In silico methods and tools for drug discovery

Classification and regression machine learning models for predicting mixed toxicity of carbamazepine and its transformation products

Carbamazepine (CBZ) and its transformation products (TPs) often occur in aquatic environments in the form of mixtures, posing potential risks to ecosystems. However, establishing standardized protocols for synthesizing, isolating, and acquiring these TPs has been challenging, leading to difficulty in obtaining toxicity data. Accurately assessing the risks associated with mixed toxicity of TPs was therefore critical. The research evaluated the binary toxicity of CBZ and its TPs using luminescent bacteria. The mixed toxicity of TPs showed simply additive effects. In order to comprehend the connection between the toxicity of TPs and CBZ, we labeled TPs with toxicity higher than CBZ as 'high risk' and TPs with lower toxicity as 'low risk.' Subsequently, we developed and tested seven classification models and five regression models. The classification models were of guiding significance for the management of toxicity risk. In contrast, the regression models are capable of addressing the lack of mixed toxicity data. However, these regression models show limitations on the experimental datasets, and their performance is unsatisfactory. Considering the challenges in obtaining toxicity data of transformed products, we addressed this limitation by enhancing the dataset using generative adversarial networks (GANs), thereby improving the generalization capability about the regression models. This study highlighted the potential of quantitative structure-activity relationship (QSAR) models, which are based on based on machine learning for predicting the mixed toxicity of TPs, providing a solution for toxicity assessment without chemical standards.

A Review of In Silico and In Vitro Approaches in the Fight Against Carbapenem-Resistant Enterobacterales

OBJECTIVES

The rise in carbapenem-resistant Enterobacterales (CRE) has reinforced the global quest for developing effective therapeutics. Traditional drug discovery approaches have been inadequate in overcoming this challenge due to their resource and time constraints.

METHODS

English literature was searched by structured queries related to our review between January 1, 2020, and December 31, 2024.

RESULTS

The key resistance mechanisms in CRE, such as enzymatic hydrolysis, decreased permeability, and efflux pump overexpression, have been examined in this review. Computational technologies have become pivotal in discovering novel antimicrobial agents with improved accuracy and efficiency. Besides this, the review highlights the advances in structure- and ligand-based drug discovery approaches for identifying potential drugs against CRE. Recent studies demonstrating the use of such in silico techniques to develop targeted drugs against CRE have also been explored. Moreover, this review also underscores the significance of integrating both in silico and in vitro techniques to counter resistance in Enterobacterales, supported by the latest studies. However, these promising computational technologies have a few major drawbacks, such as a lack of standardized parameterization, potentially false positives, and the complexity of effective clinical translations. The drug regulatory barriers also restrict the progress of new antimicrobials for market approval.

CONCLUSION

The use of computational technologies for antimicrobial inhibitor discovery is gaining popularity, and it can be expedited by refining computational techniques and integrating them with reliable in vitro validation. The use of innovative hybrid in silico and in vitro technologies is the need of the hour to tackle CRE and mitigate the global threat of antimicrobial resistance.

Exploring the hub genes and potential drugs involved in Fanconi anemia using microarray datasets and bioinformatics analysis

Fanconi anemia (FA) is a genetic disorder that occurs when certain genes responsible for repairing DNA replication and promoting homologous recombination fail to function properly. This leads to severe clinical symptoms and a wide range of cancer-related characteristics. Recent treatment approaches for FA involve hematopoietic stem cell transplantation (HSCT), which helps restore the population of stem cells. A survival study using p-values indicated that specific hub genes play a significant role in diagnosing and predicting the disease. To find potential medications that interact with the identified hub genes, researchers inferred drugs. Among hub genes, TP53 was found to be particularly promising through computational analysis. Further investigation focused on two drugs, Topiramate and Tocofersolan predicted based on drug bank database analysis. Molecular docking strategies were employed to assess the best binding pose of these drugs with TP53. Topiramate showed a binding affinity of -6.5 kcal/mol, while Tocofersolan showed -8.5 kcal/mol against the active residues within the binding pocket. Molecular dynamics (MD) simulations were conducted to observe the stability of each drug's interaction with the TP53 protein over time. Both drugs exhibited stable confirmation with only slight changes in the loop region of the TP53 protein during the simulation intervals. Results also shows that there was a high fluctuation observed during apo-sate simulation time intervals as compared to complex system. Hence, it is suggested that the exploration of structure-based drug design holds promising results to specific target. This could potentially lead to a breakthrough in future experimental approaches for FA treatment.

Targeting CDK6 in hormone receptor-positive breast cancer: inhibitor discovery for precision oncology through dynamics study

CDK6 is a critical protein involved in the regulation of the cell cycle, playing an important role in the progression from the G1 to S phase. In breast cancer, dysregulation of this protein is involved in tumour development and progression, particularly in hormone receptor-positive (HR+) breast cancers. The upregulation of CDK6 have been observed in a subset of breast cancers, leading to uncontrolled progression of the cell cycle and increased proliferation of cells. The purpose of this abstract is to provide an outline of CDK6's role. In breast cancer and the therapeutic strategies targeting CDK6 using specific selected inhibitors. To discover viable therapeutic candidates after competitive inhibition of CDK6 with a small molecular drug complex, high throughput screening and docking studies were used. Further, we carried the compounds based on ADMET properties and prediction of activity spectra for substances analysis. Finally, two different compounds were selected to carry out MD simulations. CDK6-IMPHY002642 and CDK6-IMPHY005260 are the two compounds that were identified. Overall, our results suggest that the CDK6-IMPHY002642 and CDK6-IMPHY005260 complex was relatively stable during the simulation. The compounds that have been found can also be further examined as potential therapeutic possibilities. The combined findings suggest that CDK6, together with their genetic changes, can be investigated in therapeutic interventions for precision oncology, leveraging early diagnostics and target-driven therapy.

3D-QSAR and ADMET studies of morpholino-pyrimidine inhibitors of DprE1 from Mycobacterium tuberculosis

DprE1 is involved in the synthesis of Mycobacterium tuberculosis cell wall and is a potent drug target for Tuberculosis (TB) treatment. The structure and dynamics of the loops L-I and L-II flanking the inhibitor binding site was studied using molecular dynamics (MD) simulation and MMPBSA in Amber v18. Docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) of 55 Morpholino-pyrimidine (MP) inhibitors was carried out using Autodock v1.2.0 and Forge v10. ADMET analysis was done using SwissADME and pkCSM. All MP inhibitors docked in the DprE1 binding pocket, making contacts with L-II residues. MD studies showed that L-I and L-II unfold in the absence of the inhibitor but fold stably structure with reduced protein motions in the presence of MP-38, the highest affinity inhibitor. This was confirmed by k-means clustering and secondary structure analysis. L-II residues, L317, F320 and R325 contributed most towards the MMPBSA binding free energy of MP-38. A robust field-based 3D-QSAR model showed values of r2train = 0.982, r2test = 0.702 and q2 = 0.516. The MP inhibitor field points were broadly divided into negative electrostatics near the A, B rings and hydrophobic electrostatics near the D, E rings. Addition of negative groups at methanone position and ring B as well as addition of hydrophobic and bulky groups at ring E will improve activity. Highly active compounds 47, 49 and 50 of MP series exhibited highly favourable drug-like properties. SAR and ADMET insights attained from this model will help in the development of active DprE1 inhibitors in future.

Integration of metabolomics and chemometrics with in-silico and in-vitro approaches to unravel SARS-Cov-2 inhibitors from South African plants

Coronavirus disease (COVID-19) is still a severe concern, especially in Africa with suboptimal intention rates of vaccination. This flagged the requirement of plant-based remedies as an alternative treatment. In this study we integrated metabolomics and chemometrics approaches with In silico and In vitro approaches to accelerate and unravel compounds from commonly used South African plants that may inhibit SARS-CoV-2 main protease. The selected commonly used plants, Artemisia afra and Artemisia annua, were found to be non-toxic against Vero cells, as determined by the resazurin cell viability assay. Metabolites profiling revealed eighty-one compounds and the top three hit compounds, quercetin 3-O-(6"-acetyl-glucoside), 2"-O-acetylrutin, and quercetin 3-(6"-malonyl-glucoside), had binding affinities of -9.3 kcal/mol, -9.5 kcal/mol, and -9.3 kcal/mol, respectively. The 2"-O-acetyl group of the rutin moiety and quercetin moiety produces a hydrogen bond with the amide nitrogen of His41 and with the side chain carboxylate of Cys145, respectively. Molecular dynamics simulations revealed a stable binding of the docked complexes. In silico observations were validated by In vitro bioassay, which flagged the ability of these compounds to inhibit SARS-CoV-2 3CLpro. The collected analysed data of this study does not only draw special attention to the surfaced 2"-O-acetylrutin as the best suitable inhibitor of SARS-CoV-2 3CLpro, but also indirectly reveals the importance of integrating metabolomics and chemometrics approaches with In silico and In vitro approaches to accelerate and unravel compounds from South African commonly used plants.

Can quercetin reduce arsenic induced toxicity in mouse BALB/c 3T3 fibroblast cells? A study involving in vitro, molecular docking, and ADME predictions

This study aimed to investigate the protective effect of quercetin against arsenic-induced oxidative damage, inflammation, and apoptosis in mouse BALB/c 3T3 fibroblast cells (NIH-3T3). Arsenic at different concentrations of 0.05 µM (low), 0.5 µM (medium), 10 µM (high) doses were used to induce toxicity, while 120 μm quercetin was used for treatment. MTT and LDH analyses were performed to determine the effect of arsenic and quercetin on cell viability, while oxidative stress markers and antioxidant enzyme activities were measured by spectrophotometric method. TNF-α and IL-1β levels were measured by the ELISA method, Autodock programs were used for molecular docking studies. In addition, computer-based analyses of quercetin and succimer molecules were performed using SwissADME web tools. TNF-α (PDB ID: 2AZ5), IL-1β (PDB ID: 1ITB), Caspase3 (PDB ID: 2XYG), Bax (PDB ID: 4S0O), SOD (PDB ID:1CBJ), GSH-Px (PDB ID: 1GP1) and Bcl-2 (PDB ID: 1G5M) crystal structures were obtained from the Protein Data Bank. Bax and Bcl-2 levels of apoptotic genes and mRNA expression levels of Caspase-3 activity were measured using the QRT-PCR technique. TUNEL staining was performed to determine DNA fragmentations, while DAPI staining was done to visualise nuclear modifications. Quercetin has been found to significantly reduce oxidative stress, inflammation, and apoptosis in cells and exert anti-apoptotic effects. Molecular docking studies revealed quercetin shows good binding affinity with molecules with SOD, GSH-Px, Bax, Bcl-2, Caspase-3, TNF-α and IL-1β structures, and has been observed to bind with Bax and Bcl-2 with molecular docking scores of -7.5 and - 7.7 kcal/mol, respectively. These findings are supported by results showing that quercetin is effective in anti-apoptotic and anti-inflammatory processes in arsenic-induced cells under in vitro conditions. In addition, when ADME values are examined, it can be considered that quercetin is a useful and effective candidate compound in reducing arsenic toxicity, considering its higher synthetic accessibility score, better pharmacokinetic properties, and good biological transition and interaction capacities compared to succimer.

Comprehensive biochemical, molecular and structural characterization of subtilisin with fibrinolytic potential in bioprocessing

Enzyme deployment is proliferating extensively in industries owing to their environmentally friendly and easily degradable attributes. This article undertakes an exhaustive examination of wild subtilisin enzyme, covering purification, biochemical delineation, analytical techniques, and practical implementations. The purification methodology involved partial refinement, anionic exchange, and gel filtration chromatography, culminating in a purification factor of 3.406, corroborated by SDS-PAGE showcasing a molecular weight of ~ 42 kDa. Biochemical scrutiny unveiled the enzyme's response, with an optimal pH at 9 and temperature peak at 60 ℃. Various surfactants, metal ions, organic solvents and inhibitors exhibited notable efficacy. Substrate specificity and kinetics showcased the utmost specificity with N-Suc-F-A-A-F-pNA, registering Km and Vmax values of 0.731 ± 0.5 mM and 0.87 ± 9 × 103 U/mg, respectively. Different bioanalytical techniquesproffered insights into structural and biophysical facets. Practical applications encompassed goat skin depilation, feather disintegration, blood clot dissolution, exemplifying the enzyme's multifaceted utility. To embark upon the elucidation of structure-function relationships, a three-dimensional model was devised through homology modelling, leveraging existing subtilisin structures (PDB: 3WHI). Molecular docking score of - 8.8 kcal/mol and dynamic simulations augmented the comprehension of molecular interactions with N-Suc-F-A-A-F-pNA. This research significantly contributes to unravelling the biochemical intricacies of wild subtilisin and underscores potential industrial and biomedical prowess. Subtilisin can be explored for its thrombolytic potential in several cardiovascular diseases. It may aid in the management of thrombosis by dissolving blood clots in conditions like deep pulmonary embolism, myocardial infarction, ischemic strokes, and in atherosclerosis by breaking down fibrin in arterial plaques, thus preventing heart attacks and strokes.

Anthelmintic activity on Haemonchus contortus and toxicity of benzoyl-carvacrol: a study in vitro, in silico and in vivo

Carvacrol is isolated from essential oils and possesses activity against gastrointestinal nematodes of small ruminants. Benzoylation has been proposed to improve its pharmacological and pharmacokinetic properties. The objectives of this study were to evaluate the ovicidal activity of benzoyl-carvacrol (BC) against Haemonchus contortus, the in silico interaction of BC with the β-tubulin protein and the toxicity of this compound. Carvacrol was subjected to benzoylation and analyzed by gas chromatography coupled to mass spectrometry (GC/MS). The activity of BC and carvacrol was evaluated against H. contortus in the egg hatching test. The in silico study was based on molecular docking with the β-tubulin and thiabendazole used as control. The acute toxicity test was performed with BC and carvacrol by up-and-down procedure (limit test: 2,000 mg/kg) in Wistar rats. GC/MS confirmed the benzoylation. BC and carvacrol inhibited egg hatching by 99.70 and 98.89% at concentrations of 3.16 and 1 mg/mL, respectively, and interacted with β-tubulin. No mortality was caused by compounds, but rats treated with carvacrol demonstrated intoxication signs. These findings indicated that BC showed effect on H. contortus and can potentially interact with β-tubulin of nematodes in addition to presenting toxicological safety in laboratory animals.

Exploiting the Achilles' heel of cancer through a structure-based drug-repurposing approach and experimental validation of top drugs using the TRAP assay

Telomerase, a reverse transcriptase implicated in replicative immortality of cancers, remains a challenging target for therapeutic intervention due to its structural complexity and the absence of clinically approved small-molecule inhibitors. In this study, we explored drug repurposing as a pragmatic approach to address this gap, leveraging FDA-approved drugs to accelerate the identification of potential telomerase inhibitors. Using a structure-based drug discovery framework, we screened the DrugBank database through a previously validated pharmacophore model for the FVYL pocket in the hTERT thumb domain, the established binding site of BIBR1532. This was followed by molecular docking, pharmacokinetic filtering, and molecular dynamics (MD) simulations to evaluate the stability of protein-ligand complexes. Binding free energy calculations (MM-PBSA and MM-GBSA) were employed for cross-validation, identifying five promising candidates. Experimental validation using the Telomerase Repeat Amplification Protocol (TRAP) assay confirmed the inhibitory potential of Raltitrexed, showing significant inhibition with IC50 8.899 µM in comparison to control. Decomposition analysis and Structure-Activity Relationship (SAR) studies further offered insights into the binding mechanism, reinforcing the utility of the FVYL pocket as a druggable site. Raltitrexed's dual mechanism of action, targeting both telomerase and thymidylate synthase, underscores its potential as a versatile anticancer agent, suitable for combination therapies or standalone treatment. As the top lead, Raltitrexed demonstrates the potential of repurposed drugs in telomerase-targeted therapies, offering a time and cost-effective strategy for advancing its clinical development. The study also provides a robust framework for future drug development, addressing challenges in targeting telomerase for anticancer therapy.

Cancer Drug Sensitivity Prediction Based on Deep Transfer Learning

In recent years, many approved drugs have been discovered using phenotypic screening, which elaborates the exact mechanisms of action or molecular targets of drugs. Drug susceptibility prediction is an important type of phenotypic screening. Large-scale pharmacogenomics studies have provided us with large amounts of drug sensitivity data. By analyzing these data using computational methods, we can effectively build models to predict drug susceptibility. However, due to the differences in data distribution among databases, researchers cannot directly utilize data from multiple sources. In this study, we propose a deep transfer learning model. We integrate the genomic characterization of cancer cell lines with chemical information on compounds, combined with the Encyclopedia of Cancer Cell Lines (CCLE) and the Genomics of Cancer Drug Sensitivity (GDSC) datasets, through a domain-adapted approach and predict the half-maximal inhibitory concentrations (IC50 values). Afterward, the validity of the prediction results of our model is verified. This study effectively addresses the challenge of cross-database distribution discrepancies in drug sensitivity prediction by integrating multi-source heterogeneous data and constructing a deep transfer learning model. This model serves as a reliable computational tool for precision drug development. Its widespread application can facilitate the optimization of therapeutic strategies in personalized medicine while also providing technical support for high-throughput drug screening and the discovery of new drug targets.

Isolation and in silico investigation of cannflavins from Cannabis sativa leaves as potential anti-SARS-CoV-2 agents targeting the Papain-Like Protease

This study aimed to isolate and identify three prenylflavonoids (cannflavin A, B, and C) from Cannabis sativa leaves using different chromatographic techniques. The potential of the isolated compounds against SARS-CoV-2 was suggested through several in silico analysis. Structural similarity studies against nine co-crystallized ligands of SARS-CoV-2's proteins indicated the similarities of the isolated cannflavins with the SARS-CoV-2 Papain-Like Protease (PLP) ligand, Y95. Then, flexible allignment study confirmed this similarity. Docking experiments showed successful binding of all cannflavins within the active pocket of PLP, with energies comparable to Y95. Among them, cannflavin A demonstrated the most similar binding mode, while cannflavin C exhibited the best energy. Molecular dynamics (MD) simulations and MM-GPSA confirmed the accurate binding of cannflavin A to the PLP. In silico ADMET studies indicated favourable drug-like properties for all three compounds, suggesting their potential as anti-SARS-CoV-2 agents. Further In vitro and In vivo investigations are necessary to validate these findings and establish their efficacy and safety profiles.

Alkaloids Isolated from Vepris glandulosa with Antidiabetic Properties: An In Vitro and In Silico Analysis

Diabetes is a major global health issue and as current treatments fail, the search for new antidiabetic drugs is crucial. This investigation, focusing on identifying potential antidiabetic compounds from the endangered plant species Vepris glandulosa, led to the isolation of two known alkaloids, choisyine acetate (1) and choisyine (2). The study established the in vitro inhibitory activities and in silico molecular interaction of the two alkaloids with α-amylase based on IC50 values, Linewaever-Burk/Dixon plot kinetic analyses and Molecular docking, respectively. The α-amylase inhibition assay revealed noncompetitive inhibition for both compounds with IC50 and Ki values of 4.74±0.17 and 4.75 mM for compound 1, and 11.29±0.44 and 12.37 mM for compound 2, respectively. In comparison, the standard drug acarbose displayed a competitive mode of inhibition, with IC50 and Ki values of 11.99±0.02 and 12.68 mM, respectively. The binding affinities with α-amylase were -6.42 and -6.07 kcal/mol for compounds 1 and 2, respectively relative to acarbose -8.03 Kcal/mol. Moreover, these two compounds' predicted physicochemical and ADMET properties justified their potential as lead compounds for drug discovery. These compounds demonstrated remarkable inhibition potential comparable to the standard drug, highlighting their potential as viable alternatives in managing diabetes.

Synthesis, anti-proliferation, apoptosis induction in breast cancer cells, and aromatase inhibition of coumarin-triazole hybrids: In vitro and in silico studies

Breast cancer is one of the most common cancers found in women worldwide. Besides the availability of clinical drugs, drug resistance and considerable side effects are concerning issues driven the needs for the discovery of novel anticancer agents. Aromatase inhibition is one of the effective strategies for management of hormone-dependent breast cancer. Triazole, coumarin, and isatin are heterocyclic scaffolds holding great attention in the field of drug design. Molecular hybridization is a well-known strategy to achieve new molecules with improved potency and properties. Herein, a set of 27 triazole-based hybrids (i.e., coumarin-triazoles series 5-6 and isatin-triazoles series 7) were synthesized and investigated for their anti-proliferation, apoptosis induction, and aromatase inhibitory potentials. Anti-proliferative study against the hormone-dependent breast cancer (T47D) cell line indicated that coumarin-triazoles 5h (R=NO2) and 6i (R=SO2NH2) were the two most potent antiproliferative agents. Particularly, compound 5h showed comparable potency and superior selectivity index than that of the reference drug, doxorubicin. Moreover, the coumarin-triazole 5h induced cellular apoptosis of the estrogen-dependent breast cancer (MCF-7) cells. Additionally, findings from the aromatase inhibitory assay suggested four compounds as potential aromatase inhibitors (i.e., 5i, 6f, 6g and 6i, IC50 = 1.4-2.4 μM). Two QSAR models with preferable predictive performances were constructed to reveal key properties influencing antiproliferative and aromatase inhibitory effects. Molecular docking was conducted to elucidate the possible binding modalities against the target aromatase enzyme. Key structural features essential for the binding were highlighted. Moreover, the drug-like properties of top-ranking compounds were assessed to ensure their possibilities for successful development.

Computational exploration of Zika virus RNA-dependent RNA polymerase inhibitors: a promising antiviral drug discovery approach

The emergence of the Zika virus, which belongs to the Flaviviridae family, became a significant worldwide health issue due to its link with severe neurological complications. The RNA-dependent RNA polymerase (RdRp) of the Zika virus plays a significant part in the replication of the virus and is considered a promising candidate for antiviral drug identification. In this study, we employed computer-based drug discovery approaches to identify potential natural compounds that could act as inhibitors against the RdRp protein of the Zika virus. A comprehensive virtual screening strategy was implemented using the MTiOpenScreen webserver to identify natural compounds from the NP-Lib database. Four natural compounds having the ZINC ID - ZINC000253499147, ZINC000299817665, ZINC000044404209, and ZINC000253388535 were selected based on the binding score revealed during virtual screening. Molecular docking simulations of these selected compounds and reference compounds were performed to assess the binding affinities and the molecular bonds formed during the docking. Additionally, molecular dynamics (MD) simulations, endpoint free binding energy calculation and principal component analysis (PCA) were performed to evaluate the stability and dynamics of the protein-ligand complexes. These compounds exhibited favourable binding energies and formed stable interactions within the active site of the RdRp protein. Moreover, the molecular dynamics simulations revealed the robustness of the protein-ligand complexes, suggesting the potential for sustained inhibition. These findings provide valuable insights for the design and development of novel therapeutic interventions against Zika virus infection. Further experimental validation and optimization of the identified compounds are warranted to advance their potential translation into effective antiviral drugs.Communicated by Ramaswamy H. Sarma.

Unlocking the potential of in silico approach in designing antibodies against SARS-CoV-2

Antibodies are naturally produced safeguarding proteins that the immune system generates to fight against invasive invaders. For centuries, they have been produced artificially and utilized to eradicate various infectious diseases. Given the ongoing threat posed by COVID-19 pandemics worldwide, antibodies have become one of the most promising treatments to prevent infection and save millions of lives. Currently, in silico techniques provide an innovative approach for developing antibodies, which significantly impacts the formulation of antibodies. These techniques develop antibodies with great specificity and potency against diseases such as SARS-CoV-2 by using computational tools and algorithms. Conventional methods for designing and developing antibodies are frequently costly and time-consuming. However, in silico approach offers a contemporary, effective, and economical paradigm for creating next-generation antibodies, especially in accordance with recent developments in bioinformatics. By utilizing multiple antibody databases and high-throughput approaches, a unique antibody construct can be designed in silico, facilitating accurate, reliable, and secure antibody development for human use. Compared to their traditionally developed equivalents, a large number of in silico-designed antibodies have advanced swiftly to clinical trials and became accessible sooner. This article helps researchers develop SARS-CoV-2 antibodies more quickly and affordably by giving them access to current information on computational approaches for antibody creation.

A review on pyrimidine-based pharmacophore as a template for the development of hybrid drugs with anticancer potential

The low efficacy and toxicity of traditional chemotherapy, led by drug resistance of targeted anticancer therapies, have mandated the exploration and development of anticancer molecules. In this league, hybrid drugs, owing to their peculiar multitargeted functionality and structural diversity, could serve as vital leads in this quest for drug discovery. They are plausibly found to offer added advantages considering the improved efficacy, low toxicity, and improved patient compliance. Among numerous heterocycles explored, pyrimidine derivatives epitomize as a valuable resource for the hybrid drug development due to their validated efficacy and versatility. The present review discusses the role of pyrimidine, a diversified pharmacophore in drug development and concepts of hybrid drugs. The study covers the recent advancements in pyrimidine-based hybrid pharmacophores. It delves further into the challenges in hybrid drug development and ongoing research in hybrid drug discovery. Furthermore, the challenges faced in developing hybrid molecules, such as their design and optimization complexities, bioavailability and pharmacokinetics issues, target identification and validation, and off-target effects, are discussed.

Optimizing Cancer Treatment: Exploring the Role of AI in Radioimmunotherapy

Radioimmunotherapy (RIT) is a novel cancer treatment that combines radiotherapy and immunotherapy to precisely target tumor antigens using monoclonal antibodies conjugated with radioactive isotopes. This approach offers personalized, systemic, and durable treatment, making it effective in cancers resistant to conventional therapies. Advances in artificial intelligence (AI) present opportunities to enhance RIT by improving precision, efficiency, and personalization. AI plays a critical role in patient selection, treatment planning, dosimetry, and response assessment, while also contributing to drug design and tumor classification. This review explores the integration of AI into RIT, emphasizing its potential to optimize the entire treatment process and advance personalized cancer care.

Neuroprotective thiazole sulfonamides against 6-OHDA-induced Parkinsonian model: in vitro biological and in silico pharmacokinetic assessments

The limitations of currently existing medications in delaying or halting the development of Parkinson's disease (PD) remain dramatically problematic, making it the second most prevalent neurodegenerative disorder. Moreover, it is expected that the number of PD cases will double within the next 30 years. Herein, to discover a novel neuroprotective therapeutic strategy, a series of multifunctional thiazole sulfonamides underwent preliminary assessment owing to their neuroprotective capabilities against 6-hydroxydopamine (6-OHDA)-induced damage in human neuronal SH-SY5Y cells. Pretreatment with novel synthetic hybrids, including 1, 2, and 8, significantly improved cell viability, reduced lactate dehydrogenase (LDH) leakage, prevented mitochondrial dysfunction, and mitigated intracellular oxidative stress. Insight molecular mechanisms and potential targets of these compounds were elucidated through their activation and binding interaction with sirtuin 1 (SIRT1), suggesting their influencing roles on relevant downstream cascades of PD. Furthermore, in silico pharmacokinetic analysis revealed the drug-likeness of these three hybrids, which are capable of being distributed into the central nervous system (CNS) with slight toxicity. Therefore, these novel neuroprotective thiazole sulfonamides are promising candidates for further development (i.e., in vivo and clinical trials) of effective PD therapy.

Discovering broad-spectrum inhibitors for SARS-CoV-2 variants: a cheminformatics and biophysical approach targeting the main protease

The COVID-19 pandemic caused by SARS-CoV-2 still lacks effective antiviral drugs. Therefore, a thorough receptor-based virtual screening study was conducted to screen different natural and synthetic drug libraries, such as the Asinex Antiviral, Seaweed Metabolite Database, Medicinal Fungi Secondary Metabolite and Therapeutics Library, and Comprehensive Marine Natural Products Database comprising 6,827, 1,191, 1,830, and 45,000 compounds, respectively, against the main protease enzyme of SARS-CoV-2. Accordingly, three drug molecules (BBB-26580140, BDE-32007849, and LAS-51378804) are highlighted as the best binding molecules to the main protease S1 pocket. The docking binding energy scores of BBB-26580140, BDE-32007849, and LAS-51378804 were -13.02, -13.0, and -12.56 kcal/mol, respectively. Compared to the control Z1741970824 molecule with a binding energy score of -11.59 kcal/mol, the lead structures identified herein showed robust hydrophilic and van der Waals interactions with the enzyme active site residues, such as His41 and Cys145, and achieved highly stable binding modes. The simulations showed a stable structure of the main protease enzyme with the shortlisted leads in the pocket, and the network of binding interactions remained intact during the simulations. The overall molecular mechanics with generalized Born and surface area solvation binding energies of the BBB-26580140, BDE-32007849, LAS-51378804, and control molecules are -53.02, -56.85, -55.44, and -48.91 kcal/mol, respectively. Similarly, the net molecular mechanics Poisson-Boltzmann surface area binding energies of BBB-26580140, BDE-32007849, LAS-51378804, and control are -53.6, -57.61, -54.41, and -50.09 kcal/mol, respectively. The binding entropy energies of these systems showed lower free energies, indicating their stable nature. Furthermore, the binding energies were revalidated using the water swap method that considers the role of the water molecules in bridging the ligands to the enzyme active site residues. The compounds also revealed good ADMET properties and followed all major rules of drug-likeness. Thus, these compounds are predicted as promising leads and can be subjected to further experimental studies for evaluation of their biological activities.

Machine Learning-Guided Prediction of Hydroformylation

A holistic model for predicting yield and linear selectivity for the hydroformylation of 1-octene was developed by machine learning using the experimental data collected from literatures. Physical organic chemistry (POC) parameter-based descriptors were adopted to represent pre-catalyst molecular features. Machine learning models trained respectively by Random Forests (RF) and Extreme Gradient Boost (XGBoost) algorithm showed remarkable performance on predicting linear selectivity. The method can also comprehensively map the correlation between reaction conditions and the results. The accuracy of the prediction results was verified by experimental data.

An update on drug repurposing in Parkinson's disease: Preclinical and clinical considerations

The strategy of drug repositioning has historically played a significant role in the identification of new treatments for Parkinson's disease. Still today, numerous clinical and preclinical studies are investigating drug classes, already marketed for the treatment of metabolic disorders, for their potential use in Parkinson's disease patients. While drug repurposing offers a promising, fast, and cost-effective path to new treatments, these drugs still require thorough preclinical evaluation to assess their efficacy, addressing the specific neurodegenerative mechanisms of the disease. This review explores the state-of-the-art approaches to drug repurposing for Parkinson's disease, highlighting particularly relevant aspects. Preclinical studies still predominantly rely on traditional neurotoxin-based animal models, which fail to effectively replicate disease progression and are characterized by significant variability in model severity and timing of drug treatment. Importantly, for almost all the drugs analyzed here, there is insufficient data regarding the mechanism of action responsible for the therapeutic effect. Regarding drug efficacy, these factors may obviously render results less reliable or comparable. Accordingly, future preclinical drug repurposing studies in the Parkinson's disease field should be carried out using next-generation animal models like α-synuclein-based models that, unfortunately, have to date been used mostly for studies of disease pathogenesis and only rarely in pharmacological studies.

Identification of potential drug molecules against fibroblast growth factor receptor 3 (FGFR3) by multi-stage computational-biophysics correlate

The fibroblast growth factor receptor 3 (FGFR3) is warranted as a promising therapeutic target in bladder cancer as it is described in 75% of papillary bladder tumors. Considering this, the present study was conducted to use different approaches of computer-aided drug discovery (CADD) to identify the best binding compounds against the active pocket of FGFR3. Compared to control pyrimidine derivative, the study identified three promising lead structures; BDC_24037121, BDC_21200852, and BDC_21206757 with binding energy value of -14.80 kcal/mol, -12.22 kcal/mol, and -11.67 kcal/mol, respectively. The control molecule binding energy score was -9.85 kcal/mol. The compounds achieved deep pocket binding and produced balanced interactions of hydrogen bonds and van der Waals. The FGFR3 enzyme residues such as Leu478, Lys508, Glu556, Asn562, Asn622, and Asp635. The molecular dynamic (MD) simulation studies additionally validated the docked conformation stability with respect to FGFR3 with a mean root mean square deviation (RMSD) value of < 3 Å. The root mean square fluctuation (RMSF) complements the complexes structural stability and the residues showed less fluctuation in the presence of compounds. The Poisson-Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods revalidated compounds better binding and highlighted van der Waals energy to dominate the overall net energy. The docked stability was additionally confirmed by WaterSwap and AMBER normal mode entropy energy analyses. In a nutshell, the compounds shortlisted in this study are promising in term of theoretical binding affinity for FGFR3 but experimental validation is needed.Communicated by Ramaswamy H. Sarma.

Antiangiogenic potential of phytochemicals from Clerodendrum inerme (L.) Gaertn investigated through in silico and quantum computational methods

Suppressing vascular endothelial growth factor (VEGF), its receptor (VEGFR2), and the VEGF/VEGFR2 signaling cascade system to inhibit angiogenesis has emerged as a possible cancer therapeutic target. The present work was designed to discover and evaluate bioactive phytochemicals from the Clerodendrum inerme (L.) Gaertn plant for their anti-angiogenic potential. Molecular docking of twenty-one phytochemicals against the VEGFR-2 (PDB ID: 3VHE) protein was performed, followed by ADMET profiling and molecular docking simulations. These investigations unveiled two hit compounds, cirsimaritin (- 12.29 kcal/mol) and salvigenin (- 12.14 kcal/mol), with the highest binding energy values when compared to the reference drug, Sorafenib (- 15.14 kcal/mol). Furthermore, only nine phytochemicals (cirsimaritin and salvigenin included) obeyed Lipinski's rule of five and passed ADMET filters. Molecular dynamics simulations run over 100 ns revealed that the protein-ligand complexes remained stable with minimal backbone fluctuations. The binding free energy values of cirsimaritin (- 52.35 kcal/mol) and salvigenin (- 55.89 kcal/mol), deciphered by MM-GBSA analyses, further corroborated the docking interactions. The HOMO-LUMO band energy gap (ΔE) was calculated using density-functional theory (DFT) and substantiated using density of state (DOS) spectra. The chemical reactivity analyses revealed that salvigenin exhibited the highest chemical softness value (6.384 eV), the lowest hardness value (0.07831 eV), and the lowest ΔE value (0.1566 eV), which implies salvigenin was less stable and chemically more reactive than cirsimaritin and sorafenib. These findings provide further evidence that cirsimaritin and salvigenin have the ability to prevent angiogenesis and the development of cancer. Nevertheless, more in vitro and in vivo confirmation is necessary.

The latest progress in assay development in leishmaniasis drug discovery: a review of the available papers on PubMed from the past year

INTRODUCTION

Leishmaniasis is a significant neglected tropical disease with limited treatment options that urgently requires ongoing efforts in drug discovery. Recent advances have focused on the development of new assays and methods to identify effective therapeutic candidates.

AREAS COVERED

This review explores recent trends and methodologies in leishmaniasis drug discovery, with a particular focus on in silico and in vitro studies, as well as in vivo validation, using animal models. A detailed analysis of recent studies was provided, discussing the methodologies employed, such as manual and automated parasite quantification, and the use of fluorescence and luminescence-based techniques. Additionally, global research trends were analyzed, highlighting the leading countries in scientific output and the collaborative efforts driving advancements in this field.

EXPERT OPINION

The field of leishmaniasis drug discovery has rapidly progressed in the last years, but the lack of standardized methodologies and limited in vivo validation remain significant hurdles. To advance promising treatments to clinical trials, cross-validation of preclinical findings and interdisciplinary collaboration are essential. Increased funding and global partnerships are also crucial to accelerate the discovery and development of alternative and effective therapies.

Antibacterial activities of phytochemical constituents from the leaves of Coula edulis (Olacaceae) and In silico investigations of ursolic acid

From the leaves of Coula edulis, fourteen compounds were isolated and identified: D-mannitol (1), a mixture of β-sitosterol (2) and stigmasterol (3), α-amyrin (4), betulin (5), lupeol (6), lupenone (7), betulinic acid (8), taraxerol (9), 3β-(E)-coumaroyltaraxerol (10), 3β-(Z)-coumaroyltaraxerol (11), ursolic acid (12), stigmasterol 3-O-β-D-glucoside (13), and β-sitosterol 3-O-β-D-glucoside (14). These compounds were analysed through NMR spectroscopy (both 1D and 2D) and by comparing them to previously published data. Compounds 1, 4, 5, and 7-9 have been identified from this species for the first time. Antibacterial activity was assessed, with compound 12 displaying the best efficacy against Staphylococcus aureus (MIC: 15.6 μg/mL). Molecular docking of compound 12 led to twenty lead compounds, among which 12 F displayed the highest score (-10.1 kcal/mol). Most lead compounds showed better scores compared to Vancomycin (-8.8 kcal/mol). Biovia Discovery Studio analysis reveals lead compounds interacting with CASTp-predicted active pocket amino acids.

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.

Improving the binding affinity of plastic degrading cutinase with polyethylene terephthalate (PET) and polyurethane (PU); an in-silico study

Plastic pollution is a worrying problem, and its degradation is a laborious process. Although enzymatic plastic breakdown is a sustainable method, drawbacks such as numerous plastic kinds of waste make the degradation challenging. Therefore, a multi-plastic degrading (MPD) enzyme becomes necessary. In this in-silico study, microorganisms and their enzymes that are known to degrade plastic polymers such as PET, PU, PVC, and PE were identified to assess their MPD capability. The cutinase of Thermobifida fusca was found to degrade both PET and PU polymers. The crystallized structure of cutinase was retrieved from PDB, and PET, PU ligands were docked using Schrodinger. However, the interactions between cutinase and the ligands were not efficient, as evidenced by the docking scores of -4.047 and -4.993 for PET and PU, respectively. Nevertheless, the interaction of the cutinase's active site with the ligands by hydrogen bond formation was promising. In this work, unconserved regions of cutinase were identified as potential mutation sites to enhance binding efficiency. In-silico Alanine Scanning Mutagenesis (ASM) and Site Saturation Mutagenesis (SSM) were performed as screening tests to find variants of cutinase with better docking scores for both ligands, specifically S136D, N28M, and S136Q. Molecular Dynamic Simulation (MDS) was performed for Wild Type (WT) cutinase, variants, and their respective complexes formed with the ligands. This simulation indicated the compactness, stability, and minimal energy of the variant complexes compared to WT complexes. Subsequent in vitro studies can ensure the improved degradation of both PET and PU by the variants.

Biochemical markers in hand osteoarthritis: a path to precision medicine

Hand osteoarthritis (HOA) is a heterogeneous joint disease with high radiographic and symptomatic prevalence. The diagnosis of HOA is based on clinical and radiographic features. The identification of potential biomarkers for diagnosis, prognosis, disease severity assessment, and therapeutic efficacy evaluation of НОА remains an active area of research. To summarize the eligible biomarker data, a comprehensive narrative review was performed using the PubMed and Scopus databases covering publications from inception to December 2024. Our search uncovered five distinct groups of biomarkers associated with HOA, categorized based on their origin and involvement in distinct biological processes: (1) cartilage synthesis and catabolism, (2) bone remodeling, (3) inflammation, (4) adipokines, and (5) others classified separately. Each biomarker was evaluated in accordance with the Burden of disease, Investigative, Prognostic, Efficacy of intervention, and Diagnostic (BIPED) criteria. In conclusion, no biomarker has yet demonstrated sufficient sensitivity, specificity, or reproducibility to meet the BIPED criteria for classification. The early diagnosis and treatment of HOA require the development of more sensitive assays, advanced platforms, and rigorous bio-clinical trials to stratify previously studied biomarkers and identify novel ones. Precision medicine in HOA demands reliable biomarkers, cost-effective assays, and standardized, reproducible methodologies for global applicability.

The physics-AI dialogue in drug design

A long path has led from the determination of the first protein structure in 1960 to the recent breakthroughs in protein science. Protein structure prediction and design methodologies based on machine learning (ML) have been recognized with the 2024 Nobel prize in Chemistry, but they would not have been possible without previous work and the input of many domain scientists. Challenges remain in the application of ML tools for the prediction of structural ensembles and their usage within the software pipelines for structure determination by crystallography or cryogenic electron microscopy. In the drug discovery workflow, ML techniques are being used in diverse areas such as scoring of docked poses, or the generation of molecular descriptors. As the ML techniques become more widespread, novel applications emerge which can profit from the large amounts of data available. Nevertheless, it is essential to balance the potential advantages against the environmental costs of ML deployment to decide if and when it is best to apply it. For hit to lead optimization ML tools can efficiently interpolate between compounds in large chemical series but free energy calculations by molecular dynamics simulations seem to be superior for designing novel derivatives. Importantly, the potential complementarity and/or synergism of physics-based methods (e.g., force field-based simulation models) and data-hungry ML techniques is growing strongly. Current ML methods have evolved from decades of research. It is now necessary for biologists, physicists, and computer scientists to fully understand advantages and limitations of ML techniques to ensure that the complementarity of physics-based methods and ML tools can be fully exploited for drug design.

Multilayered screening for multi-targeted anti-Alzheimer's and anti-Parkinson's agents through structure-based pharmacophore modelling, MCDM, docking, molecular dynamics and DFT: a case study of HDAC4 inhibitors

Abstract

Alzheimer's disease (AD) and Parkinson's disease (PD) are neurological conditions that primarily impact the elderly having distinctive traits and some similarities in terms of symptoms and progression. The multifactorial nature of AD and PD encourages exploring potentiality of multi-target therapy for addressing these conditions to conventional, the "one drug one target" strategy. This study highlights the searching of potential HDAC4 inhibitors through multiple screening approaches. In this context, structure-based pharmacophore model, ligand profiler mapping and MCDM approaches were performed for target prioritization. Similarly, ligand profiler, MCDM and Docking studies were performed to prioritize multi-targeted HDAC4 inhibitors. These comprehensive approaches unveiled 5 common targets and 5 multi-targeted prioritized compounds consensually. MD simulations, DFT and binding free energy calculations corroborated the stability and robustness of propitious compound 774 across 5 prioritized targets. In conclusion, the screened compound 774 (ChEMBL 4063938) could be a promising multi-targeted therapy for managing AD and PD further rendering experimental validation.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00302-4.

In Silico-Guided Discovery of Polysaccharide Derivatives as Adjuvants in Nanoparticle Vaccines for Cancer Immunotherapy

Cancer vaccines utilizing nanoparticle (NP) structures that integrate antigens and adjuvants to enhance delivery and stimulate immune responses are emerging as a promising avenue in cancer immunotherapy. However, the development of cancer vaccines has been significantly hindered by the low immunogenicity of tumor antigens. To address this challenge, substantial efforts have been made in developing innovative adjuvants to elicit effective immune responses. In this study, we develop a NP cancer vaccine assisted by a polysaccharide derivative adjuvant, designed through a computational strategy, to evoke effective antigen-specific antitumor immunity. Using TLR4 as the putative receptor, we conducted a comprehensive evaluation of a prescreening library consisting of 34 inulin derivatives through docking and molecular dynamics simulation. Consequently, a new derivative, benzoylated inulin (InBz), is selected as the most promising TLR4 agonist. The adjuvant effect of InBz is evaluated by fabricating InBz NPs encapsulating the model antigen ovalbumin (OVA). In vitro, InBz-OVA NPs effectively activate the TLR4 signaling pathways and facilitate dendritic cell maturation, thereby enhancing the antigen delivery and presentation. In vivo, InBz-OVA NPs outperform a commercial aluminum-based adjuvant, elicit robust antibody titers, induce antigen-specific cytotoxic T lymphocytes, and achieve significant tumor suppression in murine models. Besides, the adjuvant effects of other representative derivatives, namely, acetylated and chloroacetylated inulin, with moderate and low potential from the library, are also chemically synthesized and experimentally evaluated and found to be in agreement with computational predictions, confirming the credibility of the strategy. This study provides an effective platform for the pursuit of efficient polysaccharide-based vaccine adjuvants.

Prediction of the Binding to the Nuclear Factor NF-Kappa-B by Constituents from Teucrium polium L. Essential Oil

Teucrium polium L. is a plant with various claims of ethnobotanical use, primarily for inflammatory diseases. Chemical studies have already isolated different types of terpenes from the species, and studies have established its pharmacological potential. The present study evaluates the components of T. polium essential oil cultivated in the Algerian Saharan Atlas. GC-MS identified the major components as fenchone (31.25%), 3-carene (15.77%), cis-limonene oxide (9.77%), and myrcene (9.15%). In the in silico prediction, molecules with more than 1% abundance were selected. Regarding Lipinski's rule, all molecules followed the rule. All molecules were found to be toxic in at least one model, with some molecules being non-genotoxic (6, 8, 10, 11, 12, 13) and others being non-mutagenic (5, 7, 9, 14). Three molecules were selected that showed the best results in pharmacokinetic and toxicity studies: the molecules that did not present carcinogenic potential (7-myrtenal; 9-myrtenol; 14-verbenol). The molecular target was established, and it seems that all three bound to the nuclear factor NF-kappa-B. Based on the docking and molecular dynamics results, these molecules have potential as anti-inflammatory and antitumor therapies, with further in vitro and in vivo studies needed to evaluate their activity and toxicity.

Cu(II)-Catalyzed Synthesis of Pyrazolo[3,4-b]pyridine Derivatives and Their Potential Antibacterial and Cytotoxic Activities with Molecular Docking, DFT Calculation, and SwissADME Analysis

The present work focuses on a newly synthesized pyrazolo[3,4-b]pyridine prepared by formal [3 + 3] cycloaddition using copper(II) acetylacetonate as the catalyst; efficient and effective mild reactions with high yields were obtained using this method. The synthesized compounds were identified by FT-IR, 1H and 13C NMR, and mass spectra (m/z) analyses. The compounds (2a-l) were screened for several in vitro and in silico activities. Compound 2g showed impressive inhibitory activities against methicillin-resistant Staphylococcus aureus (MIC: 2 μg/mL), vancomycin-resistant Enterococci (MIC: 8 μg/mL), piperacillin-resistant Pseudomonas aeruginosa, and extended-spectrum beta-lactamase-producing Escherichia coli (MIC: 4 μg/mL) compared to the positive control, ciprofloxacin. Compared to standard doxorubicin, compound 2g had a higher efficacy against the HepG2 cancer cell line, with a GI50 value of 0.01 μM. The highly active compound 2g was investigated for in silico molecular docking, density functional theory calculations (DFT), and SwissADME physicochemical properties. Compound 2g had a higher docking score compared with standard (-8.5 vs -7.3 and -10.0 vs -8.4 kcal/mol). In compound 2g, the energy gap was 0.17 eV, as determined by using DFT calculations. The physicochemical properties of all compounds were investigated by using SwissADME. Overall, compound 2g exhibited promising antibacterial and cytotoxic activities.

Special Issue "Advances in Drug Discovery and Synthesis"

In modern medicinal chemistry, drug discovery is a long, difficult, highly expensive and highly risky process for the identification of new drug compounds [...].

Advancing Treatment for Leishmaniasis: From Overcoming Challenges to Embracing Therapeutic Innovations

Protozoan parasite infections, particularly leishmaniasis, present significant public health challenges in tropical and subtropical regions, affecting socio-economic status and growth. Despite advancements in immunology, effective vaccines remain vague, leaving drug treatments as the primary intervention. However, existing medications face limitations, such as toxicity and the rise of drug-resistant parasites. This presents an urgent need to identify new therapeutic targets for leishmaniasis treatment. Understanding the complex life cycle of Leishmania and its survival in host macrophages can provide insights into potential targets for intervention. Current treatments, including antimonials, amphotericin B, and miltefosine, are constrained by side effects, costs, resistance, and reduced efficacy. Exploring novel therapeutic targets within the parasite's physiology, such as key metabolic enzymes or essential surface proteins, may lead to the development of more effective and less toxic drugs. Additionally, innovative strategies like drug repurposing, combination therapies, and nanotechnology-based delivery systems could enhance efficacy and combat resistance, thus improving anti-leishmanial therapies.

In-silico-based lead optimization of hit compounds targeting mitotic kinesin Eg5 for cancer management

Lead optimization is vital for turning hit compounds into therapeutic drugs. This study builds upon a prior in silico research, where the hit compounds had better binding affinity and stability compared to a reference drug. Using a genetic algorithm, 12,500 analogs of the top compounds from the prior study were generated. Virtual screening was done using a quantitative structure-activity relationship (QSAR) model. Top analogs, selected based on pChembL values below 6.000nM, underwent molecular docking targeting Human Eg5. The top five analogs from this study (Compound 9794, Compound 8592, Compound 9786, Compound 2744, and Compound 3246) demonstrated strong binding energies and interactions with key amino acids (GLU 116, GLU 117, and ARG 119). MMGBSA analysis revealed comparable affinities to the co-crystallized ligand, suggesting the top analogs' potential as Human Eg5 inhibitors. Induced fit docking highlighted Compound 9786's superior efficacy. Quantum Polarized Ligand Docking indicated promising scores for Compounds 8592 and 9786. ADMET predictions offered insights into pharmacological properties, with all compounds predicted to be HIA-positive and non-carcinogenic. Further MD simulation study confirms the stability of the top compounds in the active site of Eg5. This study shows the significance of integrated strategies in drug design. However, in vitro and in vivo studies should be conducted for these promising candidates to confirm their efficacy as Eg5 inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00300-6.

In silico docking and molecular dynamics for the discovery of inhibitors of enteric methane production in ruminants - A review

The increase in methane emissions, a major greenhouse gas, threatens human well-being and global ecosystems due to its contribution to global warming. Livestock, particularly ruminants, have been a major research topic in recent decades due to their methane production. Therefore, the objective of the current review was to comprehensively discuss the in silico techniques used to mitigate methane production from ruminants. The review covers the principles of in silico docking and molecular dynamics, which can be used to develop methanogenesis inhibitors. It also discusses specific methanogen enzymes as potential targets for inhibitor development. Furthermore, in silico-based methanogenesis inhibitor development studies have been reviewed with the authors' opinions. The further use of in silico-based research techniques, including artificial intelligence-based systems, is encouraged to help reduce methane production from livestock more efficiently and costeffectively.

Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug-Target Interaction Prediction

Predicting drug-target interactions (DTIs) with precision is a crucial challenge in the quest for efficient and cost-effective drug discovery. Existing DTI prediction models often require significant computational resources because of the intricate and exceptionally lengthy protein target sequences. This study introduces MMF-DTI, a lightweight model that uses multimodal fusion, to improve the generalizability of DTI predictions without sacrificing computational efficiency. The MMF-DTI model combines four distinct modalities: molecular sequence, molecular properties, target sequence, and target function description. This approach is noteworthy because it is the first to use natural language-based target function descriptions in predicting DTIs. The effectiveness of MMF-DTI has been confirmed through benchmark data sets, demonstrating its comparable performance in terms of generalizability, especially in scenarios with limited information about the drug or target. Remarkably, MMF-DTI accomplishes this using only half of the parameters and 17% of the VRAM compared with previous state-of-the-art models. This allows it to function even in constrained computational environments. The combination of performance and efficiency highlights the potential of multimodal data fusion in improving the overall applicability of models, providing promising opportunities for future drug discovery endeavors.

Computational Evaluation of Fusarium nygamai Compounds as AcrD Efflux Pump Protein Inhibitors of Salmonella Typhimurium

In Salmonella Typhimurium, efflux pump proteins, such as AcrD actively expel drugs and hazardous chemicals from bacterial cells, resulting in treatment failure and the emergence of antibiotic-resistant variants. Focusing on AcrD may lead to the development of novel antimicrobials against multidrug-resistant bacteria. However, challenges persist in achieving high selectivity, low toxicity, and effective bacterial penetration. Natural products, particularly microbial secondary metabolites, possess distinct chemical structures that may target the efflux pump systems. The efflux pump inhibitor capabilities of Fusarium nygamai compounds in Salmonella have not been previously investigated. This study employed molecular docking and molecular dynamics simulations to evaluate 25 F. nygamai compounds as potential inhibitors of AcrD. Additionally, the pharmacological characteristics of these substances were examined. Molecular docking results revealed that 3,6-Dimethoxy-2,5-dinitrobenzonitrile, methyl (2-oxo-3-phenylquinoxalin-1(2H)-yl)acetate, and 7-Methyl-5-nitro-1,4-dihydro-quinoxaline-2,3-dione exhibited the highest binding energies with AcrD. Furthermore, molecular dynamics simulations indicated stable ligand-receptor complex variations over time. This study contributes to the efforts against antibiotic resistance and the improvement of Salmonella infection treatment outcomes globally by facilitating the development of novel therapeutic approaches and enhancing antibiotic efficacy.

Identifying therapeutic target for prostate cancer: exploring Diosmetin as a CYP inhibitor

Prostate cancer is a prevalent and highly heterogeneous malignancy that affects men globally. Despite the availability of various treatment targets, Cytochrome P450 (CYP) enzymes have gained significant attention due to their crucial role in metabolizing both endogenous and exogenous compounds. This study explores Diosmetin as a potential CYP antagonist for treating prostate cancer. To evaluate Diosmetin's potential as a CYP antagonist, we employed a comprehensive in silico approach. Molecular docking was conducted using the Glide software to assess the binding affinity of Diosmetin with CYP enzymes, specifically CYP17A1 and CYP19A1, which are associated with prostate cancer. The druglike properties of Diosmetin were evaluated, focusing on its pharmacokinetic attributes. Additionally, Diosmetin's ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics were analyzed to determine its suitability as a therapeutic agent. Molecular dynamics (MD) simulations were performed using Desmond to assess the stability and persistence of Diosmetin binding with the CYP enzymes over a 200 ns simulation period. Molecular docking studies revealed robust binding affinities between Diosmetin and CYP17A1 (- 11.261 kcal/mol) and CYP19A1 (- 11.145 kcal/mol). Diosmetin demonstrated favorable pharmacokinetic properties and advantageous ADMET characteristics, including high bioavailability, good dispersion, and favorable metabolism. MD simulations indicated persistent binding interactions between Diosmetin and the CYP enzymes throughout the 200 ns simulation, reinforcing the reliability of these interactions. Pharmacoinformatics investigations provide valuable insights into the potential of Diosmetin as a promising lead compound for the development of novel drug candidates against prostate cancer. The strong binding affinity and favorable pharmacokinetic and ADMET profiles suggest that Diosmetin could be an effective CYP antagonist and warrants further investigation as a potential therapeutic agent for prostate cancer.

Identification of novel inhibitors of cancer target telomerase using a dual structure-based pharmacophore approach to virtually screen libraries, molecular docking and validation by molecular dynamics simulations

In about 85% of cancer malignancies, replicative immortality caused by increased telomerase activity makes it an attractive target for developing anticancer therapeutics. However, the lack of approved small-molecule inhibitors rooted in the structural ambiguity of telomerase has impeded drug development for decades. In this study, we have exploited the FVYL pocket in the thumb domain, which plays a key role in the enzyme's processivity. Due to the unavailability of a co-crystalized structure of BIBR1532 with the catalytic hTERT thumb domain, we utilized the molecular dynamics method to identify the precise binding site of the inhibitor. Two pharmacophore models were generated and validated for the putative (Site-I) and newly identified (Site-II) binding pockets which were screened virtually through the ChemDiv anticancer library, Otava drug-like green collection to identify novel lead compounds, and Binding database to screen out thumb domain-specific telomerase inhibitors. The top hits obtained were filtered using drug-likeliness parameters followed by redocking using a three-level screening strategy into their binding site. The structural investigation, molecular docking studies, and confirmatory molecular dynamics revealed that the exact binding site of BIBR1532 is away from the reported FVYL pocket with characteristic interactions conserved. Subsequently, the lead compounds with the highest docking scores and significant interactions in the newly discovered extended FVYL pocket were validated using 100 ns MD simulations. Additionally, cross-validated binding free energy calculations were performed using MM-PB(GB)SA methods followed by PCA and FEL characterization. The identified top lead compounds can be validated in vitro and taken forward for anticancer drug development.

Rationalize the Functional Roles of Protein-Protein Interactions in Targeted Protein Degradation by Kinetic Monte Carlo Simulations

Targeted protein degradation is a promising therapeutic strategy to tackle disease-causing proteins that lack binding pockets for traditional small-molecule inhibitors. Its first step is to trigger the proximity between a ubiquitin ligase complex and a target protein through a heterobifunctional molecule, such as proteolysis targeting chimeras (PROTACs), leading to the formation of a ternary complex. The properties of protein-protein interactions play an important regulatory role during this process, which can be reflected by binding cooperativity. Unfortunately, although computer-aided drug design has become a cornerstone of modern drug development, the endeavor to model-targeted protein degradation is still in its infancy. The development of computational tools to understand the impacts of protein-protein interactions on targeted protein degradation, therefore, is highly demanded. To reach this goal, we constructed a nonredundant structural benchmark of the most updated ternary complexes and applied a kinetic Monte Carlo method to simulate the association between ligases and PROTAC-targeted proteins in the benchmark. Our results show that proteins in most complexes with positive cooperativity tend to associate into native-like configurations more often. In contrast, proteins very likely failed to associate into native-like configurations in complexes with negative cooperativity. Moreover, we compared protein-protein association through different interfaces generated from molecular docking. The native-like binding interface shows a higher association probability than all the other alternative interfaces only in the complex with positive cooperativity. These observations support the idea that the formation of ternary complexes is closely regulated by the binary interactions between proteins. Finally, we applied our method to cyclin-dependent kinases 4 and 6 (CDK4/6). We found that their interactions with the ligase are not as similar as their structures. Altogether, our study paves the way for understanding the role of protein-protein interactions in the PROTAC-induced ternary complex formation. It can potentially help in searching for degraders that selectively target specific proteins.

Natural anti-adhesive components against pathogenic bacterial adhesion and infection in gastrointestinal tract: case studies of Helicobacter pylori, Salmonella enterica, Clostridium difficile, and diarrheagenic Escherichia coli

Antimicrobial resistance (AMR) poses a global public health concern. Recognizing the critical role of bacterial adhesion in pathogenesis of infection, anti-adhesive therapy emerges as a promising approach to impede initial bacterial attachment, thus preventing pathogenic colonization and infection. Natural anti-adhesive agents derived from food sources are generally safe and have the potential to inhibit the emergence of resistant bacteria. This comprehensive review explored diverse natural dietary components exhibiting anti-adhesive activities against several model enteric pathogens, including Helicobacter pylori, Salmonella enterica, Clostridium difficile, and three key diarrheagenic Escherichia coli (i.e., enterotoxigenic E. coli, enteropathogenic E. coli, and enterohemorrhagic E. coli). Investigating various anti-adhesive products will advance our understanding of current research of the field and inspire further development of these agents as potential nutraceuticals or adjuvants to improve the efficacy of conventional antibiotics.

Identification of Novel Drug Molecules Against NS3-Like Helicase Enzyme of Alongshan Virus

Alongshan virus (ALSV) is a novel tick-borne virus associated with human diseases. The ALSV is a segmented flavivirus from the family Flaviviridae. It is currently considered as tick-borne arbovirus. There is a high incidence of fever and headache among patients with ALSV infection, and some patients also present with fatigue, coma, depression, nausea, myalgia/arthralgia, and skin rashes. Neither a licensed vaccine nor a drug is currently available to treat ALSV. The development of new, practical, and innovative therapeutic approaches is needed to overcome the emergence of the pathogen. Research on drugs remains a complex, time-consuming, and expensive. The field of drug development has undergone a revolution due to the use of computational approaches, which provide several benefits that speed up and improve the process of developing novel drugs. The goal of this study is to identify novel drug-like molecules against NS3-like helicase enzyme of Alongshan virus. Using molecular docking, the binding potential of the top three ligands to the specified target was determined. Molecular dynamic simulations were used to identify the stabilities of the best-docked conformations followed by energy calculations and ADMET analysis. Three potential and promising compounds were identified by performing structure-based virtual screening of non-structural protein 3 (NS3) like helicase of Alongshan virus. The best-docked complexes identified through virtual screening were BDC-23169381, BDB-26412846, BDB-2641954. All these compounds had good pharmacokinetics characteristics and were identified as drug like.

Physiologically-based pharmacokinetic model of in vitro porcine ear skin permeation for drug delivery research

In silico techniques, such as physiologically based pharmacokinetic modeling (PBKP), are recently gaining importance. Computational methods in drug discovery and development and the generic drugs industry enhance research effectiveness by saving time and money and avoiding ethical issues. One key advantage is the ability to conduct toxicology studies without risking harm to living beings. This study aimed to repurpose the multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) PBPK model for simulation permeation through porcine ear skin under in vitro conditions. The work was divided into four steps: (1) the development of a pig ear skin model based on a previously collected dataset; (2) testing the model's ability to discriminate permeation between pig ear, human abdomen, and human back skin; (3) development of a caffeine permeation model; and (4) testing the caffeine model's performance against in vitro generated data sourced from the scientific literature. Data from 31 manuscripts were used for the development of the pig skin model. Based on these data, values specific to pig skin were found for 22 parameters of the MPML MechDermA model. The model was able to discriminate permeation between pig and human skin. A caffeine model was developed and used to simulate seven experiments identified in the literature. The model's performance was assessed by comparing simulated to observed results. Based on a visual check, all simulations were considered acceptable, whereas three out of seven experiments met the twofold difference criterion. The variability of the experimental data was considered the biggest challenge for reliable model assessment.

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.

Neuroprotective Properties of Coriander-Derived Compounds on Neuronal Cell Damage under Oxidative Stress-Induced SH-SY5Y Neuroblastoma and in Silico ADMET Analysis

An imbalance between reactive oxygen species (ROS) production and antioxidant defense driven by oxidative stress and inflammation is a critical factor in the progression of neurodegenerative diseases such as Alzheimer's and Parkinson's. Coriander (Coriandrum sativum L.), a culinary plant in the Apiaceae family, displays various biological activities, including anticancer, antimicrobial, and antioxidant effects. Herein, neuroprotective properties of three major bioactive compounds derived from coriander (i.e., linalool, linalyl acetate, and geranyl acetate) were investigated on hydrogen peroxide-induced SH-SY5Y neuroblastoma cell death by examining cell viability, ROS production, mitochondrial membrane potential, and apoptotic profiles. Moreover, underlying mechanisms of the compounds were determined by measuring intracellular sirtuin 1 (SIRT1) enzyme activity incorporated with molecular docking. The results showed that linalool, linalyl acetate, and geranyl acetate elicited their neuroprotection against oxidative stress via protecting cell death, reducing ROS production, preventing cell apoptosis, and modulating SIRT1 longevity. Additionally, in silico pharmacokinetic predictions indicated that these three compounds are drug-like agents with a high probability of absorption and distribution, as well as minimal potential toxicities. These findings highlighted the potential neuroprotective linalool, linalyl acetate, and geranyl acetate for developing alternative natural compound-based neurodegenerative therapeutics and prevention.

In silico exploration of phytochemicals as inhibitors for acute myeloid leukemia by targeting LIN28A gene: A cheminformatics study

BACKGROUND

Recent discoveries have illustrated that Lin28A is an oncogene in various cancers, particularly acute myeloid leukemia (AML). The upregulation of Lin28A can actively contribute to tumorigenesis and migration processes in multiple organs. Hence, the inhibition of Lin28A can be achieved by applying phytochemical herbals and targeting Lin28A protein using a computer-aided drug design (CAAD) approach.

METHODS

In this study, we comprehensively applied several bioinformatics tools, including gene ontologies, gene enrichment analysis, and protein-protein interactions (PPI), to determine the biological pathways, functional gene ontology, and biological pathway. Furthermore, we investigated a list of phytochemical herbs as a candidate drug by applying a computation technique involving molecular docking, density functional theory (DFT), molecular dynamics simulation (MDs), and pharmacokinetic and physiochemical properties by applying the SwissADME, pkCSM, and Molsoft LLC web-servers.

RESULTS

The Lin28A gene is related to two significant enrichment pathways, including proteoglycans in cancer and the pluripotency of stem cells through interactions with different genes such as MAPK12, MYC, MTOR, and PIK3CA. Interestingly, limonin, 18β Glycyrrhetic Acid, and baicalein have the highest binding energy scores of -8.4, -8.2, and -7.3 kcal/mol, respectively. The DFT study revealed that baicalein has a higher reactivity than limonin and 18β-Glycyrrhetic due to a small energy gap between LUMO and HUMO. Molecular dynamics simulation exhibited that baicalein complex with Lin28A protein is more stable than other complexes during simulation time due to low fluctuation with simulation periods as compared with other complexes, which indicated that baicalein was more fitting to docking and combining in the protein cave because of the largest number of H-bonds available for the docking simulation process. Furthermore, the drug-likeness and ADMET profiles revealed the activity of limonin, baicalein, and 18β-glycyrrhizic Acid, which possess significant inhibiting Lin28A proteins.

CONCLUSION

This study elucidated that baicalein, 18β-glycyrrhizic, and limonin may be applied as potential candidates for targeting Lin28A as an active oncogene for acute myeloid leukemia.

Protein-Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions

Quantum-chemical fragmentation methods offer an attractive approach for the accurate calculation of protein-ligand interaction energies. While the molecular fractionation with conjugate caps (MFCC) scheme offers a rather straightforward approach for this purpose, its accuracy is often not sufficient. Here, we upgrade the MFCC scheme for the calculation of protein-ligand interactions by including many-body contributions. The resulting fragmentation scheme is an extension of our previously developed MFCC-MBE(2) scheme [J. Comput. Chem. 2023, 44, 1634-1644]. For a diverse test set of protein-ligand complexes, we demonstrate that by upgrading the MFCC scheme with many-body contributions, the error in protein-ligand interaction energies can be reduced significantly, and one generally achieves errors below 20 kJ/mol. Our scheme allows for systematically reducing these errors by including higher-order many-body contributions. As it combines the use of single amino acid fragments with high accuracy, our scheme provides an ideal starting point for the parametrization of accurate machine learning potentials for proteins and protein-ligand interactions.