discovery Approaches in the Identification of Anticancer Drugs from Natural Products: A Review

Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.

Hydroxyethylamine based analog targets microtubule assembly: an in silico study for anti-cancerous drug development

Microtubules are dynamic cytoskeletal structures essential for cell architecture, cellular transport, cell motility, and cell division. Due to their dynamic nature, known as dynamic instability, microtubules can spontaneously switch between phases of growth and shortening. Disruptions in microtubule functions have been implicated in several diseases, including cancer, neurodegenerative disorders such as Alzheimer's and Parkinson's disease, and birth defects. The role of microtubules during various phases of the cell cycle, particularly in cell division, makes them attractive targets for drug development against cancer. Several successful drugs currently on the market are designed to target microtubules. However, the presence of cellular toxicity and the development of multidrug resistance necessitate the search for new microtubule-targeting drugs.Here, a library of 106 biologically active compounds were screened to identify potent microtubule assembly inhibitors. Out of all the screened compounds, the hydroxyethylamine (HEA) analogues are found to be the best hit.We identified three inhibitors, BKS3031A, BKS3045A and BKS3046A, that bind at the same site as the well-known microtubule targeting agent colchicine. These inhibitors were simulated for 100 ns with tubulin complexes, and the results indicated that they remain stable within the binding pocket of α-β tubulin complexes. In addition, we estimated the binding free energy of BKS3031A, BKS3045A and BKS3046A by using molecular mechanics generalized Born surface area (MM-GBSA) calculations, and it was found to be -32.67 ± 6.01, -21.77 ± 5.12 and - 22.92 ± 5.09 kcal/mol, respectively. Our findings suggest that these novel inhibitors have potential to bind and perturb the microtubule network, positioning them as promising microtubule-targeting agents.

In Silico Study of the Anti-MYC Potential of Lanostane-Type Triterpenes

One of the most investigated molecular targets for anticancer therapy is the proto-oncogene MYC, which is amplified and thus overexpressed in many types of cancer. Due to its structural characteristics, developing inhibitors for the target has proven to be challenging. In this study, the anti-MYC potential of lanostane-type triterpenes was investigated for the first time, using computational approaches that involved ensemble docking, prediction of structural properties and pharmacokinetic parameters, molecular dynamics (MD), and binding energy calculation using the molecular mechanics-generalized born surface area (MM-GBSA) method. The analysis of physicochemical properties, druglikeness, and pharmacokinetic parameters showed that ligands ganoderic acid E (I), ganoderlactone D (II), ganoderic acid Y (III), ganoderic acid Df (IV), lucidenic acid F (V), ganoderic acid XL4 (VI), mariesiic acid A (VII), and phellinol E (VIII) presented properties within the filter used. These eight ligands, in general, could interact with the molecular target favorably, with interaction energy values between -8.3 and -8.6 kcal mol-1. In MD, the results of RMSD, RMSF, radius of gyration, and hydrogen bonds of the complexes revealed that ligands I, IV, VI, and VII interacted satisfactorily with the protein during the simulations and assisted in its conformational and energetic stabilization. The binding energy calculation using the MM-GBSA method showed better results for the MYC-VII and MYC-I complexes (-44.98 and -41.96 kcal mol-1, respectively). These results support the hypothesis that such molecules can interact with MYC for a considerable period, which would be an essential condition for them to exert their inhibitory activity effectively.

Engineering considerations in the design of tissue specific bioink for 3D bioprinting applications

Over eight million surgical procedures are conducted annually in the United Stats to address organ failure or tissue losses. In response to this pressing need, recent medical advancements have significantly improved patient outcomes, primarily through innovative reconstructive surgeries utilizing tissue grafting techniques. Despite tremendous efforts, repairing damaged tissues remains a major clinical challenge for bioengineers and clinicians. 3D bioprinting is an additive manufacturing technique that holds significant promise for creating intricately detailed constructs of tissues, thereby bridging the gap between engineered and actual tissue constructs. In contrast to non-biological printing, 3D bioprinting introduces added intricacies, including considerations for material selection, cell types, growth, and differentiation factors. However, technical challenges arise, particularly concerning the delicate nature of living cells in bioink for tissue construction and limited knowledge about the cell fate processes in such a complex biomechanical environment. A bioink must have appropriate viscoelastic and rheological properties to mimic the native tissue microenvironment and attain desired biomechanical properties. Hence, the properties of bioink play a vital role in the success of 3D bioprinted substitutes. This review comprehensively delves into the scientific aspects of tissue-centric or tissue-specific bioinks and sheds light on the current challenges of the translation of bioinks and bioprinting.

The vascular effects of peppermint (Mentha longifolia. L) on aorta in a mouse model: an ex-vivo and computational study

The present study examined the vascular effects of peppermint or mint (Mentha longifolia L.) using an abdominal aortic rings model. Concentration-response curves for mint oil were generated after precontracting isolated mouse aorta with phenylephrine. The effect of different receptor antagonists and ion channel or enzyme inhibitors on the vasorelaxant potential of mint oil were studied. Molecular docking studies were conducted using computational techniques to investigate the potential interactions between the bioactive constituents of mint oil and key vascular targets. The tension of aortic rings, which had been contracted by phenylephrine, relaxed as a function of the concentration of mint oil (0.0002-2 mg/mL). Pretreatment of the rings with the nitric oxide synthase inhibitor (L-NAME), a nonselective β-blocker (propranolol), and a muscarinic receptor blocker (atropine) didn't show significant resistance to the vasodilatory effects of the mint oil. The vasodilatory effects of mint oil were significantly diminished when the rings were pretreated with glibenclamide, an inhibitor of ATP-sensitive K+ channels. In addition, indomethacin, a cyclooxygenase (COX) inhibitor, did influence mint oil's tension in the preparations precontracted with phenylephrine. The present findings imply that ATP-sensitive K+ channels activation, blocking of Ca2+ channels, and inhibition of COX play a role in mediating the mint oil-induced vasorelaxation. Molecular docking studies of mint oil constituents showed that β-Elemene and Aromadendrene can interact with K+ and Ca2+ channels through various hydrophobic interactions with key amino acid residues. Additional work is needed to confirm the possible beneficial application of mint oil or its constituents in regulating the vascular tone.

Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery

In the field of computational chemistry, computer models are quickly and cheaply constructed to predict toxicology hazards and results, with no need for test material or animals as these computational predictions are often based on physicochemical properties of chemical structures. Multiple methodologies are employed to support in silico assessments based on machine learning (ML) and deep learning (DL). This review introduces the development of computational toxicology, focusing on ML and DL and emphasizing their importance in the field of toxicology. A fine balance between target potency, selectivity, absorption, distribution, metabolism, excretion, toxicity (ADMET) and clinical safety properties should be achieved to discover a potential new drug. It is advantageous to perform virtual predictions as early as possible in drug development processes, even before a molecule is synthesized. Currently, there are numerous commercially available and free web-based programs for toxicity prediction, which can be used to construct various predictive models. The key features of the QSAR method are also outlined, and the selection of appropriate physicochemical descriptors is a prerequisite for robust predictions. In addition, examples of open-source tools applied to toxicity prediction are included, as well as examples of the application of different computational methods for the prediction of toxicity in drug design and environmental toxicology.

Discovery of novel Akt1 inhibitors by an ensemble-based virtual screening method, molecular dynamics simulation, and in vitro biological activity testing

Akt1, as an important member of the Akt family, plays a controlled role in cancer cell growth and survival. Inhibition of Akt1 activity can promote cancer cell apoptosis and inhibit tumor growth. Therefore, in this investigation, a multilayer virtual screening approach, including receptor-ligand interaction-based pharmacophore, 3D-QSAR, molecular docking, and deep learning methods, was utilized to construct a virtual screening platform for Akt1 inhibitors. 17 representative compounds with different scaffolds were identified as potential Akt1 inhibitors from three databases. Among these 17 compounds, the Hit9 exhibited the best inhibitory activity against Akt1 with inhibition rate of 33.08% at concentration of 1 μM. The molecular dynamics simulations revealed that Hit9 and Akt1 could form a compact and stable complex. Moreover, Hit9 interacted with some key residues by hydrophobic, electrostatic, and hydrogen bonding interactions and induced substantial conformation changes in the hinge region of the Akt1 active site. The average binding free energies for the Akt1-CQU, Akt1-Ipatasertib, and Akt1-Hit9 systems were - 34.44, - 63.37, and - 39.14 kJ mol-1, respectively. In summary, the results obtained in this investigation suggested that Hit9 with novel scaffold may be a promising lead compound for developing new Akt1 inhibitor for treatment of various cancers with Akt1 overexpressed.

Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities

Molecular docking is a widely used technique in drug discovery to predict the binding mode of a given ligand to its target. However, the identification of the near-native binding pose in docking experiments still represents a challenging task as the scoring functions currently employed by docking programs are parametrized to predict the binding affinity, and, therefore, they often fail to correctly identify the ligand native binding conformation. Selecting the correct binding mode is crucial to obtaining meaningful results and to conveniently optimizing new hit compounds. Deep learning (DL) algorithms have been an area of a growing interest in this sense for their capability to extract the relevant information directly from the protein-ligand structure. Our review aims to present the recent advances regarding the development of DL-based pose selection approaches, discussing limitations and possible future directions. Moreover, a comparison between the performances of some classical scoring functions and DL-based methods concerning their ability to select the correct binding mode is reported. In this regard, two novel DL-based pose selectors developed by us are presented.

Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR

Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent in vitro assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.

Comprehensive applications of the artificial intelligence technology in new drug research and development

Purpose

Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods

Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results

In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion

Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

MicroRNA Guided In Silico Drug Repositioning for Malaria

BACKGROUND

The rise in Plasmodium resistant strains, decreasing susceptibility to first-line combination therapies, and inadequate efficacy shown by vaccines developed to date necessitate innovative approaches to combat malaria. Drug repurposing refers to finding newer indications for existing medications that provide significant advantages over de novo drug discovery, leading to rapid treatment options. Growing evidence suggests that drugs could regulate the expression of disease-associated microRNAs (miRNAs), implying the potential of miRNAs as attractive targets of therapy for several diseases.

METHODS

We aimed to computationally predict drug-disease relationships through miRNAs for the potential repurposing of the drugs as antimalarials. To achieve this, we created a model that combines experimentally validated miRNA-drug interactions and miRNA-disease correlations, assuming that drugs will be linked to disease if they share significant miRNAs. The first step involved constructing a network of drug-drug interactions using curated drug-miRNA relations from the Pharmaco-miR and SM2miR databases. Additionally, the drug-disease relations were acquired from the comparative toxicogenomics database (CTD), and the random walk with restart (RWR) algorithm was applied to the interaction network to anticipate newer drug indications. Further, experimentally verified miRNA-disease associations were procured from the human microRNA disease database (HMDD), followed by an evaluation of the model's performance by examining case studies retrieved from the literature.

RESULTS

Topological network analysis revealed that beta-adrenergic drugs in the network that are closely linked may have a tendency to be used as antimalarials. Case studies retrieved from the literature demonstrated acceptable model performance. A few of the predicted drugs, namely, propranolol, metoprolol, epinephrine, and atenolol, have been evaluated for their association with malaria, thereby indicating the adequacy of our model and offering experimental leads for alternative drugs.

CONCLUSION

The study puts forth a computational model for forecasting potential connections between beta-adrenergic receptor targeting drugs and malaria to suggest potential for future drug repurposing. This takes into account the concept of commonly associated miRNA partners and providing a mechanistic basis for targeting diseases, elucidating the implication of miRNAs in novel drug-disease relations.

Natural Compounds Targeting Thymic Stromal Lymphopoietin (TSLP): A Promising Therapeutic Strategy for Atopic Dermatitis

Atopic dermatitis (AD) is a chronic inflammatory skin disease with rising prevalence, marked by eczematous lesions, itching, and a weakened skin barrier often tied to filaggrin gene mutations. This breakdown allows allergen and microbe entry, with thymic stromal lymphopoietin (TSLP) playing a crucial role by activating immune pathways that amplify the allergic response. TSLP's central role in AD pathogenesis makes it a promising therapeutic target. Consequently, in this study, we used the virtual drug screening, molecular dynamics simulation, and binding free energies calculation approaches to explore the African Natural Product Database against the TSLP protein. The molecular screening identified four compounds with high docking scores, namely SA_0090 (-7.37), EA_0131 (-7.10), NA_0018 (-7.03), and WA_0006 (-6.99 kcal/mol). Furthermore, the KD analysis showed a strong binding affinity of these compounds with TSLP, with values of -5.36, -5.36, -5.34, and -5.32 kcal/mol, respectively. Moreover, the strong binding affinity of these compounds was further validated by molecular dynamic simulation analysis, which revealed that the WA_0006-TSLP is the most stable complex with the lowest average RMSD. However, the total binding free energies were -40.5602, -41.0967, -27.3293, and -51.3496 kcal/mol, respectively, showing the strong interaction between the selected compounds and TSLP. Likewise, these compounds showed excellent pharmacokinetics characteristics. In conclusion, this integrative approach provides a foundation for the development of safe and effective treatments for AD, potentially offering relief to millions of patients worldwide.

Molecular docking, ADME properties and synthesis of thiophene sulfonamide derivatives

This study investigates the drug-like properties of target molecules containing thiophene sulfonamide groups (7a-7s) using computational molecular docking techniques. The binding interactions of these derivatives were assessed using protein 2NSD (Enoyl acyl carrier protein reductase InhA, complexed with N-(4-methylbenzoyl)-4-benzylpiperidine, PDB DOI: 10.2210/pdb2NSD/pdb) as the receptor. Molecular docking results revealed notable docking scores for all compounds, ranging from -6 to -12 kcal/mol. Compounds 7e, 7i, and 7f, in particular, demonstrated impressive glide scores (>11 kcal/mol) and were selected for further analysis through molecular dynamics simulations, which provided deeper insights into their dynamic behavior and stability. The drug-like properties of these molecules were evaluated based on Lipinski's Rule of Five and ADME (Absorption, Distribution, Metabolism, and Excretion) criteria and compared with known drugs. Additionally, we synthesized these target molecules (7a-7s) using Suzuki-Miyaura coupling with a nickel catalyst replacing palladium. The chemical structures of the synthesized compounds were confirmed through elemental analysis, LC-MS,1H-NMR, and 13C-NMR spectroscopy.

Model Organoids: Integrated Frameworks for the Next Frontier of Healthcare Advancements

The morphogenetic events leading to tissue formation can be recapitulated using organoids, which allows studying new diseases and modelling personalized medicines. In this review, culture systems comparable to human organs are presented, these organoids are created from pluripotent stem cells or adult stem cells. The efficient and reproducible models of human tissues are discussed for biobanking, precision medicine and basic research. Mechanisms used by these model systems with an overview of models from human cells are also covered. As human physiology is different from animals, culture conditions and tissue limits often become challenging. Organoids offer novel approaches for such cases with rapid screening, transplantation studies and in immunotherapy. Discrepancies with large datasets can be handled with an integrated framework of artificial intelligence or AI and machine learning. An attempt has been made to show the improved effectiveness, simplified iterations, along with image analysis that are possible from this synergy. AI-assisted organoids have the potential to transform healthcare by improving disease understanding and accelerating clinical decision-making through personalized and precision medicine.

Screening for Potential Antiviral Compounds from Cyanobacterial Secondary Metabolites Using Machine Learning

The secondary metabolites of seawater and freshwater blue-green algae are a rich natural product pool containing diverse compounds with various functions, including antiviral compounds; however, high-efficiency methods to screen such compounds are lacking. Advanced virtual screening techniques can significantly reduce the time and cost of novel antiviral drug identification. In this study, we used a cyanobacterial secondary metabolite library as an example and trained three models to identify compounds with potential antiviral activity using a machine learning method based on message-passing neural networks. Using this method, 364 potential antiviral compounds were screened from >2000 cyanobacterial secondary metabolites, with amides predominating (area under the receiver operating characteristic curve value: 0.98). To verify the actual effectiveness of the candidate antiviral compounds, HIV virus reverse transcriptase (HIV-1 RT) was selected as a target to evaluate their antiviral potential. Molecular docking experiments demonstrated that candidate compounds, including kororamide, mollamide E, nostopeptolide A3, anachelin-H, and kasumigamide, produced relatively robust non-covalent bonding interactions with the RNase H active site on HIV-1 RT, supporting the effectiveness of the proposed screening model. Our data demonstrate that artificial intelligence-based screening methods are effective tools for mining potential antiviral compounds, which can facilitate the exploration of various natural product libraries.

A comprehensive study of experimental and theoretical characterization and in silico toxicity analysis of new molecules

In this study, for the first time in the literature, a 2-(3-methoxyphenylamino)-2-oxoethyl acrylate (3MPAEA) molecule was synthesized in two steps, and a 2-chloro-N-(3-methoxyphenyl)acetamide (m-acetamide) was obtained in the first step. Experimental results were obtained using FTIR, 1H, and 13C NMR spectroscopy methods for m-acetamide and 3MPAEA compounds created in the laboratory environment and compared with theoretical results. Band gap (BG) energy, chemical hardness, electronegativity, chemical potential, and electrophilicity index were calculated. With vibration spectroscopic analysis, atom-molecule vibrations of the theoretical and experimental peaks of the spectrum were observed. The locations of C and H atoms were determined by nuclear magnetic resonance spectroscopy. The green, blue, and red regions of the potential energy map (MEP) map were examined. Some observed that the energy thermal, heat capacity, and entropy graphs increased in direct proportion to increasing the temperature in Kelvin, which is known as thermochemistry. The changes in the rotation, translation, and vibration of the molecule as its temperature increased were examined. When the thermochemistry surface map was examined, some observed that the temperature was high in the middle binding site of the molecules. Covalent interactions were graphed using the non-covalent interactions (NCIs) calculation method. In silico toxicity studies were carried out for m-acetamide and 3MPAEA molecules: fathead minnow LC50 (96 h), Daphnia magna LC50 (48 h), Tetrahymena pyriformis IGC50 (48 h), oral rat LD50, water solubility, bioconcentration factor, developmental toxicity, mutation, normal boiling point, flash point, melting point, density, thermal conductivity, viscosity, vapor pressure, etc. parameters were investigated.

MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction

Accurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization. To address this, we propose a multi-perspective feature fusion model, MFF-DTA, which integrates chemical structure, biological sequence, and other data to comprehensively capture drug-target affinity features. The MFF-DTA model incorporates multiple feature learning components, each of which is capable of extracting drug molecular features and protein target information, respectively. These components are able to obtain key information from both global and local perspectives. Then, these features from different perspectives are efficiently combined using specific splicing strategies to create a comprehensive representation. Finally, the model uses the fused features to predict drug-target affinity. Comparative experiments show that MFF-DTA performs optimally on the Davis and KIBA data sets. Ablation experiments demonstrate that removing specific components results in the loss of unique information, thus confirming the effectiveness of the MFF-DTA design. Improvements in DTA prediction methods will decrease costs and time in drug development, enhancing industry efficiency and ultimately benefiting patients.

Design, Synthesis, Physicochemical Properties, and Biological Activity of Thymidine Compounds Attached to 5,8-Quinolinedione Derivatives as Potent DT-Diaphorase Substrates

After heart disease, cancer is the second-leading cause of death worldwide. The most effective method of cancer treatment is target therapy. One of the potential goals of therapy could be DT-diaphorase, which reduces quinone moiety to hydroquinone, and reactive oxygen species are create as a byproduct. The obtaining of hybrid compounds containing the quinone moiety and other bioactive compounds leads to new derivatives which can activate DT-diaphorase. The aim of this research was the synthesis and characterization of new hybrids of 5,8-quinolinedione with thymidine derivatives. The analysis of the physicochemical properties shows a strong relationship between the structure and properties of the tested compounds. The enzymatic assay shows that hybrids are good substrates of NQO1 protein. The analysis of the structure-activity relationship shows that the localization of nitrogen atoms influences the enzymatic conversion rate. The analysis was supplemented by a molecular docking study. Comparing the results of the enzymatic assay and the molecular docking presents a strong correlation between the enzymatic conversion rate and the scoring value.

Acceleration of chondrogenic differentiation utilizing biphasic core-shell alginate sulfate electrospun nanofibrous scaffold

Engineering new biomedical materials with tailored physicochemical, mechanical and biological virtues in order to differentiate stem cells into chondrocytes for cartilage regeneration has garnered much scientific interest. In this study, core/shell nanofibrous scaffold based on poly(ɛ-caprolactone) (PCL) as a core material and alginate sulfate (AlgS)-poly(vinyl alcohol) (PVA) blend as shell materials (AlgS-PVA/PCL) was fabricated by emulsion electrospinning. In this vein, the influence of AlgS to PVA ratio (30:70, 50:50), organic to aqueous phase ratio (1:2, 1:3 and 1:5) and acid concentration (0, 10, 20, 30, 40 and 50 %) on nanofibers morphology were investigated. SEM images depicted that AlgS to PVA ratio of 30:70 and 50:50, organic to aqueous phase ratio of 1:3 and 1:5 and acid concentration of 30 % led to uniform, bead-free fibrous mats. AlgS-PVA/PCL scaffolds with AlgS to PVA ratio of 30:70 and organic to aqueous phase ratio of 1:3, showed admirable mechanical features, high porosity (>90 %) with desirable swelling ratio in wet condition. In vitro assays indicated that the AlgS-PVA/PCL scaffold surface had desirable interaction with stem cells and promotes cells attachment, proliferation and differentiation. Thus, we envision that this salient structure could be an intriguing construction as a cartilage tissue-engineered scaffold.

Predicting of novel homoserine dehydrogenase inhibitors against Paracoccidioides brasiliensis: integrating in silico and in vitro approaches

Aim: To search for potential inhibitors to homoserine dehydrogenase (HSD) in Paracoccidioides brasiliensis the causative agent of paracoccidioidomycosis, an infection with a high mortality rate in Brazil.Materials & methods: The enzyme was modeled and used in the virtual screening of the compounds. The library was first screened by the Autodock, in which 66 molecules were better ranked than substrate, and then, also evaluated by the Molegro and Gold programs.Results: The HS23 and HS87 molecules were selected in common by the three programs, and ADME/Tox evaluation indicates they are not toxic. The molecular dynamics of PbHSD bonded to ligands showed stable complexes until 50 ns. To validate the results, compounds were purchased for assays of minimum inhibitory concentration (MIC), minimum fungicidal concentration (MFC), synergic profile with Amphotericin B (AmB) and cytotoxicity. The two molecules presented MIC of 32 μg/ml and MFC of 64 μg/ml against the P. brasiliensis (strain Pb18). They also showed synergistic activity with AmB and a lack of toxicity against Hela and Vero cell lines.Conclusion: These results suggest that the HS23 and HS87 are promising candidates as PbHSD inhibitors and may be used as hits for the development of new drugs against paracoccidioidomycosis.

Discovery of novel and highly potential inhibitors of glycogen synthase kinase 3-beta (GSK-3β) through structure-based pharmacophore modeling, virtual computational screening, docking and in silico ADMET analysis

The protein Glycogen Synthase Kinase 3-Beta (GSK-3β), is a promising therapeutic target for treating various diseases such as neurodegenerative disorders, diabetes, inflammation and cancer. This study aims to investigate the potential of compounds targeting inflammation or carbohydrate metabolism to selectively inhibit GSK3β by binding to its ATP site. To achieve this goal, we filtered a database of 49367 molecules involved in carbohydrate metabolism or targeting inflammation using various computational analyses, including pharmacophore modeling, molecular docking, dynamic simulation, prime MM-GBSA calculation, and in silico ADME studies. We generated a pharmacophore model (hypo S: AADDHRR) using two different crystallographic complexes of GSK3β and evaluated the model's performance in identifying hits using various parameters, including EF, GH, ROC, AUC and BEDROC. Subsequently, we performed various dockings (HTVS, SP, XP and IFD) for the retrieved hits and found that, 5 out of the top 10 ranked compounds had the scaffold of pyrazolidine 3,5-dione, which has never been reported to inhibit kinases. We also conducted ADMET studies to and concluded that compound N6 exhibited the best pharmacokinetic profile passing the blood-brain barrier, possessing high lipophilicity and a high coefficient of skin permeability in the intestines, along with good bioavailability and low toxicity risk assessment. Dynamic simulation were also performed indicating that compounds N6 derived from pyrazolidine 3,5-dione demonstrated better binding potential for GSK3β during the simulation period. Therefore, we propose that compounds derived from pyrazolidine-3,5-dione, which modulate the activity of lysosomal alpha-glucosidase could serve as a novel scaffold for the selective inhibition of GSK-3β.Communicated by Ramaswamy H. Sarma.

Integrating Computational Design and Experimental Approaches for Next-Generation Biologics

Therapeutic protein engineering has revolutionized medicine by enabling the development of highly specific and potent treatments for a wide range of diseases. This review examines recent advances in computational and experimental approaches for engineering improved protein therapeutics. Key areas of focus include antibody engineering, enzyme replacement therapies, and cytokine-based drugs. Computational methods like structure-based design, machine learning integration, and protein language models have dramatically enhanced our ability to predict protein properties and guide engineering efforts. Experimental techniques such as directed evolution and rational design approaches continue to evolve, with high-throughput methods accelerating the discovery process. Applications of these methods have led to breakthroughs in affinity maturation, bispecific antibodies, enzyme stability enhancement, and the development of conditionally active cytokines. Emerging approaches like intracellular protein delivery, stimulus-responsive proteins, and de novo designed therapeutic proteins offer exciting new possibilities. However, challenges remain in predicting in vivo behavior, scalable manufacturing, immunogenicity mitigation, and targeted delivery. Addressing these challenges will require continued integration of computational and experimental methods, as well as a deeper understanding of protein behavior in complex physiological environments. As the field advances, we can anticipate increasingly sophisticated and effective protein therapeutics for treating human diseases.

In silico analysis of potential inhibitors for breast cancer targeting 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) catalyses

Breast cancer (BC) is still one of the major issues in world health, especially for women, which necessitates innovative therapeutic strategies. In this study, we investigated the efficacy of retinoic acid derivatives as inhibitors of 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1), which plays a crucial role in the biosynthesis and metabolism of oestrogen and thereby influences the progression of BC and, the main objective of this investigation is to identify the possible drug candidate against BC through computational drug design approach including PASS prediction, molecular docking, ADMET profiling, molecular dynamics simulations (MD) and density functional theory (DFT) calculations. The result has reported that total eight derivatives with high binding affinity and promising pharmacokinetic properties among 115 derivatives. In particular, ligands 04 and 07 exhibited a higher binding affinity with values of -9.9 kcal/mol and -9.1 kcal/mol, respectively, than the standard drug epirubicin hydrochloride, which had a binding affinity of -8.2 kcal/mol. The stability of the ligand-protein complexes was further confirmed by MD simulations over a 100-ns trajectory, which included assessments of hydrogen bonds, root mean square deviation (RMSD), root mean square Fluctuation (RMSF), dynamic cross-correlation matric (DCCM) and principal component analysis. The study emphasizes the need for experimental validation to confirm the therapeutic utility of these compounds. This study enhances the computational search for new BC drugs and establishes a solid foundation for subsequent experimental and clinical research.

Synthesis and characterization of gold(I) thiolate derivatives and bimetallic complexes for HIV inhibition

Introduction: The human immunodeficiency virus (HIV) remains a significant global health concern, with a reported high infection rate of 38.4 million cases globally; an estimated 2 million new infections and approximately 700,000 HIV/AIDS-related deaths were reported in 2021. Despite the advent of anti-retroviral therapy (ART), HIV/AIDS persists as a chronic disease. To combat this, several studies focus on developing inhibitors targeting various stages of the HIV infection cycle, including HIV-1 protease. This study aims to synthesize and characterize novel glyco diphenylphosphino metal complexes with potential HIV inhibitory properties. Method: A series of new gold(I) thiolate derivatives and three bimetallic complexes, incorporating amino phosphines and thiocarbohydrate as auxiliary ligands, were synthesized using procedures described by Jiang, et al. (2009) and Coetzee et al. (2007). Structural elucidation and purity assessment of the synthesized compounds (1-11) were conducted using micro-analysis, NMR, and infrared spectrometry. Results and Discussion: Using molecular modeling techniques, three of the metal complexes were identified as potential HIV protease inhibitors, exhibiting strong binding affinity interactions with binding pocket residues. These inhibitors demonstrated an ability to inhibit the flexibility of the flap regions of the HIV protease, similar to the known HIV protease inhibitor, darunavir. This study sheds light on the promising avenues for the development of novel therapeutic agents against HIV/AIDS.

A Perspective on Interdicting in Protein Misfolding for Therapeutic Drug Design: Modulating the Formation of Nonlocal Contacts in α-Synuclein as a Strategy against Parkinson's Disease

In recent work we proposed that interdiction in the earliest contact-formation events along the folding pathway of key viral proteins could provide a novel avenue for therapeutic drug design. In this Perspective we explore the potential applicability of the protein folding interdiction strategy in the realm of neurodegenerative diseases with a specific focus on synucleinopathies. In order to fulfill this goal we review the interdiction proposal and its practical challenges, and we present new results concerning design strategies for possible peptide drugs that could be useful in preventing α-synuclein aggregation.

Design of vilazodone-donepezil chimeric derivatives as acetylcholinesterase inhibitors by QSAR, molecular docking and molecular dynamics simulations

Alzheimer's disease (AD) is a disease that affects the cognitive abilities of older adults, and it is one of the biggest global medical challenges of the 21st century. Acetylcholinesterase (AChE) can increase acetylcholine concentrations and improve cognitive function in patients, and is a potential target to develop small molecule inhibitors for the treatment of Alzheimer's disease (AD). In this study, 29 vilazodone-donepezil chimeric derivatives are systematically studied using 3D-QSAR modeling, and a robust and reliable Topomer CoMFA model was obtained with: q2 = 0.720, r2 = 0.991, F = 287.234, N = 6, and SEE = 0.098. Based on the established model and combined with the ZINC20 database, 33 new compounds with ideal inhibitory activity are successfully designed. Molecular docking and ADMET property prediction also show that these newly designed compounds have a good binding ability to the target protein and can meet the medicinal conditions. Subsequently, four new compounds with good comprehensive ability are selected for molecular dynamics simulation, and the simulation results confirm that the newly designed compounds have a certain degree of reliability and stability. This study provides guidance for vilazodone-donepezil chimeric derivatives as a potential AChE inhibitor and has certain theoretical value.

POxload: Machine Learning Estimates Drug Loadings of Polymeric Micelles

Block copolymers, composed of poly(2-oxazoline)s and poly(2-oxazine)s, can serve as drug delivery systems; they form micelles that carry poorly water-soluble drugs. Many recent studies have investigated the effects of structural changes of the polymer and the hydrophobic cargo on drug loading. In this work, we combine these data to establish an extended formulation database. Different molecular properties and fingerprints are tested for their applicability to serve as formulation-specific mixture descriptors. A variety of classification and regression models are built for different descriptor subsets and thresholds of loading efficiency and loading capacity, with the best models achieving overall good statistics for both cross- and external validation (balanced accuracies of 0.8). Subsequently, important features are dissected for interpretation, and the DrugBank is screened for potential therapeutic use cases where these polymers could be used to develop novel formulations of hydrophobic drugs. The most promising models are provided as an open-source software tool for other researchers to test the applicability of these delivery systems for potential new drug candidates.

Pharmaceutical Potential of Remedial Plants and Helminths for Treating Inflammatory Bowel Disease

Research is increasingly revealing that inflammation significantly contributes to various diseases, particularly inflammatory bowel disease (IBD). IBD is a major medical challenge due to its chronic nature, affecting at least one in a thousand individuals in many Western countries, with rising incidence in developing nations. Historically, indigenous people have used natural products to treat ailments, including IBD. Ethnobotanically guided studies have shown that plant-derived extracts and compounds effectively modulate immune responses and reduce inflammation. Similarly, helminths and their products offer unique mechanisms to modulate host immunity and alleviate inflammatory responses. This review explored the pharmaceutical potential of Aboriginal remedial plants and helminths for treating IBD, emphasizing recent advances in discovering anti-inflammatory small-molecule drug leads. The literature from Scopus, MEDLINE Ovid, PubMed, Google Scholar, and Web of Science was retrieved using keywords such as natural product, small molecule, cytokines, remedial plants, and helminths. This review identified 55 important Aboriginal medicinal plants and 9 helminth species that have been studied for their anti-inflammatory properties using animal models and in vitro cell assays. For example, curcumin, berberine, and triptolide, which have been isolated from plants; and the excretory-secretory products and their protein, which have been collected from helminths, have demonstrated anti-inflammatory activity with lower toxicity and fewer side effects. High-throughput screening, molecular docking, artificial intelligence, and machine learning have been engaged in compound identification, while clustered regularly interspaced short palindromic repeats (CRISPR) gene editing and RNA sequencing have been employed to understand molecular interactions and regulations. While there is potential for pharmaceutical application of Aboriginal medicinal plants and gastrointestinal parasites in treating IBD, there is an urgent need to qualify these plant and helminth therapies through reproducible clinical and mechanistic studies.

One-Step Synthesis, Crystallography, and Acute Toxicity of Two Boron-Carbohydrate Adducts That Induce Sedation in Mice

Boronic acids form diester bonds with cis-hydroxyl groups in carbohydrates. The formation of these adducts could impair the physical and chemical properties of precursors, even their biological activity. Two carbohydrate derivatives from d-fructose and d-arabinose and phenylboronic acid were synthesized in a straightforward one-step procedure and chemically characterized via spectroscopy and X-ray diffraction crystallography. Additionally, an acute toxicity test was performed to determine their lethal dose 50 (LD50) values by using Lorke's method. Analytical chemistry assays confirmed the formation of adducts by the generation of diester bonds with the β-d-pyranose of carbohydrates, including signals corresponding to the formation of new bonds, such as the stretching of B-O bonds. NMR spectra yielded information about the stereoselectivity in the synthesis reaction: Just one signal was found in the range for the anomeric carbon in the 13C NMR spectra of both adducts. The acute toxicity tests showed that the LD50 value for both compounds was 1265 mg/kg, while the effective dose 50 (ED50) for sedation was 531 mg/kg. However, differences were found in the onset and lapse of sedation. For example, the arabinose derivative induced sedation for more than 48 h at 600 mg/kg, while the fructose derivative induced sedation for less than 6 h at the same dose without the death of the mice. Thus, we report for the first time two boron-containing carbohydrate derivatives inducing sedation after intraperitoneal administration. They are bioactive and highly safe agents. Further biological evaluation is desirable to explore their medical applications.

Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism

MOTIVATION

Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models.

RESULTS

In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism.

AVAILABILITY AND IMPLEMENTATION

https://github.com/XuLew/MIDTI.

DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph

Knowledge graph intent graph attention mechanism Predicting drug-target interactions (DTIs) plays a crucial role in drug discovery and drug development. Considering the high cost and risk of biological experiments, developing computational approaches to explore the interactions between drugs and targets can effectively reduce the time and cost of drug development. Recently, many methods have made significant progress in predicting DTIs. However, existing approaches still suffer from the high sparsity of DTI datasets and the cold start problem. In this paper, we develop a new model to predict drug-target interactions via a knowledge graph and intent graph named DTKGIN. Our method can effectively capture biological environment information for targets and drugs by mining their associated relations in the knowledge graph and considering drug-target interactions at a fine-grained level in the intent graph. DTKGIN learns the representation of drugs and targets from the knowledge graph and the intent graph. Then the probabilities of interactions between drugs and targets are obtained through the inner product of the representation of drugs and targets. Experimental results show that our proposed method outperforms other state-of-the-art methods in 10-fold cross-validation, especially in cold-start experimental settings. Furthermore, the case studies demonstrate the effectiveness of DTKGIN in predicting potential drug-target interactions. The code is available on GitHub: https://github.com/Royluoyi123/DTKGIN.

PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes

In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 MPro, confirming the robustness of PharmaCore.

Screening approach by a combination of computational and in vitro experiments: identification of fluvastatin sodium as a potential SIRT2 inhibitor

BACKGROUND

Sirtuins (SIRTs) are NAD+-dependent deacetylases that play various roles in numerous pathophysiological processes, holding promise as therapeutic targets worthy of further investigation. Among them, the SIRT2 subtype is closely associated with tumorigenesis and malignancies. Dysregulation of SIRT2 activation can regulate the expression levels of related genes in cancer cells, leading to tumor occurrence and metastasis.

METHODS

In this study, we used computer simulations to screen for novel SIRT2 inhibitors from the FDA database, based on which 10 compounds with high docking scores and good interactions were selected for in vitro anti-pancreatic cancer metastasis testing and enzyme binding inhibition experiments. The results showed that fluvastatin sodium may possess inhibitory activity against SIRT2. Subsequently, fluvastatin sodium was subjected to molecular docking experiments with various SIRT isoforms, and the combined results from Western blotting experiments indicated its potential as a SIRT2 inhibitor. Next, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were performed, revealing the binding mode of fluvastatin sodium at the SIRT2 active site, further validating the stability and interaction of the ligand-protein complex under physiological conditions.

RESULTS

Overall, this study provides a systematic virtual screening workflow for the discovery of SIRT2 activity inhibitors, identifies the potential inhibitory effect of fluvastatin sodium as a lead compound on SIRT2, and opens up a new direction for developing highly active and selectively targeted SIRT2 inhibitors.

Subtractive genomics study for the identification of therapeutic targets against Cronobacter sakazakii: A threat to infants

Cronobacter sakazakii is an opportunistic pathogen that has been associated with severe infection in neonates such as necrotizing enterocolitis (NEC), neonatal meningitis, and bacteremia. This pathogen can survive in a relatively dry environment, especially in powdered infant formula (PIF). Unfortunately, conventional drugs that were once effective against C. sakazakii are gradually losing their efficacy due to rising antibiotic resistance. In this study, a subtractive genomic approach was followed in order to identify potential therapeutic targets in the pathogen. The whole proteome of the pathogen was filtered through a step-by-step process, which involved removing paralogous proteins, human homologs, sequences that are less essential for survival, proteins with shared metabolic pathways, and proteins that are located in cells other than the cytoplasmic membrane. As a result, nine novel drug targets were identified. Further, the analysis also unveiled that the FDA-approved drug Terbinafine can be repurposed against the Glutathione/l-cysteine transport system ATP-binding/permease protein CydC of C. sakazakii. Moreover, molecular docking and dynamics studies of Terbinafine and CydC suggested that this drug can be used to treat C. sakazakii infection in neonates. However, for clinical purposes further in vitro and in vivo studies are necessary.

An Overview of SARS-CoV-2 Potential Targets, Inhibitors, and Computational Insights to Enrich the Promising Treatment Strategies

The rapid spread of the SARS-CoV-2 virus has emphasized the urgent need for effective therapies to combat COVID-19. Investigating the potential targets, inhibitors, and in silico approaches pertinent to COVID-19 are of utmost need to develop novel therapeutic agents and reprofiling of existing FDA-approved drugs. This article reviews the viral enzymes and their counter receptors involved in the entry of SARS-CoV-2 into host cells, replication of genomic RNA, and controlling the host cell physiology. In addition, the study provides an overview of the computational techniques such as docking simulations, molecular dynamics, QSAR modeling, and homology modeling that have been used to find the FDA-approved drugs and other inhibitors against SARS-CoV-2. Furthermore, a comprehensive overview of virus-based and host-based druggable targets from a structural point of view, together with the reported therapeutic compounds against SARS-CoV-2 have also been presented. The current study offers future perspectives for research in the field of network pharmacology investigating the large unexplored molecular libraries. Overall, the present in-depth review aims to expedite the process of identifying and repurposing drugs for researchers involved in the field of COVID-19 drug discovery.

The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods

Histone deacetylases (HDACs) are crucial in gene transcription, removing acetyl groups from histones. They also influence the deacetylation of non-histone proteins, contributing to the regulation of various biological processes. Thus, HDACs play pivotal roles in various diseases, including cancer, neurodegenerative disorders, and inflammatory conditions, highlighting their potential as therapeutic targets. This paper reviews the structure and function of the four classes of human HDACs. While four HDAC inhibitors are currently available for treating hematological malignancies, numerous others are undergoing clinical trials. However, their non-selective toxicity necessitates ongoing research into safer and more efficient class-selective or isoform-selective inhibitors. Computational techniques have greatly facilitated the discovery of HDAC inhibitors that achieve the desired potency and selectivity. These techniques encompass ligand-based strategies such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure–activity relationships (3D-QSAR), and structure-based virtual screening (molecular docking). Additionally, advancements in molecular dynamics simulations, along with Poisson–Boltzmann/molecular mechanics generalized Born surface area (PB/MM-GBSA) methods, have enhanced the accuracy of predicting ligand binding affinity. In this review, we delve into the ways in which these methods have contributed to designing and identifying HDAC inhibitors.

TransGEM: a molecule generation model based on Transformer with gene expression data

MOTIVATION

It is difficult to generate new molecules with desirable bioactivity through ligand-based de novo drug design, and receptor-based de novo drug design is constrained by disease target information availability. The combination of artificial intelligence and phenotype-based de novo drug design can generate new bioactive molecules, independent from disease target information. Gene expression profiles can be used to characterize biological phenotypes. The Transformer model can be utilized to capture the associations between gene expression profiles and molecular structures due to its remarkable ability in processing contextual information.

RESULTS

We propose TransGEM (Transformer-based model from gene expression to molecules), which is a phenotype-based de novo drug design model. A specialized gene expression encoder is used to embed gene expression difference values between diseased cell lines and their corresponding normal tissue cells into TransGEM model. The results demonstrate that the TransGEM model can generate molecules with desirable evaluation metrics and property distributions. Case studies illustrate that TransGEM model can generate structurally novel molecules with good binding affinity to disease target proteins. The majority of genes with high attention scores obtained from TransGEM model are associated with the onset of the disease, indicating the potential of these genes as disease targets. Therefore, this study provides a new paradigm for de novo drug design, and it will promote phenotype-based drug discovery.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/hzauzqy/TransGEM.

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction

Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.

A Multi-Drug Concentration Gradient Mixing Chip: A Novel Platform for High-Throughput Drug Combination Screening

Combinatorial drug therapy has emerged as a critically important strategy in medical research and patient treatment and involves the use of multiple drugs in concert to achieve a synergistic effect. This approach can enhance therapeutic efficacy while simultaneously mitigating adverse side effects. However, the process of identifying optimal drug combinations, including their compositions and dosages, is often a complex, costly, and time-intensive endeavor. To surmount these hurdles, we propose a novel microfluidic device capable of simultaneously generating multiple drug concentration gradients across an interlinked array of culture chambers. This innovative setup allows for the real-time monitoring of live cell responses. With minimal effort, researchers can now explore the concentration-dependent effects of single-agent and combination drug therapies. Taking neural stem cells (NSCs) as a case study, we examined the impacts of various growth factors-epithelial growth factor (EGF), platelet-derived growth factor (PDGF), and fibroblast growth factor (FGF)-on the differentiation of NSCs. Our findings indicate that an overdose of any single growth factor leads to an upsurge in the proportion of differentiated NSCs. Interestingly, the regulatory effects of these growth factors can be modulated by the introduction of additional growth factors, whether singly or in combination. Notably, a reduced concentration of these additional factors resulted in a decreased number of differentiated NSCs. Our results affirm that the successful application of this microfluidic device for the generation of multi-drug concentration gradients has substantial potential to revolutionize drug combination screening. This advancement promises to streamline the process and accelerate the discovery of effective therapeutic drug combinations.