Computational approaches in target identification and drug discovery

Pioneering Computational Culture Within Pharmacy Schools by Empowering Students With Data Science and Bioinformatics Skills

As advancements in digital health lead to the generation of increasingly diverse and voluminous pharmaceutical data, it is increasingly critical that we teach trainee pharmaceutical scientists how to leverage this data to lead future innovations in health care and pharmaceutical research. To address this need, the University of Southern California Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences is incorporating data science and bioinformatics into the graduate and undergraduate curricula through introductory courses tailored for students without prior programming experience. These courses feature a teaching framework designed to make the fundamentals of data science and bioinformatics accessible to pharmacy students through step-by-step, Jupyter-based coding assignments with examples relevant to the pharmaceutical sciences. The framework supports Doctor of Pharmacy students by focusing on the practical applications of data science in clinical settings, while for Doctor of Philosophy (PhD) and Master's (MS) students, the emphasis is on research methodologies and advanced data analysis techniques. Here, we outline the design of this framework, highlighting the strategies we developed and the opportunities it provides to cultivate a computational culture within our institution and beyond.

A novel hypothesis-generating computational workflow utilizing reverse pharmacophore mapping-A drug repurposing perspective of istradefylline towards major depressive disorder

BACKGROUND AND PURPOSE

Drug repurposing (DR) offers a compelling alternative to traditional drug discovery's lengthy, resource-intensive process. DR is the process of identifying alternative clinical applications for pre-approved drugs as a low-risk and low-cost strategy. Computational approaches are crucial during the early hypothesis-generating stage of DR. However, 'large-scale' data retrieval remains a significant challenge. A computational workflow addressing such limitations might improve hypothesis generation, ultimately benefit patients and advance DR research.

EXPERIMENTAL APPROACH

We introduce a novel computational workflow (combining free-accessible computational platforms) to provide 'proof-of-concept' of the pre-approved drug's suitability for repurposing. Three key phases are included: target fishing (via reverse pharmacophore mapping), target identification (via disease- and drug-target pathway identification) and retrospective literature and drug-like analysis (via in silico ADMET properties determination). Istradefylline is a Parkinson's disease-approved drug with literature-attributed antidepressant properties remaining unclear. Practically applied, istradefylline's antidepressant activity was assessed in the context of major depressive disorder (MDD).

KEY RESULTS

Data mining aided by target identification resulted in istradefylline potentially representing a novel antidepressant drug class. Retrieved drug targets (KYNU, MAO-B, ALOX12 and PLCB2) associated with selected MDD pathways (tryptophan metabolism and serotonergic synapse) generated a hypothesis that istradefylline increased extracellular 5-HT levels (MAO-B inhibition) and reduced inflammation (KYNU, ALOX12 and PLCB2 inhibition).

CONCLUSION AND IMPLICATIONS

The practically applied workflow's generated hypothesis aligns with known experimental data, validating the effectiveness of this novel computational workflow. It is a low-risk and low-cost DR computational tool providing a bird's-eye view for exploring alternative clinical applications of pre-approved drugs.

Targeting cyclin-dependent kinase 11: a computational approach for natural anti-cancer compound discovery

Cancer, a leading global cause of death, presents considerable treatment challenges due to resistance to conventional therapies like chemotherapy and radiotherapy. Cyclin-dependent kinase 11 (CDK11), which plays a pivotal role in cell cycle regulation and transcription, is overexpressed in various cancers and is linked to poor prognosis. This study focused on identifying potential inhibitors of CDK11 using computational drug discovery methods. Techniques such as pharmacophore modeling, virtual screening, molecular docking, ADMET predictions, molecular dynamics simulations, and binding free energy analysis were applied to screen a large natural product database. Three pharmacophore models were validated, leading to the identification of several promising compounds with stronger binding affinities than the reference inhibitor. ADMET profiling indicated favorable drug-like properties, while molecular dynamics simulations confirmed the stability and favorable interactions of top candidates with CDK11. Binding free energy calculations further revealed that UNPD29888 exhibited the strongest binding affinity. In conclusion, the identified compound shows potential as a CDK11 inhibitor based on computational predictions, suggesting their future application in cancer treatment by targeting CDK11. These computational findings encourage further experimental validation as anti-cancer agents.

Plasmepsin II inhibitory potential of phytochemicals isolated from African antimalarial plants: a computational approach

Plamepsin II has been identified as a therapeutic target in the Plasmodium falciparum's life cycle and may lead to a drastic reduction in deaths caused by malaria worldwide. Africa flora is rich in medicinal qualities and possesses both simple and complex bioactive phytochemicals. This study utilized computational approaches like molecular docking, molecular dynamics simulation, quantum chemical calculations and ADMET to evaluate the plasmepsin II inhibitory properties of phytochemicals isolated from African antimalarial plants. Molecular docking was carried out to estimate the binding affinity of 229 phytochemicals whereby ekeberin A, dichamanetin, 10-hydroxyusambaresine, chamuvaritin and diuvaretin were selected. Further, RMSD and RMSF plots from the 100 ns simulation results showed that the screened phytochemicals were stable in the enzyme's binding pocket. The quantum chemical calculation revealed that all the phytochemicals are strong electrophiles, while ekeberin A was identified as the most stable and dichamanetin as the most reactive. Also, ADMET studies established the drug candidacy of the phytochemicals. Thus, these phytochemicals could act as good antimalarial agents after extensive in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

DysRegNet: Patient-specific and confounder-aware dysregulated network inference towards precision therapeutics

BACKGROUND AND PURPOSE

Gene regulation is frequently altered in diseases in unique and patient-specific ways. Hence, personalised strategies have been proposed to infer patient-specific gene-regulatory networks. However, existing methods do not scale well because they often require recomputing the entire network per sample. Moreover, they do not account for clinically important confounding factors such as age, sex or treatment history. Finally, a user-friendly implementation for the analysis and interpretation of such networks is missing.

EXPERIMENTAL APPROACH

We present DysRegNet, a method for inferring patient-specific regulatory alterations (dysregulations) from bulk gene expression profiles. We compared DysRegNet to the well-known SSN method, considering patient clustering, promoter methylation, mutations and cancer-stage data.

KEY RESULTS

We demonstrate that both SSN and DysRegNet produce interpretable and biologically meaningful networks across various cancer types. In contrast to SSN, DysRegNet can scale to arbitrary sample numbers and highlights the importance of confounders in network inference, revealing an age-specific bias in gene regulation in breast cancer. DysRegNet is available as a Python package (https://github.com/biomedbigdata/DysRegNet_package), and analysis results for 11 TCGA cancer types are available through an interactive web interface (https://exbio.wzw.tum.de/dysregnet).

CONCLUSION AND IMPLICATIONS

DysRegNet introduces a novel bioinformatics tool enabling confounder-aware and patient-specific network analysis to unravel regulatory alteration in complex diseases.

Identification of molecular targets of Trigonelline for treating breast cancer through network pharmacology and bioinformatics-based prediction

Breast cancer, a highly prevalent and fatal cancer that affects the female population worldwide, stands as a significant health challenge. Despite the abundance of chemotherapy drugs, the adverse side effects associated with them have initiated an investigation into natural plant-based compounds. Trigonelline, an alkaloid found in Trigonella foenum-graecum, was previously reported for its anticancer properties by the researchers. In this present study, we have identified the molecular targets of Trigonelline in breast cancer and predicted its drug-like properties and toxicity. By analyzing breast cancer targets from databases including TTD, TCGA, Gene cards, and Trigonelline targets obtained from CTD, we identified 14 specific targets of Trigonelline in the context of breast cancer. The protein-protein interaction (PPI) network of the 14 Trigonelline targets provided insights into the complex relationships between different genes and targets. Heatmap analysis demonstrated the expression patterns of these 14 genes at the protein and RNA levels in breast cancer cells and breast tissues. Notably, four genes, namely EGF, BAX, EGFR, and MTOR, were enriched in the breast cancer pathway. At the same time, PARP1, DDIT3, BAX, and TNF were associated with the apoptosis pathway according to KEGG pathway enrichment analyses. Molecular docking studies between Trigonelline and target proteins from the Protein Data Bank (PDB) revealed favorable binding affinity. Furthermore, mutation analysis of target genes within a dataset of 1918 samples from cBioPortal revealed the absence of mutations. Remarkably, Trigonelline also exhibited binding affinity towards two mutant proteins, and based on these findings, we predicted that Trigonelline could be utilized to target breast cancer genes and their mutants through network pharmacology. Additionally, this was supported by molecular dynamic simulation studies. As our study is preliminary, further validation through in vitro and in vivo studies is essential to confirm the efficacy of Trigonelline in breast cancer treatment.

Indian Medicinal Plant-Derived Phytochemicals as Potential Antidotes for Snakebite: A Pharmacoinformatic Study of Atrolysin Inhibitors

Snakebite envenoming is a significant health threat, particularly in tropical regions, causing substantial morbidity and mortality. Traditional treatments, including antivenom therapy, have limitations and associated risks. This research aims to discover novel phytochemical antidotes for snakebites, specifically targeting the western diamondback rattlesnake (Crotalus atrox) venom metalloproteinase Atrolysin. Utilizing pharmacoinformatic techniques such as molecular docking, high-throughput ligand screening, pharmacophore mapping, pharmacokinetic profiling, and molecular dynamics (MD) simulations, we analyzed phytochemicals from the Indian Medicinal Plants, Phytochemistry And Therapeutics (IMPPAT) database alongside well-known nine metalloproteinase inhibitors from the PubChem database. From an initial set of 17,967 compounds, 4708 unique compounds were identified for further study. These compounds were evaluated based on drug likeness, molecular descriptors, ADME properties, and toxicity profiles. Binding site predictions and molecular docking identified key interacting residues and binding energies, highlighting several promising compounds. Density functional theory (DFT) analysis provided insights into these compounds' electronic properties and stability. MD simulations assessed the dynamic stability of protein-ligand complexes using parameters such as RMSD, RMSF, the radius of gyration, and hydrogen bond interactions. This study identified top candidates, including CID5291, IMPHY001495, IMPHY014737, IMPHY008983, IMPHY008176, and IMPHY003833, based on their favorable binding energies, interaction forces, and structural stability. These findings suggest that the selected phytochemicals have the potential to serve as effective alternatives to traditional antivenom treatments, offering a promising avenue for further research and development in snakebite management.

Computer-Aided Discovery of Abrus precatorius Compounds With Anti-Schistosomal Potential

Schistosomiasis, which causes over 200 000 deaths annually, has since the 1970s been controlled by praziquintel. The reliance on a single drug to combat schistosomiasis, and reports of laboratory resistance to the drug, has created an urgent need in the scientific community to develop new chemotherapies to complement or supplement praziquantel. Medicinal plants are a potential reservoir of compounds with schistosomicidal activity. In the current study, we carried out computer-aided screening of Abrus precatorius compounds to discover compounds with potential to inhibit Schistosoma mansoni purine nucleoside phosphorylase (SmPNP). Thus, 99 compounds retrieved from Lotus Natural Compounds Database were docked into the active site of SmPNP. The top-ranked compounds were subjected to Lipinski's druglikeness and toxicity risk predictions. Three lead compounds, abrusogenin, cirsimaritin and hispidulin, were identified as having high binding affinities, favourable interactions with SmPNP active site residues and good toxicity risk prediction results. Molecular dynamics (MD) simulations were used to assess the stability of the interactions of these lead compounds with SmPNP. Collectively, analyses of the MD trajectories confirms that the lead compounds bound and interacted stably with active site residues of SmPNP. We conclude that abrusogenin, cirsimaritin and hispidulin could serve as hit compounds for the development of new antischistosomal drugs, based on plant-derived natural products. However, experimental studies are required to further evaluate the potentials of these compounds as possible therapeutics against schistosomiasis.

Identification of FOXM1 as a novel protein biomarker and therapeutic target for colorectal cancer progression: Evidence from immune infiltration and bioinformatic analyses

Colorectal cancer (CRC) remains a major global health challenge, with its underlying molecular mechanisms, particularly the role of FOXM1, not yet fully understood. This study employed an integrated approach combining bioinformatics along with experimental validation to explore the role of FOXM1 in CRC. Using advanced computational tools and experimental techniques, we aimed to clarify the biological significance of FOXM1 and its potential impact on CRC progression and treatment. Bioinformatic analyses, including pan-cancer views, mRNA expression analysis, immune infiltrations, pathway enrichment, and functional annotations, highlighted the oncogenic potential of FOXM1 in CRC. Protein and gene expression analyses (western blot and qPCR) were conducted in HCT-116 and HT-29 cells. Platforms like GEPIA and UALCAN confirmed the diagnostic relevance of FOXM1, showing upregulated mRNA expression across various stages and metastasis. The influence of FOXM1 on immune cells, particularly CD4+, CD8+, and B cells, was significant, as revealed by immunohistochemistry. Protein-protein interaction analysis through STRING and CYTOSCAPE identified genes closely linked to FOXM1 in CRC. KEGG pathway enrichment suggested FOXM1's involvement in the p53 pathway, reinforcing its role in oncogenesis. Experimental validation confirmed elevated FOXM1 expression in HCT-116 and HT-29 cells. In summary, this study indicates that targeting FOXM1 could be a promising therapeutic strategy in CRC, emphasizing its importance in the molecular landscape of cancer progression.

Medicinal plants as a potential resource for the discovery of novel structures towards cancer drug resistance treatment

Despite extensive research in chemotherapy, global cancer concerns persist, exacerbated by the challenge of drug resistance, which imposes economic and medical burdens. Natural compounds, particularly secondary metabolites from medicinal plants, present promising avenues for overcoming cancer drug resistance due to their diverse structures and essential pharmacological effects. This review provides a comprehensive exploration of cancer cell resistance mechanisms and target actions for reversing resistance and highlights the in vitro and in vivo efficacy of noteworthy alkaloids, flavonoids, and other compounds, emphasizing their potential as therapeutic agents. The molecular properties supporting ligand interactions are thoroughly examined, providing a robust theoretical foundation. The review concludes by discussing methods including quantitative structure-activity relationships and molecular docking, offering insights into screening potential candidates. Current trends in clinical treatment, contributing to a holistic understanding of the multifaceted approaches to address cancer drug resistance are also outlined.

Aplospojaveedins A-C, unusual sulfur-containing alkaloids produced by the endophytic fungus Aplosporella javeedii using OSMAC strategy

Three sulfur-containing alkaloids aplospojaveedins A-C (1-3) with a hitherto undescribed carbon skeleton comprising octahy-dronaphthalene, α, β-unsaturated lactam and glycine-cysteine moieties were isolated from Aplosporella javeedii. Their structures were elucidated by 1D and 2D NMR spectroscopy, HR-MS, X-ray diffraction analysis, DFT-NMR and TDDFT-ECD calculations. A plausible biosynthetic pathway and putative targets are described. The blind docking suggested that 1-3 may have functional effects on several putative targets such as the GPCR cannabinoid receptor 2 or the integrin α5β1 complex.

Identification of Small-Molecule Antagonists Targeting the Growth Hormone Releasing Hormone Receptor (GHRHR)

The growth hormone-releasing hormone receptor (GHRHR) belongs to Class B1 of G protein-coupled receptors (GPCRs). Class B1 GPCR peptides such, as growth hormone-releasing hormone (GHRH), have been proposed to bind in a two-step model, where first the C-terminal region of the peptide interacts with the extracellular domain of the receptor and, subsequently, the N-terminus interacts with the seven transmembrane domain of the receptor, resulting in activation. The GHRHR has recently been highlighted as a promising drug target toward several types of cancer and has been shown to be overexpressed in prostate, breast, pancreatic, and ovarian cancer. Indeed, peptide GHRHR antagonists have displayed promising results in many cancer models. However, no nonpeptide GHRHR-targeting compounds have yet been identified. We have utilized several computational tools to target GHRHR and identify potential small-molecule compounds directed at this receptor. These compounds were validated in vitro using a cyclic adenosine monophosphate (cAMP) ELISA to measure activity at the GHRHR. In vitro results suggest that several of the novel small-molecule compounds could inhibit GHRH-induced cAMP accumulation. Preliminary analysis of the specificity/selectivity of one of the most effective hit compounds indicated that the effect seen was via inhibition of the GHRHR. We therefore report the first nonpeptide antagonists of GHRHR and propose a structural basis for inhibition induced by the compounds, which may assist in the future design of lead GHRHR compounds for treating disorders attributed to dysregulated/aberrant GHRHR signaling.

Sedative Effects of Daidzin, Possibly Through the GABAA Receptor Interaction Pathway: In Vivo Approach with Molecular Dynamic Simulations

The soy isoflavone daidzin (DZN) has been considered a hopeful bioactive compound having diverse biological activities, including anxiolytic, memory-enhancing, and antiepileptic effects, in experimental animals. However, its sedative and hypnotic effects are yet to be discovered. This study aimed to evaluate its sedative/hypnotic effect on Swiss mice. Additionally, in silico studies were also performed to see the possible molecular mechanisms behind the tested neurological effect. For this, male Swiss albino mice were treated with DZN (5, 10, or 20 mg/kg) intraperitoneally (i.p.) with or without the standard GABAergic medication diazepam (DZP) and/or flumazenil (FLU) and checked for the onset and duration of sleeping time using thiopental sodium-induced as well as DZP-induced sleeping tests. A molecular docking study was also performed to check its interaction capacity with the α1 and β2 subunits of the GABAA receptor. Findings suggest that DZN dose-dependently and significantly reduced the latency while increasing the duration of sleep in animals. In combination therapy, DZN shows synergistic effects with the DZP-2 and DZP-2 + FLU-0.01 groups, resulting in significantly (p < 0.05) reduced latency and increased sleep duration. Further, molecular docking studies demonstrate that DZN has a strong binding affinity of - 7.2 kcal/mol, which is closer to the standard ligand DZP (- 8.3 kcal/mol) against the GABAA (6X3X) receptor. Molecular dynamic simulations indicated stability and similar binding locations for DZP and DZN with 6X3X. In conclusion, DZN shows sedative effects on Swiss mice, possibly through the GABAA receptor interaction pathway.

Identification of potential dipeptidyl peptidase IV inhibitors from the ConMedNP library by virtual screening, and molecular dynamics methods

In this study, we screened novel dipeptidyl peptidase IV (DPP4) inhibitors from the ConMedNP library consisting of 3507 molecules. Interestingly, molecular docking, ADMET, and the anti-diabetic activity predictions suggest that three molecules, namely OTH_UD_XX06_1, GB19, and BMC_000104, have a high binding affinity toward DPP4. The molecular dynamics (MD) simulation results suggest that these hit molecules have a stable binding pose and occupy the binding pockets throughout the 200 ns simulation. The presence of intermolecular H-bonding between the ligands and DPP4 was observed throughout the simulation period. Thus, docking and MD results, predicted that the three compounds were the most potent DPP4 inhibitors that could putatively bind to the DPP4 active site via both conventional H-bonding and hydrophobic interactions. These results could aid the discovery of new drugs to treat type 2 diabetes.

Investigating the inhibitory potential of natural bioactive compounds against cyclin-dependent kinase 13: virtual high throughput screening and MD simulation studies to target CDK signaling

Cyclin-dependent kinase 13 (CDK13) belongs to the cyclin-dependent kinase (CDK) family that is actively involved in transcription regulation and RNA splicing. CDK13 binds with its partner, cyclin K, to regulate several biological processes. CDK13 and cyclin K complex phosphorylates RNA pol II carboxyl-terminal domain (CTD) at several serine residues, creating transcription elongation. The upregulation of the kinase contributes to tumor growth and cell proliferation, and is highly associated with various cancers, including skin, stomach, and ovarian. Thus, it can be considered an efficient therapeutic target for the development of drugs against cancer. In this work, a virtual high throughput screening (vHTS) of the ZINC library was carried out to elucidate the initial potent compounds. Further, filters were applied to identify the hit compounds among the ∼90,000 compound library. Based on the docking scores and binding affinity, the top 100 hits were elucidated, and they were further narrowed down to 50 compounds based on ADMET and Lipinski's RO5 filter. Finally, 10 compounds were chosen that showed appreciable biological activity. Among them, ZINC02136558 was selected as a potent lead compound that showed strong interaction with the amino acid residues of active and binding sites of CDK13. Furthermore, the all-atom molecular dynamic simulation was performed at 200 ns to explore the dynamic evolution of the system. Finally, the results showed that the ZINC02136558 may be considered as a potential lead molecule to inhibit CDK13 and implicated in therapeutic management of cancer and associated diseases.

AI-assisted chemistry research: a comprehensive analysis of evolutionary paths and hotspots through knowledge graphs

Artificial intelligence (AI) offers transformative potential for chemical research through its ability to optimize reactions and processes, enhance energy efficiency, and reduce waste. AI-assisted chemical research (AI + chem) has become a global hotspot. To better understand the current research status of "AI + chem", this study conducted a scientific bibliometric investigation using CiteSpace. The web of science core collection was utilized to retrieve original articles related to "AI + chem" published from 2000 to 2024. The obtained data allowed for the visualization of the knowledge background, current research status, and latest knowledge structure of "AI + chem". The "AI + chem" has entered a stage of explosive growth, and the number of papers will maintain long-term high-speed growth. This article systematically analyzes the latest progress in "AI + chem" and objectively predicts future trends, including molecular design, reaction prediction, materials design, drug design, and quantum chemistry. The outcomes of this study will provide readers with a comprehensive understanding of the overall landscape of "AI + chem".

Hybrid Caffeic Acid-Based DHFR Inhibitors as Novel Antimicrobial and Anticancer Agents

A novel series of 1,2,4-triazole analogues of caffeic acid was designed, synthesized, characterized, and assessed for their capacity to inhibit DHFR, as well as their anticancer and antimicrobial properties. A molecular docking analysis was conducted on DHFR, utilizing PDB IDs 1U72 and 2W9S, aiming to design anticancer and antimicrobial drugs, respectively. Among all the synthesized derivatives, compound CTh7 demonstrated the highest potency as a DHFR inhibitor, with an IC50 value of 0.15 μM. Additionally, it exhibited significant cytotoxic properties, with an IC50 value of 8.53 µM. The molecular docking analysis of the CTh7 compound revealed that it forms strong interactions with key residues of homo sapiens DHFR such as Glu30, Phe34, Tyr121, Ile16, Val115, and Phe31 within the target protein binding site and displayed excellent docking scores and binding energy (-9.9; -70.38 kcal/mol). Additionally, synthesized compounds were screened for antimicrobial properties, revealing significant antimicrobial potential against bacterial strains and moderate effects against fungal strains. Specifically, compound CTh3 exhibited notable antibacterial efficacy against Staphylococcus aureus (MIC = 5 µM). Similarly, compound CTh4 demonstrated significant antibacterial activity against both Escherichia coli and Pseudomonas aeruginosa, with MIC values of 5 µM for each. A docking analysis of the most active antimicrobial compound CTh3 revealed that it forms hydrogen bonds with Thr121 and Asn18, a π-cation bond with Phe92, and a salt bridge with the polar residue Asp27.

Chemoinformatics and machine learning techniques to identify novel inhibitors of the lemur tyrosine kinase-3 receptor involved in breast cancer

Breast cancer is still the largest cause of cancer death in women, and around 70% of primary breast cancer patients are estrogen receptor (ER)-positive, which is the most frequent kind of breast cancer. The lemur tyrosine kinase-3 (LMTK3) receptor has been linked to estrogen responsiveness in breast cancer. However, the function of LMTK3 in reaction to cytotoxic chemotherapy has yet to be studied. Breast cancer therapy research remains tricky due to a paucity of structural investigations on LMTK3. We performed structural investigations on LMTK3 using molecular docking and molecular dynamics (MD) simulations of the LMTK3 receptor in complex with the top three inhibitor molecules along with a control inhibitor. Analysis revealed the top three compounds show the best binding affinities during docking simulations. Interactive analysis of hydrogen bonds inferred hotspot residues Tyr163, Asn138, Asp133, Tyr56, Glu52, Ser132, Asp313, and Asp151. Some other residues in the 5-Å region determined strong alkyl bonds and conventional hydrogen bond linkages. Furthermore, protein dynamics analysis revealed significant modifications among the top complexes and the control system. There was a transition from a loop to a-helix conformation in the protein-top1 complex, and in contrast, in complexes top2 and top3, the formation of a stabilizing sheet in the C chain was observed, which limited significant mobility and increased complex stability. Significant structural alterations were observed in the protein-top complexes, including a shorter helix region and the creation of some loop regions in comparison to the control system. Interestingly, binding free energies, including MMGB/PBSA WaterSwap analysis estimation, reveals that the top1 complex system was more stable than other systems, especially in comparison to the control inhibitor complex system. These results suggest a the plausible mode of action for the novel inhibitors. Therefore, the current investigation contributes to understanding the mechanism of action, serving as a basis for future experimental studies.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target

Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).

Targeting human progesterone receptor (PR), through pharmacophore-based screening and molecular simulation revealed potent inhibitors against breast cancer

Breast cancer, the prevailing malignant tumor among women, is linked to progesterone and its receptor (PR) in both tumorigenesis and treatment responsiveness. Despite thorough investigation, the precise molecular mechanisms of progesterone in breast cancer remain unclear. The human progesterone receptor (PR) serves as an essential therapeutic target for breast cancer treatment, warranting the rapid design of small molecule therapeutics that can effectively inhibit HPR. By employing cutting-edge computational techniques like molecular screening, simulation, and free energy calculation, the process of identifying potential lead molecules from natural products has been significantly expedited. In this study, we employed pharmacophore-based virtual screening and molecular simulations to identify natural product-based inhibitors of human progesterone receptor (PR) in breast cancer treatment. High-throughput molecular screening of traditional Chinese medicine (TCM) and zinc databases was performed, leading to the identification of potential lead compounds. The analysis of binding modes for the top five compounds from both database provides valuable structural insights into the inhibition of HPR for breast cancer treatment. The top five hits exhibited enhanced stability and compactness compared to the reference compound. In conclusion, our study provides valuable insights for identifying and refining lead compounds as HPR inhibitors.

The Effects of Ellagic Acid on Experimental Corrosive Esophageal Burn Injury

This study aimed to investigate the antioxidant effect of Ellagic acid (EA) on wound healing in sodium hydroxide (NaOH)-induced corrosive esophageal burn injury. The interaction networks and functional annotations were conducted using Cytoscape software. A total of 24 Wistar albino rats were divided into control, corrosive esophageal burn (CEB) and CEB + EA groups. Burn injury was created by 20% NaOH and 30 mg/kg EA was per oral administered to rats. At the end of the 28-day experimental period, Malondialdehyde (MDA) content was measured. Esophageal tissue samples were processed for histological staining. The EA-target interaction network was revealed to be involved in regulating crucial cellular mechanisms for burn wound healing, with epidermal growth factor (EGF) identified as a central mediator. An increase in animal weight in the CEB + EA group was observed in the EA-treated group after CEB injury. Burn injury increased MDA content, but EA treatment decreased its level after CEB injury. Stenosis index, collagen degeneration, inflammation, fibrosis and necrosis levels were increased after CEB injury. EA treatment improved histopathology in the CEB + EA group compared to the CEB group. The expression of EGF was decreased in the CEB group but upregulated in the EA-treated group, suggesting a potential involvement of EA in cellular processes and tissue regeneration. EA, through its antioxidative and tissue regenerative properties, significantly contributes to alleviating the adverse effects of CEB injury, promoting wound healing.

Analysis of SIKVAV's receptor affinity, pharmacokinetics, and pharmacological characteristics: a matrikine with potent biological function

Matrikines are biologically active peptides generated from fragments fragmentation of extracellular matrix components (ECM) that are functionally distinct from the original full-length molecule. The active matricryptic sites can be unmasked by ECM components enzymatic degradation or multimerization, heterotypic binding, adsorption to other molecules, cell-mediated mechanical forces, exposure to reactive oxygen species, ECM denaturation, and others. Laminin α1-derived peptide (SIKVAV) is a bioactive peptide derived from laminin-111 that participates in tumor development, cell proliferation, angiogenesis in various cell types. SIKVAV has also a potential pharmaceutical activity that may be used for tissue regeneration and bioengineering in Alzheimer's disease and muscular dystrophies. In this work, we made computational analyzes of SIKVAV regarding the ADMET panel, that stands for Administration, Distribution, Metabolism, Excretion, and Toxicity. Docking analyzes using the α3β1 and α6β1 integrin receptors were performed to fill in the gaps in the SIKVAV's signaling pathway and coupling tests showed that SIKVAV can interact with both receptors. Moreover, there is no indication of cytotoxicity, mutagenic or carcinogenic activity, skin or oral sensitivity. Our analysis suggests that SIKVAV has a high probability of interacting with peroxisome proliferator-activated receptor-gamma (NR-PPAR-γ), which has anti-inflammatory activity. The results of bioinformatics can help understand the participation of SIKVAV in homeostasis and influence the understanding of how this peptide can act as a biological asset in the control of dystrophies, neurodegenerative diseases, and tissue engineering.

Computational Studies on 6-Pyruvoyl Tetrahydropterin Synthase (6-PTPS) of Plasmodium falciparum

6-Pyruvoyl tetrahydropterin synthase (6-PTPS) is a lyase involved in the synthesis of tetrahydrobiopterin. In Plasmodium species where dihydroneopterin aldolase (DHNA) is absent, it acts in the folate biosynthetic pathway necessary for the growth and survival of the parasite. The 6-pyruvoyl tetrahydropterin synthase of Plasmodium falciparum (PfPTPS) has been identified as a potential antimalarial drug target. This study identified potential inhibitors of PfPTPS using molecular docking techniques. Molecular docking and virtual screening of 62 compounds including the control to the deposited Protein Data Bank (PDB) structure was carried out using AutoDock Vina in PyRx. Five of the compounds, N,N-dimethyl-N'-[4-oxo-6-(2,2,5-trimethyl-1,3-dioxolan-4-yl)-3H-pteridin-2-yl]methanimidamide (140296439), 2-amino-6-[(1R)-3-cyclohexyl-1-hydroxypropyl]-3H-pteridin-4-one (140296495), 2-(2,3-dihydroxypropyl)-8,9-dihydro-6H-pyrimido[2,1-b]pteridine-7,11-dione (144380406), 2-(dimethylamino)-6-[(2,2-dimethyl-1,3-dioxolan-4-yl)-hydroxymethyl]-3H-pteridin-4-one (135573878), and [1-acetyloxy-1-(2-methyl-4-oxo-3H-pteridin-6-yl)propan-2-yl] acetate (136075207), showed better binding affinity than the control ligand, biopterin (135449517), and were selected and screened. Three conformers of 140296439 with the binding energy of -7.2, -7.1, and -7.0 kcal/mol along with 140296495 were better than the control at -5.7 kcal/mol. In silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies predicted good pharmacokinetic properties of all the compounds while reporting a high risk of irritant toxicity in 140296439 and 144380406. The study highlights the five compounds, 140296439, 140296495, 144380406, 135573878 and 136075207, as potential inhibitors of PfPTPS and possible compounds for antimalarial drug development.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

In silico design of novel bioactive molecules to treat breast cancer with chlorogenic acid derivatives: a computational and SAR approach

Introduction: Cancer is a vast group of diseases comprising abnormal cells that multiply and grow uncontrollably, and it is one of the top causes of death globally. Several types of cancers are diagnosed, but the incidence of breast cancer, especially in postmenopausal women, is increasing daily. Chemotherapeutic agents used to treat cancer are generally associated with severe side effects on host cells, which has led to a search for safe and potential alternatives. Therefore, the present research has been conducted to find novel bioactive molecules to treat breast cancer with chlorogenic acid and its derivatives. Chlorogenic acid was selected because of its known activity in the field. Methods: Several chlorogenic acid derivatives were subjected to computational studies such as molecular docking, determination of absorption, distribution, metabolism, and excretion (ADME), druglikeness, toxicity, and prediction of activity spectra for substances (PASS) to develop a potential inhibitor of breast cancer. The Protein Data Bank (PDB) IDs used for docking purposes were 7KCD, 3ERT, 6CHZ, 3HB5, and 1U72. Result: Exhaustive analysis of results has been conducted by considering various parameters, like docking score, binding energy, types of interaction with important amino acid residues in the binding pocket, ADME, and toxicity data of compounds. Among all the selected derivatives, CgE18, CgE11, CgAm13, CgE16, and CgE9 have astonishing interactions, excellent binding energy, and better stability in the active site of targeted proteins. The docking scores of compound CgE18 were -11.63 kcal/mol, -14.15 kcal/mol, and -12.90 kcal/mol against breast cancer PDB IDs 7KCD, 3HB5, and 1U72, respectively. The docking scores of compound CgE11 were -10.77 kcal/mol and -9.11 kcal/mol against breast cancer PDB IDs 3ERT and 6CHZ, respectively, whereas the docking scores of epirubicin hydrochloride were -3.85 kcal/mol, -6.4 kcal/mol, -8.76 kcal/mol, and -10.5 kcal/mol against PDB IDs 7KCD, 3ERT, 6CHZ, and 3HB5. The docking scores of 5-fluorouracil were found to be -5.25 kcal/mol, -3.43 kcal/mol, -3.73 kcal/mol, and -5.29 kcal/mol against PDB IDs 7KCD, 3ERT, 6CHZ, and 3HB5, which indicates the designed compounds have a better docking score than some standard drugs. Conclusion: Taking into account the results of molecular docking, drug likeness analysis, absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation, and PASS, it can be concluded that chlorogenic acid derivatives hold promise as potent inhibitors for the treatment of breast cancer.

Mutant-Huntingtin Molecular Pathways Elucidate New Targets for Drug Repurposing

The spectrum of neurodegenerative diseases known today is quite extensive. The complexities of their research and treatment lie not only in their diversity. Even many years of struggle and narrowly focused research on common pathologies such as Alzheimer's, Parkinson's, and other brain diseases have not brought cures for these illnesses. What can be said about orphan diseases? In particular, Huntington's disease (HD), despite affecting a smaller part of the human population, still attracts many researchers. This disorder is known to result from a mutation in the HTT gene, but having this information still does not simplify the task of drug development and studying the mechanisms of disease progression. Nonetheless, the data accumulated over the years and their analysis provide a good basis for further research. Here, we review studies devoted to understanding the mechanisms of HD. We analyze genes and molecular pathways involved in HD pathogenesis to describe the action of repurposed drugs and try to find new therapeutic targets.

Integrated Molecular Modeling and Machine Learning for Drug Design

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug to the market. Computational approaches have increasingly become an indispensable part of helping reduce the time and cost of the research and development of new drugs. In this Perspective, we summarize our recent efforts on integrating molecular modeling and machine learning to develop computational tools for modulator design, including a pocket-guided rational design approach based on AlphaSpace to target protein-protein interactions, delta machine learning scoring functions for protein-ligand docking as well as virtual screening, and state-of-the-art deep learning models to predict calculated and experimental molecular properties based on molecular mechanics optimized geometries. Meanwhile, we discuss remaining challenges and promising directions for further development and use a retrospective example of FDA approved kinase inhibitor Erlotinib to demonstrate the use of these newly developed computational tools.

Therapeutic Potential of the Genus Zanthoxylum Phytochemicals: A Theoretical ADME/Tox Analysis

Natural products (NPs) are essential in the search for new drugs to treat a wide range of diseases, including infectious and malignant disorders. However, despite the discovery of many bioactive NPs, they often do not make it to market as drugs due to toxicity and other challenges. The development of NPs into drugs is a long and expensive process, and many promising compounds are abandoned along the way. These molecules require in silico ADMET profiling in order to speed up their development into drugs lower costs, and the high attrition rate. The objective of this work was to produce thorough ADMET profiles of secondary metabolites from several classes that were isolated from Zanthoxylum species. The genus has a long history of therapeutic use, including treating tumours, hypertension, gonorrhoea, coughs, bilharzia, chest pains, and toothaches. The study used a dataset of 406 compounds from the genus for theoretical ADMET analysis. The findings revealed that 81% of the compounds met Lipinski's rule of five, indicating good oral bioavailability. The drug-likeness criteria were taken into account, with percentages ranging from 66.2 to 88.1 percent. Additionally, 9.2% of the compounds were predicted to be lead-like, demonstrating their potential as promising drug development candidates. Interestingly, none of the compounds inhibited hERG I, while 33% inhibited hERG II, potentially having cardiac implications. Additionally, 30% of the compounds exhibited AMES toxicity inhibition, while 23.6% were identified as hepatotoxic and 22.2% would cause skin sensitivity. Moreover, 81.8% of the compounds demonstrated high intestinal absorption, making them desirable for oral drugs. In conclusion, these findings highlight the diverse properties of the investigated compounds and their potential for drug development.

Precision drug design against Acidovorax oryzae: leveraging bioinformatics to combat rice brown stripe disease

Bacterial brown stripe disease caused by Acidovorax oryzae is a major threat to crop yields, and the current reliance on pesticides for control is unsustainable due to environmental pollution and resistance. To address this, bacterial-based ligands have been explored as a potential treatment solution. In this study, we developed a protein-protein interaction (PPI) network for A. oryzae by utilizing shared differentially expressed genes (DEGs) and the STRING database. Using a maximal clique centrality (MCC) approach through CytoHubba and Network Analyzer, we identified hub genes within the PPI network. We then analyzed the genomic data of the top 10 proteins, and further narrowed them down to 2 proteins by utilizing betweenness, closeness, degree, and eigenvector studies. Finally, we used molecular docking to screen 100 compounds against the final two proteins (guaA and metG), and Enfumafungin was selected as a potential treatment for bacterial resistance caused by A. oryzae based on their binding affinity and interaction energy. Our approach demonstrates the potential of utilizing bioinformatics and molecular docking to identify novel drug candidates for precision treatment of bacterial brown stripe disease caused by A. oryzae, paving the way for more targeted and sustainable control strategies. The efficacy of Enfumafungin in inhibiting the growth of A. oryzae strain RS-1 was investigated through both computational and wet lab methods. The models of the protein were built using the Swiss model, and their accuracy was confirmed via a Ramachandran plot. Additionally, Enfumafungin demonstrated potent inhibitory action against the bacterial strain, with an MIC of 100 µg/mL, reducing OD600 values by up to 91%. The effectiveness of Enfumafungin was further evidenced through agar well diffusion assays, which exhibited the highest zone of inhibition at 1.42 cm when the concentration of Enfumafungin was at 100 µg/mL. Moreover, Enfumafungin was also able to effectively reduce the biofilm of A. oryzae RS-1 in a concentration-dependent manner. The swarming motility of A. oryzae RS-1 was also found to be significantly inhibited by Enfumafungin. Further validation through TEM observation revealed that bacterial cells exposed to Enfumafungin displayed mostly red fluorescence, indicating destruction of the bacterial cell membrane.

Development of Kinase-Centric Drugs: A Computational Perspective

Kinases are prominent drug targets in the pharmaceutical and research community due to their involvement in signal transduction, physiological responses, and upon dysregulation, in diseases such as cancer, neurological and autoimmune disorders. Several FDA-approved small-molecule drugs have been developed to combat human diseases since Gleevec was approved for the treatment of chronic myelogenous leukemia. Kinases were considered "undruggable" in the beginning. Several FDA-approved small-molecule drugs have become available in recent years. Most of these drugs target ATP-binding sites, but a few target allosteric sites. Among kinases that belong to the same family, the catalytic domain shows high structural and sequence conservation. Inhibitors of ATP-binding sites can cause off-target binding. Because members of the same family have similar sequences and structural patterns, often complex relationships between kinases and inhibitors are observed. To design and develop drugs with desired selectivity, it is essential to understand the target selectivity for kinase inhibitors. To create new inhibitors with the desired selectivity, several experimental methods have been designed to profile the kinase selectivity of small molecules. Experimental approaches are often expensive, laborious, time-consuming, and limited by the available kinases. Researchers have used computational methodologies to address these limitations in the design and development of effective therapeutics. Many computational methods have been developed over the last few decades, either to complement experimental findings or to forecast kinase inhibitor activity and selectivity. The purpose of this review is to provide insight into recent advances in theoretical/computational approaches for the design of new kinase inhibitors with the desired selectivity and optimization of existing inhibitors.

Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation

Cell lines are valuable resources as model for human biology and translational medicine. It is thus important to explore the concordance between the expression in various cell lines vis-à-vis human native and disease tissues. In this study, we investigate the expression of all human protein-coding genes in more than 1,000 human cell lines representing 27 cancer types by a genome-wide transcriptomics analysis. The cell line gene expression is compared with the corresponding profiles in various tissues, organs, single-cell types and cancers. Here, we present the expression for each cell line and give guidance for the most appropriate cell line for a given experimental study. In addition, we explore the cancer-related pathway and cytokine activity of the cell lines to aid human biology studies and drug development projects. All data are presented in an open access cell line section of the Human Protein Atlas to facilitate the exploration of all human protein-coding genes across these cell lines.

Identification of Cyanobacteria-Based Natural Inhibitors Against SARS-CoV-2 Druggable Target ACE2 Using Molecular Docking Study, ADME and Toxicity Analysis

In 2019-2020, the novel "severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)" had emerged as the biggest challenge for humanity, causing "coronavirus disease 19 (COVID-19)". Scientists around the world have been putting continuous efforts to unfold potential inhibitors of SARS-CoV-2. We have performed computational studies that help us to identify cyanobacterial photoprotective compounds as potential inhibitors against SARS-CoV-2 druggable target human angiotensin-converting enzyme (ACE2), which plays a vital role in the attachment and entry of the virus into the cell. Blocking the receptor-binding domain of ACE2 can prevent the access of the virus into the compartment. A molecular docking study was performed between photoprotective compounds mycosporine-like amino acids, scytonemins and ACE2 protein using AutoDock tools. Among sixteen molecularly docked metabolites, seven compounds were selected with binding energy < 6.8 kcal/mol. Afterwards, drug-likeness and toxicity of the top candidate were predicted using Swiss ADME and Pro Tox-II online servers. All top hits show desirable drug-likeness properties, but toxicity pattern analysis discloses the toxic effect of scytonemin and its derivatives, resulting in the elimination from the screening pipeline. Further molecular interaction study of the rest two ligands, mycosporine-glycine-valine and shinorine with ACE2 was performed using PyMol, Biovia Discovery studio and LigPlot+. Lastly biological activity of both the ligands was predicted by using the PASS online server. Combining the docking score and other studied properties, we believe that mycosporine-glycine-valine and shinorine have potential to be potent inhibitors of ACE2 and can be explored further to use against COVID-19.

AI in drug discovery and its clinical relevance

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.

Preliminary Evaluation of Lablab purpureus Phytochemicals for Anti-BoHV-1 Activity Using In Vitro and In Silico Approaches

Lablab purpureus from the Fabaceae family has been reported to have antiviral properties and used in traditional medical systems like ayurveda and Chinese medicine and has been employed to treat a variety of illnesses including cholera, food poisoning, diarrhea, and phlegmatic diseases. The bovine alphaherpesvirus-1 (BoHV-1) is notorious for causing significant harm to the veterinary and agriculture industries. The removal of the contagious BoHV-1 from host organs, particularly in those reservoir creatures, has required the use of antiviral drugs that target infected cells. This study developed LP-CuO NPs from methanolic crude extracts, and FTIR, SEM, and EDX analyses were used to confirm their formation. SEM analysis revealed that the LP-CuO NPs had a spherical shape with particle sizes between 22 and 30 nm. Energy-dispersive X-ray pattern analysis revealed the presence of only copper and oxide ions. By preventing viral cytopathic effects in the Madin-Darby bovine kidney cell line, the methanolic extract of Lablab purpureus and LP-CuO NPs demonstrated a remarkable dose-dependent anti-BoHV-1 action in vitro. Furthermore, molecular docking and molecular dynamics simulation studies of bio-actives from Lablab purpureus against the BoHV-1 viral envelope glycoprotein disclosed effective interactions between all phytochemicals and the protein, although kievitone was found to have the highest binding affinity, with the greatest number of interactions, which was also validated with molecular dynamics simulation studies. Understanding the chemical reactivity qualities of the four ligands was taken into consideration facilitated by the global and local descriptors, which aimed to predict the chemical reactivity descriptors of the studied molecules through the conceptual DFT methodology, which, along with ADMET finding, support the in vitro and in silico results.

Updates on drug designing approach through computational strategies: a review

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

AI-Driven De Novo Design and Molecular Modeling for Discovery of Small-Molecule Compounds as Potential Drug Candidates Targeting SARS-CoV-2 Main Protease

Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

In silico screening of inhibitors against human dihydrofolate reductase to identify potential anticancer compounds

In all species, dihydrofolate reductase (DHFR) is an essential enzyme that regulates the cellular amount of tetrahydrofolate. Human DHFR (hDHFR) activity inhibition results in tetrahydrofolate depletion and cell death. This property has made hDHFR a therapeutic target for cancer. Methotrexate is a well-known hDHFR inhibitor, but its administration has shown some light to severe adverse effects. Therefore, we aimed to find new potential hDHFR inhibitors using structure-based virtual screening, ADMET prediction, molecular docking, and molecular dynamics simulations. Here, we used the PubChem database to find all compounds with at least 90% structural similarity to known natural DHFR inhibitors. To explore their interaction pattern and estimate their binding affinities, the screened compounds (2023) were subjected to structure-based molecular docking against hDHFR. The fifteen compounds that showed higher binding affinity to the hDHFR than the reference compound (methotrexate) displayed important molecular orientation and interactions with key residues in the enzyme's active site. These compounds were subjected to Lipinski and ADMET prediction. PubChem CIDs: 46886812 and 638190 were identified as putative inhibitors. In addition, molecular dynamics simulations revealed that the binding of compounds (CIDs: 46886812 and 63819) stabilized the hDHFR structure and caused minor conformational changes. Our findings suggest that two compounds (CIDs: 46886812 and 63819) could be promising potential inhibitors of hDHFR in cancer therapy.Communicated by Ramaswamy H. Sarma.

Target identification by structure-based computational approaches: Recent advances and perspectives

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

Repositioning linifanib as a potent anti-necroptosis agent for sepsis

Sepsis is a systemic inflammatory syndrome (SIRS) caused by acute microbial infection, and it has an extremely high mortality rate. Tumor necrosis factor-α (TNF-α)-induced necroptosis contributes to the pathophysiology of sepsis, so inhibiting necroptosis might be expected to improve clinical outcomes in septic patients. Here we predicted candidate drugs for treating sepsis in silico by combining genes differentially expressed in septic patients and controls combined with interrogation of the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 perturbation database. Sixteen candidate drugs were screened out through bioinformatics analysis, and the top candidate linifanib was validated in cellular and mouse models of TNF-α-induced necroptosis. Cell viability was measured using a luminescent ATP assay, while the effects of linifanib on necroptosis were investigated by western blotting, immunoprecipitation, and RIPK1 kinase assays. Linifanib effectively protected cells from necroptosis and rescued SIRS mice from TNF-α-induced shock and death. In vitro, linifanib directly suppressed RIPK1 kinase activity. In vivo, linifanib effectively reduced overexpressed IL-6, a marker of sepsis severity, in the lungs of SIRS mice. Our preclinical evidence using an integrated in silico and experimental drug repositioning approach supports the potential clinical utility of linifanib in septic patients. Further clinical validation is now warranted.

Molecular Modeling and In Vitro Evaluation of Piplartine Analogs against Oral Squamous Cell Carcinoma

Cancer is a principal cause of death in the world, and providing a better quality of life and reducing mortality through effective pharmacological treatment remains a challenge. Among malignant tumor types, squamous cell carcinoma-esophageal cancer (EC) is usually located in the mouth, with approximately 90% located mainly on the tongue and floor of the mouth. Piplartine is an alkamide found in certain species of the genus Piper and presents many pharmacological properties including antitumor activity. In the present study, the cytotoxic potential of a collection of piplartine analogs against human oral SCC9 carcinoma cells was evaluated. The analogs were prepared via Fischer esterification reactions, alkyl and aryl halide esterification, and a coupling reaction with PyBOP using the natural compound 3,4,5-trimethoxybenzoic acid as a starting material. The products were structurally characterized using 1H and 13C nuclear magnetic resonance, infrared spectroscopy, and high-resolution mass spectrometry for the unpublished compounds. The compound 4-methoxy-benzyl 3,4,5-trimethoxybenzoate (9) presented an IC50 of 46.21 µM, high selectively (SI > 16), and caused apoptosis in SCC9 cancer cells. The molecular modeling study suggested a multi-target mechanism of action for the antitumor activity of compound 9 with CRM1 as the main target receptor.

In silico Structure Prediction, Molecular Docking, and Dynamic Simulation of Plasmodium falciparum AP2-I Transcription Factor

Plasmodium falciparum Apicomplexan Apetala 2 Invasion (PfAP2-I) transcription factor (TF) is a protein that regulates the expression of a subset of gene families involved in P. falciparum red blood cell (RBC) invasion. Inhibiting PfAP2-I TF with small molecules represents a potential new antimalarial therapeutic target to combat drug resistance, which this study aims to achieve. The 3D model structure of PfAP2-I was predicted ab initio using ROBETTA prediction tool and was validated using Save server 6.0 and MolProbity. Computed Atlas of Surface Topography of proteins (CASTp) 3.0 was used to predict the active sites of the PfAP2-I modeled structure. Pharmacophore modeling of the control ligand and PfAP2-I modeled structure was carried out using the Pharmit server to obtain several compounds used for molecular docking analysis. Molecular docking and postdocking studies were conducted using AutoDock vina and Discovery studio. The designed ligands' toxicity predictions and in silico drug-likeness were performed using the SwissADME predictor and OSIRIS Property Explorer. The modeled protein structure from the ROBETTA showed a validation result of 96.827 for ERRAT, 90.2% of the amino acid residues in the most favored region for the Ramachandran plot, and MolProbity score of 1.30 in the 98th percentile. Five (5) best hit compounds from molecular docking analysis were selected based on their binding affinity (between -8.9 and -11.7 Kcal/mol) to the active site of PfAP2-I and were considered for postdocking studies. For the absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, compound MCULE-7146940834 had the highest drug score (0.63) and drug-likeness (6.76). MCULE-7146940834 maintained a stable conformation within the flexible protein's active site during simulation. The good, estimated binding energies, drug-likeness, drug score, and molecular dynamics simulation interaction observed for MCULE-7146940834 against PfAP2-I show that MCULE-7146940834 can be considered a lead candidate for PfAP2-I inhibition. Experimental validations should be carried out to ascertain the efficacy of these predicted best hit compounds.

Computational Exploration of Anti-cancer Potential of Flavonoids against Cyclin-Dependent Kinase 8: An In Silico Molecular Docking and Dynamic Approach

Over the centuries, cancer has been considered one of the significant health threats. It holds the position in the list of deadliest diseases over the globe. In women, breast cancer is the most common among many cancers and is the second most common cancer all over the world, while lung cancer is the first. Cyclin-dependent kinase 8 (CDK8) has been identified as a critical oncogenic driver that is found in breast cancer and associated with tumor progression. Flavonoids were virtually screened against CDK8 using molecular docking, drug-likeness, ADMET prediction, and a molecular dynamics (MD) simulation approach to determine the potential flavonoid structure against CDK8. The results indicated that ZINC000005854718 showed the highest negative binding affinity of -10.7 kcal/mol with the targeted protein and passed all the drug-likeness parameters. Performed molecular dynamics simulation showed that docked complex systems have good conformational stability over 100 ns in different temperatures (298, 300, 305, 310, and 320 K). The comparison between calculated binding free energy via MM/PB(GB)SA methods and binding affinity calculated via molecular docking suggested tight binding of ZINC000005854718 with targeted protein. The results concluded that ZINC000005854718 has drug-like properties with tight and stable binding with the targeted protein.

How to Design Peptides

Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.

The module triad: a novel network biology approach to utilize patients' multi-omics data for target discovery in ulcerative colitis

Tumor necrosis factor-[Formula: see text] inhibitors (TNFi) have been a standard treatment in ulcerative colitis (UC) for nearly 20 years. However, insufficient response rate to TNFi therapies along with concerns around their immunogenicity and inconvenience of drug delivery through injections calls for development of UC drugs targeting alternative proteins. Here, we propose a multi-omic network biology method for prioritization of protein targets for UC treatment. Our method identifies network modules on the Human Interactome-a network of protein-protein interactions in human cells-consisting of genes contributing to the predisposition to UC (Genotype module), genes whose expression needs to be modulated to achieve low disease activity (Response module), and proteins whose perturbation alters expression of the Response module genes to a healthy state (Treatment module). Targets are prioritized based on their topological relevance to the Genotype module and functional similarity to the Treatment module. We demonstrate utility of our method in UC and other complex diseases by efficiently recovering the protein targets associated with compounds in clinical trials and on the market . The proposed method may help to reduce cost and time of drug development by offering a computational screening tool for identification of novel and repurposing therapeutic opportunities in UC and other complex diseases.

Artificial Intelligence (AI) in Pharmacy: An Overview of Innovations

Artificial Intelligence (AI) emerged as an intervention for data and number-related problems. This breakthrough has led to several technological advancements in virtually all fields from engineering to architecture, education, accounting, business, health, and so on. AI has come a long way in healthcare, having played significant roles in data and information storage and management - such as patient medical histories, medicine stocks, sale records, and so on; automated machines; software and computer applications like diagnostic tools such as MRI radiation technology, CT diagnosis and many more have all been created to aid and simplify healthcare measures. Inarguably, AI has revolutionized healthcare to be more effective and efficient and the pharmacy sector is not left out. During the past few years, a considerable amount of increasing interest in the uses of AI technology has been identified for analyzing as well as interpreting some important fields of pharmacy like drug discovery, dosage form designing, polypharmacology, and hospital pharmacy. Given the growing importance of AI, we wanted to create a comprehensive report which helps every practicing pharmacist understand the biggest breakthroughs which are assisted by the deployment of this field.