Computer-aided Drug Design Techniques Research Articles

Utilizing deep learning for alzheimer's disease treatment: Targeting -amyloid for therapeutic intervention

Abstract. Amyloid-beta (A for short) is a protein intricately linked to Alzheimers disease (AD for short). In the brains of AD patients, A forms abnormal deposits known as amyloid plaques, which are considered one of the key factors in the progression of AD. These plaques may disrupt communication between neurons, leading to cell death and a decline in cognitive function. Therefore, this study aims to utilize Computer-aided drug design (CADD) techniques to screen and optimize potential therapeutic agents targeting A. Through literature review and UniProt, we identified the active sites of A and constructed a three-dimensional structural model using AlphaFold. We employed Molecular docking technology to virtually screen a compound library for candidate molecules that may bind to A. The selected candidates were then subjected to Molecular dynamics simulation to verify their stability, and their molecular structures were further optimized using Pharmacophore modeling. Our research results indicate the successful screening of a series of candidate compounds with high affinity and selectivity. These compounds can form stable complexes with the active sites of A, thereby inhibiting its aggregation and deposition. Current structural determination methods for A have certain limitations. Techniques such as cryo-electron microscopy (cryo-EM) and scanning electron microscopy (SEM) can observe the morphology of A fibrils but typically do not provide atomic-level structural information. Additionally, A proteins tend to form non-specific aggregates in vitro, presenting challenges in preparing samples suitable for structural analysis. The innovation of this study lies in the combination of various computer-assisted technologies, offering new perspectives and methods for the drug treatment of AD and laying the groundwork for the development of novel therapeutic agents targeting A.

Investigation of Novel Quinoline Derivatives Targeting Epidermal Growth Factor Receptors as Anticancer Agents a Computational Approach

Background: Newer chemical entities are created and synthesis has been made feasible by a variety of computer-aided drug design (CAAD) techniques. In addition to facilitating the visualisation of the ligand-target binding process, the application of in silico methodologies and structure-based drug design (SBDD) allows for the prediction of receptor affinities and significant binding pocket locations. Objective: The goal of the current study was to identify new quinoline derivatives by computational methods specially designed to bind the EGFR receptor in the treatment of breast cancer. Materials and Methods: ChemAxon Marvin Sketch 5.11.5 was used to create derivatives of quinolines. The admetSAR online web tools and SwissADME were utilised to forecast the toxicity and pharmacokinetic characteristics of several substances. A multitude of software programmes, such as Autodock 1.1.2, MGL Tools 1.5.6, Procheck, Protparam ExPasy tool, PyMOL, and Biovia Discovery Studio Visualizer v20.1.0.19295 were also employed to ascertain the ligand-receptor interactions between quinoline derivatives and the target receptor (PDB -5GNK). Result: Almost all components were shown to be less hazardous, orally consumable and to have the appropriate pharmacokinetic characteristics based on in silico study. All newly generated derivative compounds have higher docking scores when compared to the widely used medication sorafenib. Conclusion: Interactions with quinoline analogues boost binding energy and the number of Hbonds produced, making them a suitable place to start when creating compounds for further exploration. The quinoline moiety increases its potential as a novel therapy alternative for breast cancer and could facilitate more comprehensive in vivo, in vitro, chemical-based, and pharma studies by medicinal chemists.

Application of computer-aided drug design in drug discovery and development: Updating knowledge

Coronavirus (CoV) diseases are widespread throughout the world and have caused considerable socio-economic disruptions. For this reason, efforts have been made to develop a direct or indirect antiviral drugs against these diseases. However, no specific antiviral drug has yet been approved by the Food and Drug Administration (FDA) for CoV infections. Thus, the challenge in discovering therapeutic molecules against these infections remains pertinent. Computer-aided drug design (CADD) is one of the modern techniques for drug discovery and development. It accelerates the process, minimizes costs, and reduces research time. In this article, we present the three CADD approaches, namely structure-based drug discovery (SBDD), ligand-based drug discovery (LBDD) and high-throughput virtual screening (HTVS). The different methods used in these three approaches CADD, such as molecular modelling, target structure analysis, molecular docking, molecular dynamics simulation, pharmacophore modelling, quantitative structure-activity relationship (QSAR), ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) are detailed. In addition, the bioinformatics tools and databases commonly used in these different CADD techniques are also described.

A critical review on phytochemicals as antiviral medications for SARS-CoV-2 virus infection.

A pandemic of acute respiratory infection, which was specified as coronavirus disease 2019, was instigated by a different strain of the virulent coronavirus SARS-CoV-2 that first appeared in late 2019. Since viral infections spread fast and there is presently no effective treatment, the use of plants with a long history of use in treating these infections has been explored regularly. The pandemic of coronavirus disease 2019 (COVID-19) has brought to light the dearth of medications with approval to treat acute viral illnesses. Because of this, the illness had a high fatality rate. The mortality rate was initially quite high and varied according to the patient's geographic location. For instance, among Chinese patients, the rate was 3·6%, whereas 1·5% of COVID-19-related deaths were documented outside of China. As of 2020, India has a 1.4% case fatality rate (CFR) of COVID-19 mortality, compared to 2.8% in Brazil and 1.8% in the USA. Many studies are being conducted to create pharmaceutical compounds specifically targeting important SARS-CoV-2 proteins. Several drug discovery initiatives are being undertaken to find powerful inhibitors by combining biochemical assay and computer-aided drug design techniques. Although plant-derived compounds have not had much success in the dominion of antivirals, plants are, however, believed to be a limitless supply of medications for a variety of diseases and clinical conditions. The scientific foundation required for developing novel natural source medications is provided by the chemical characterization and analysis of plant components. Most viral infections treated by ethnobotanical applications and historical literature on ayurveda, and traditional medicine are generally attributed to phytochemicals, which are compounds derived from medicinal plants. In this review, we have described the application of vascular plant-derived chemicals, such as tannins, polyphenols, alkaloids, and flavonoids, as antivirals, especially for managing COVID-19. This article discusses novel bioactive compounds and their molecular structures that target the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as prospective candidates for anti-coronavirus disease drugs. Moreover, to confirm the effectiveness of the phytochemicals that have demonstrated antiviral activity, clinical trials would need to be conducted in addition to the preclinical research that has already been done. To ensure spectacular findings, more applications of the compound would need to be studied to fully understand the effects of those phytochemicals whose clinical usefulness has already been established.

TRPA1 Inhibition Effects by 3-Phenylcoumarin Derivatives.

Transient receptor potential ankyrin 1 (TRPA1) protein plays an important role in the inflammatory response, and it has been associated with different pain conditions and pain-related diseases, making TRPA1 a valid target for painkillers. In this study, we identified potential TRPA1 inhibitors and located their binding sites utilizing computer-aided drug design (CADD) techniques. The designed 3-phenylcoumarin-based TRPA1 inhibitors were successfully synthesized using a microwave assisted synthetic strategy. 3-(3-Bromophenyl)-7-acetoxycoumarin (5), 7-hydroxy-3-(3-hydroxyphenyl)coumarin (12) and 3-(3-hydroxyphenyl)coumarin (23) all showed inhibitory activity toward TRPA1 in vitro. Compound 5 also decreased the size and formation of breast cancer cells. Hence, targeting TRPA1 may represent a promising alternative for the treatment of pain and inflammation.

In-silico Binding, Stability, Pharmacokinetics, and Toxicity Studies on Natural (-)-ambrox Metabolites as Binding Ligands to Luminal B and Triple-negative/basal-like Proteins for Breast Cancer Therapy

Background:: Breast cancer is the most prevalent malignant tumour in women of all races and is the second largest cause of cancer-related death in the majority of races. Based on the pattern of gene expression, five intrinsic or molecular classifications for breast tumours are frequently used. Our research, which is presently being utilized to treat breast cancer and has the potential to significantly change the course of the illness, is focused on two of them: luminal B breast cancer and triplenegative/ basal-like breast cancer. Methods:: Screening a database containing millions of drug molecules or phytochemicals has become rapid and simple due to computer-aided drug design (CADD) techniques. In the current work, nine natural compounds were screened for ambrox from a sperm whale using docking research. Results:: Following docking studies, nine substances were discovered to interact with basal-like and luminal B breast cancer proteins. All nine metabolites, however, adhered to Lipinski's rule of five and had sufficient oral bioavailability. The greatest binding affinities were demonstrated by 13,14,15,16-tetranorlabdane-3-oxo-8,12-diol, 6-β-hydroxy ambrox, 1-α-hydroxy-3-oxoambrox, and 2-α-3-β-dihydroxy ambrox. Conclusion:: Therefore, it can be concluded that research on molecular docking and pharmacological mimics may hasten the discovery of new medications. The use of ambrox metabolites in the treatment of breast cancer also requires future perspectives on their therapeutic use.

Molecular Dynamics Simulations and Binding Free Energy Calculations to Discover New Insights into NLRP3 Inhibitors

Background: Inflammation is an immunological reaction against an aggressor agent. NLRP3 inflammasome is a component of the immune system, which, when excessively activated, results in several inflammatory diseases, making it an attractive target for discovering antiinflammatory drugs. Computer-Aided Drug Design (CADD) techniques are powerful tools used to search for new drugs in less time and financial cost. Recently, studies demonstrated the CADD methods to discover information about NLRP3 inhibitors MCC950 and NP3-146. In addition, the discovery of GDC-2394 and its evaluation in clinical trials instigate new studies to find binding modes and structural attributes that can used in drug design works against this target. Objectives: Here, molecular modeling methods were used to discover the significant interactions of GDC-2394, MCC950, and NP3-146 with NLRP3 to obtain helpful information in drug design compared to other inhibitors. Methods: Molecular docking was performed using GOLD software. The best complexes were submitted into molecular dynamics simulations using GROMACS software, and the MM-PBSA was used to provide the free binding energy, which was performed using the tool g_mmpbsa compiled in GROMACS. Results: The RMSD, RMSF, Rg, SASA, and H-bond plots showed that the compound was stable during MD simulation time (100 ns) for GDC-2394. The PCA analysis for all compounds verified similar variance of the complex with the inhibitors to the apo-NLRP3, indicative of stability. DCCM analysis showed the best correlation in residues 134 - 371 region, which contains critical amino acids from the binding site (Ala227, Ala228, and Arg578), besides the newly identified residues. Using MMPBSA to provide the binding free energy, it was observed that the high affinity of the drugs against NLRP3 is related to the lower rigidity of the structure. Furthermore, we identified the critical residues Phe575, Pro352, Tyr632, and Met661 related to the coupling process. result: Then, RMSD, RMSF, Rg, SASA, and H-bond plots showed that the compound was stable during MD simulation time (100 ns) for GDC-2394. The PCA analysis for all compounds verified similar variance of the complex with the inhibitors to the apo-NLRP3, indicative of stability. Through DCCM analysis, the best correlation was observed in residues 134 - 371 region, which contains critical amino acids from the binding site (Ala227, Ala228, and Arg578), besides the newly identified residues. Using MM-PBSA to provide the binding free energy, it was observed that the high affinity of the drugs against NLRP3 is related to the lower rigidity of the structure. Furthermore, we identified the critical residues Phe575, Pro352, Tyr632, and Met661 related to the coupling process. Conclusion: Thus, these discoveries may contribute to the development of new anti-inflammatory drugs, such as NLRP3 inhibitors.

Identifying the lead molecule from Carica papaya using computer-aided drug design technique against streptococcus pneumoniae

Identifying the lead molecule from Carica papaya using computer-aided drug design technique against streptococcus pneumoniae

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

In the search for better anticancer drugs, computer-aided drug design (CADD) techniques play an indispensable role in facilitating the lengthy and costly drug discovery process especially when natural products are involved. Anthraquinone is one of the most widely-recognized natural products with anticancer properties. This review aimed to systematically assess and synthesize evidence on the utilization of CADD techniques centered on the anthraquinone scaffold for cancer treatment. The conduct and reporting of this review were done in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guideline. The protocol was registered in the "International prospective register of systematic reviews" database (PROSPERO: CRD42023432904) and also published recently. The search strategy was designed based on the combination of concept 1 "CADD or virtual screening", concept 2 "anthraquinone" and concept 3 "cancer". The search was executed in PubMed, Scopus, Web of Science and MedRxiv on 30 June 2023. Databases searching retrieved a total of 317 records. After deduplication and applying the eligibility criteria, the final review ended up with 32 articles in which 3 articles were found by citation searching. The CADD methods used in the studies were either structure-based alone (69%) or combined with ligand-based methods via parallel (9%) or sequential (22%) approaches. Molecular docking was performed in all studies, with Glide and AutoDock being the most popular commercial and public software used respectively. Protein data bank was used in most studies to retrieve the crystal structure of the targets of interest while the main ligand databases were PubChem and Zinc. The utilization of in-silico techniques has enabled a deeper dive into the structural, biological and pharmacological properties of anthraquinone derivatives, revealing their remarkable anticancer properties in an all-rounded fashion. By harnessing the power of computational tools and leveraging the natural diversity of anthraquinone compounds, researchers can expedite the development of better drugs to address the unmet medical needs in cancer treatment by improving the treatment outcome for cancer patients.

Three Decades of Targeting Falcipains to Develop Antiplasmodial Agents: What have we Learned and What can be Done Next?

Malaria is a devastating infectious disease that affects large swathes of human populations across the planet's tropical regions. It is caused by parasites of the genus Plasmodium, with Plasmodium falciparum being responsible for the most lethal form of the disease. During the intraerythrocytic stage in the human hosts, malaria parasites multiply and degrade hemoglobin (Hb) using a battery of proteases, which include two cysteine proteases, falcipains 2 and 3 (FP-2 and FP-3). Due to their role as major hemoglobinases, FP-2 and FP-3 have been targeted in studies aiming to discover new antimalarials and numerous inhibitors with activity against these enzymes, and parasites in culture have been identified. Nonetheless, cross-inhibition of human cysteine cathepsins remains a serious hurdle to overcome for these compounds to be used clinically. In this article, we have reviewed key functional and structural properties of FP-2/3 and described different compound series reported as inhibitors of these proteases during decades of active research in the field. Special attention is also paid to the wide range of computer-aided drug design (CADD) techniques successfully applied to discover new active compounds. Finally, we provide guidelines that, in our understanding, will help advance the rational discovery of new FP-2/3 inhibitors.

Investigating the bioactive compounds from Capsicum annum as a probable alternative therapy for prostate cancer treatment: a structure-based drug design approach

Abstract Prostate cancer remains a significant global health challenge, necessitating the exploration of novel therapeutic approaches. Androgen receptor (AR) signaling plays a critical role in prostate cancer progression and is a primary target for therapy. This study investigates the potential of phytochemicals from Capsicum annuum (Bell pepper) along with two common standand drugs (Apalutamide and Enzalutamide) as inhibitors of the human androgen receptor (AR) and prostate-specific membrane antigen (PSMA). Utilizing computer-aided drug design techniques, molecular docking studies were conducted to evaluate the binding affinities of selected ligands against AR (PDB ID: 1XOW) and PSMA (PDB ID: 2XEI), their ADMET properties, drug-likeness, oral bioavailability, and bioactivity profiles were also examined. Coumaroylquinic acid and 5-O-caffeoylquinic acid methyl-ester emerged as top-performing ligands, demonstrating strong binding affinities of −9.4 kcal/mol and −9.2 kcal/mol, respectively, against PSMA. Additionally, molecular dynamics simulations provided insights into the stability of protein-ligand complexes, with Coumaroylquinic acid exhibiting a stable binding conformation throughout the simulation. These findings suggest the potential of C. annuum phytochemicals, particularly Coumaroylquinic acid and 5-O-caffeoylquinic acid methyl-ester, as promising inhibitors of PSMA. Moreover, other ligands (Caffeoylglucoside and 1-O-galloyl-beta-d-glucose) identified in the study demonstrate interactions with AR, highlighting a multifaceted approach to prostate cancer treatment. Overall, this study underscores the potential of C. annuum phytochemicals as a source of novel therapeutic agents for prostate cancer, laying the groundwork for further lead optimization efforts.

Abstract 1815: Optimization of Lin28 inhibitors: A promising therapeutic approach for the suppression of cancer cell proliferation, stemness properties, and spheroid growth

Abstract Background: The pluripotency factor known as Lin28 plays a crucial role in the regulation of cancer cell stem-like characteristics, hence facilitating the formation of cancer and the evolution of therapy-resistant tumors. The protein exerts its function by utilizing its cold shock domain and zinc knuckle domain (ZKD) to engage with the Let-7 pre-microRNA and impede the process of Let-7 production. The implementation of chemical agents to hinder the interaction between Lin28 and Let-7 has been proven as a therapeutic approach for treating aggressive tumors. Methods: Computer-aided drug design techniques were employed in the development of a novel series of small molecule inhibitors, which are derivatives of our previously identified lead compounds Ln115, Ln15, and Ln7. Fluorescent Polarization Assay (FP), and Electrophoretic Mobility Shift Assay (EMSA) were used to screen the chemicals as Lin28-Let7 interaction disruptors using Lin28A/B ZKD domain. To further screen the chemicals, the Incucyte cell proliferation assay was performed using two Lin28A and Lin28B positive cells, IGROV-1, and DuNE cells, respectively. The inhibition of Lin28 activity was subsequently evaluated by measuring Let-7 micro-RNA and Lin28 protein levels using RT-PCR and Western Blot. Then, the metabolism profile was explored using Microsomal Stability Assay. Incucyte proliferation assay and MTS assay determined the IC50 of lead compound in Lin28 positive cells. The anti-stemness properties of the lead compound were subsequently evaluated through assays for stem gene expression levels and colony formation. To evaluate the potential of the lead compound to further move into animal studies, we tested it on a newly developed 3D Neuroendocrine Prostate Cancer (NEPC) model. Results: In this study, we provide a novel set of derivatives, within which, Ln268 was chosen as the lead compound. Ln268 shows higher metabolic stability with a half-life time of 462 min and clearance rate of 5 ul/min/mg, compared to its father compound at 347 min and 46 ul/min/mg, respectively. Ln268 treatment resulted in a time-dependent inhibition of cell growth with an IC50 of 2.5 uM at 72 hrs. Ln268 reduces mRNA levels of stemness and neurodifferentiation markers such as CHGA, CHG, SYP, SCGN, OCT3/4, NANOG, ID4, FOXC1, ALDH1A2, and FOXO3. In the 3D NEPC model, Ln268 suppresses spheroid growth in a dose- and time-dependent manner. Ln268 achieved 100% spheroid growth inhibition at the concentration of 100 uM, while the current commercially available C1632 only reached 50%. Conclusion: We discovered a novel Ln15 derivative named Ln268, which has a better metabolism profile and increased inhibitory activity to distrust Lin28-Let7 interaction and cancer cell proliferation. Ln268 has the potential to be improved and used as an anti-cancer treatment for a variety of malignancies, such as prostate cancer. Citation Format: Victor M. Matias-Barrios, Mariia Radaeva, Graciella Rosellinny, Artem Cherkasov, Xuesen Dong. Optimization of Lin28 inhibitors: A promising therapeutic approach for the suppression of cancer cell proliferation, stemness properties, and spheroid growth [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1815.

Computational Molecular Docking and In-Silico, ADMET Prediction Studies of Quinoline Derivatives as EPHB4 Inhibitor

Background:: The creation and development of novel chemical entities is made possible by numerous computer-aided drug design techniques. The ability to visualize the ligand-target interaction and forecast the important holding pocket locations and affinities of ligands to their intended macromolecules is made possible by pharmacophore-based drug design and understanding in-silico methodologies. Objective:: The aim of the current investigation was to find novel 2-chloroquinoline-3-carboxamide derivatives that target the Ephrin B4 (EPHB4) receptor to treat cancer. Materials and Methods:: Chem Axon Marvin Sketch 5.11.5 was used to create derivatives of 2-chloroquinoline-3-carboxamide. The physicochemical characteristics of compounds as well as their toxicity were predicted using SwissADME& the admet SAR online software’s. Molecular docking technology was used to examine the ligand-receptor interactions of 2-chloroquinoline-3-carboxamide derivatives with the target receptor (PDB- 6FNM) using a variety of software’s, including Autodock1.1.2,Procheck, ProtParam tool, Biovia Discovery Studio Visualizer v20.1.0.19295, MGL Tools 1.5.6, PyMOL, and were all included. Results:: All developed compounds were determined to be orally bioavailable, less toxic, and have acceptable pharmacokinetic properties according to in silico studies. In comparison to the traditional medication Erdafitnib, all new compounds displayed higher docking scores. Conclusion:: The increase in binding energy and the number of H-bonds created by novel derivatives with interactions at distances below 3.40A provide a helpful starting point for formulating and synthesizing compounds that are most suitable for additional research. The application of the 2- chloroquinoline-3-carboxamide moiety as a potential new cancer treatment candidate is supported by its pharmacokinetics &toxicological profile, which may aid medicinal chemists in conducting more in-depth in vitro, in vivo chemical and pharmacological studies.

Exploring Lead-Like Molecules of Traditional Chinese Medicine for Treatment Quest against Aliarcobacter butzleri: In Silico Toxicity Assessment, Dynamics Simulation, and Pharmacokinetic Profiling.

Aliarcobacter butzleri is a Gram-negative, curved or spiral-shaped, microaerophilic bacterium and causes human infections, specifically diarrhea, fever, and sepsis. The research objective of this study was to employ computer-aided drug design techniques to identify potential natural product inhibitors of a vital enzyme in this bacterium. The pyrimidine biosynthesis pathway in its core genome fraction is crucial for its survival and presents a potential target for novel therapeutics. Hence, novel small molecule inhibitors were identified (from traditional Chinese medicinal (TCM) compound library) against it, which may be used for possible curbing of infection by A. butzleri. Methods. A comprehensive subtractive genomics approach was utilized to identify a key enzyme (orotidine-5'-phosphate decarboxylase) cluster conserved in the core genome fraction of A. butzleri. It was selected for inhibitor screening due to its vital role in pyrimidine biosynthesis. TCM library (n > 36,000 compounds) was screened against it using pharmacophore model based on orotidylic acid (control), and the obtained lead-like molecules were subjected to structural docking using AutoDock Vina. The top-scoring compounds, ZINC70454134, ZINC85632684, and ZINC85632721, underwent further scrutiny via a combination of physiological-based pharmacokinetics, toxicity assessment, and atomic-scale dynamics simulations (100 ns). Among the screened compounds, ZINC70454134 displayed the most favorable characteristics in terms of binding, stability, absorption, and safety parameters. Overall, traditional Chinese medicine (TCM) compounds exhibited high bioavailability, but in diseased states (cirrhosis, renal impairment, and steatosis), there was a significant decrease in absorption, Cmax, and AUC of the compounds compared to the healthy state. Furthermore, MD simulation demonstrated that the ODCase-ZINC70454134 complex had a superior overall binding affinity, supported by PCA proportion of variance and eigenvalue rank analysis. These favorable characteristics underscore its potential as a promising drug candidate. The computer-aided drug design approach employed for this study helped expedite the discovery of antibacterial compounds against A. butzleri, offering a cost-effective and efficient approach to address infection by it. It is recommended that ZINC70454134 should be considered for further experimental analysis due to its indication as a potential therapeutic agent for combating A. butzleri infections. This study provides valuable insights into the molecular basis of biophysical inhibition of A. butzleri through TCM compounds.

USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPROACHES TO ENHANCE CANCER THERAPY AND DRUG DISCOVERY: A NARRATIVE REVIEW.

This paper looks at how AI and machine learning have been applied over the last ten years to the development of anti-cancer drugs. By speeding up the synthesis of more desirable compounds and the identification of new ones, artificial intelligence (AI) has demonstrated substantial contributions to the research and therapy of anti-cancer therapies. This work is a narrative review that examines numerous uses of AI-based techniques in the development of anti-cancer medications. Future developments in human cancer research and treatment are anticipated to be significantly influenced by AI. Protein-interaction network analysis, drug target prediction, binding site prediction, and virtual screening are examples of innovative techniques. Drug design and screening are enhanced by machine learning, and the use of multitarget drug development approaches has made it possible to develop cancer treatments with fewer side effects. AI does, however, have several drawbacks, such as a heavy reliance on data and a narrow scope of explanation. Interpretable AI models, which combine data and computation in AI-assisted cancer treatment research, will be the new development path in the future. For more than thirty years, computer-aided drug design techniques have been a key component in the advancement of cancer therapies. Artificial intelligence is a new and powerful technology that has the potential to speed up, lower the cost, and improve the efficacy of anti-cancer therapy development.

New Drug Design to Suppress Nonalcoholic Steatohepatitis

A de novo designed biomolecule called INASHD was utilized through computer-aided drug design techniques to specifically target β2-spectrin, effectively suppressing and preventing NASH disease. Advanced computational software tools concerning the technologies of molecular docking and molecular dynamics (MD), were employed to showcase the drug's remarkable ability to efficiently suppress and control the α-helical topology of β2-spectrin. This protein is a vital component within the disease pathway. We successfully devised an effective design suppressing β2-spectrin, exhibiting an inhibition score surpassing any other molecule documented in scientific literature. With robust support from validated computational software, this bioorganic structure holds significant value and can be applied for a patent due to its innovative design. It shows promising potential for delivering positive outcomes in various stages, including in vitro, in vivo, ex vivo, and human phase studies.

Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment.

Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products.

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Molecular dynamics exploration of Lupenone: therapeutic implications for glioblastoma multiforme and alzheimer's amyloid beta pathogenesis.

Neuro-oncological and neurodegenerative disorders, represented paradigmatically by glioblastoma and Alzheimer's disease, respectively, persist as formidable challenges in the biomedical realm. The interconnected molecular underpinnings of these conditions necessitate rigorous and novel therapeutic examinations. This comprehensive research was anchored on the premise of unveiling the therapeutic potential and specificity of Lupenone, a potent phytoconstituent, in targeting the molecular pathways underpinning both glioblastoma and Alzheimer's amyloid beta pathology. This was gauged through its interactions with key protein structures, 5H08 and 2ZHV. An integrative approach was adopted, marrying advanced proteomics and modern computer-aided drug design techniques. Molecular docking of Lupenone with 5H08 and 2ZHV was meticulously executed, with subsequent molecular dynamics simulations providing insights into the stability, viability, and intricacies of these interactions. Lupenone demonstrated profound binding affinities, evidenced by robust docking scores of -9.54kcal/mol for 5H08 and -10.59kcal/mol for 2ZHV. These interactions underscored Lupenone's eminent therapeutic potential in mitigating glioblastoma and modulating the amyloid beta pathology inherent to Alzheimer's. The introduction of Proteolysis Targeting Chimeras (PROTACs) further magnified the therapeutic prospects, accentuating Lupenone's efficacy. The findings of this study not only underscore the therapeutic acumen of Lupenone in addressing the challenges posed by glioblastoma and Alzheimer's but also lay a strong foundation for its consideration as a leading candidate in future neuro-oncological and neurodegenerative research endeavors. Given the compelling in-silico data, a clarion call is made for its empirical validation in holistic in-vivo settings, potentially pioneering a new therapeutic epoch in both glioblastoma and Alzheimer's interventions.

Computational 3D Modeling-Based Identification of Inhibitors Targeting Cysteine Covalent Bond Catalysts for JAK3 and CYP3A4 Enzymes in the Treatment of Rheumatoid Arthritis.

This work aimed to find new inhibitors of the CYP3A4 and JAK3 enzymes, which are significant players in autoimmune diseases such as rheumatoid arthritis. Advanced computer-aided drug design techniques, such as pharmacophore and 3D-QSAR modeling, were used. Two strong 3D-QSAR models were created, and their predictive power was validated by the strong correlation (R2 values > 80%) between the predicted and experimental activity. With an ROC value of 0.9, a pharmacophore model grounded in the DHRRR hypothesis likewise demonstrated strong predictive ability. Eight possible inhibitors were found, and six new inhibitors were designed in silico using these computational models. The pharmacokinetic and safety characteristics of these candidates were thoroughly assessed. The possible interactions between the inhibitors and the target enzymes were made clear via molecular docking. Furthermore, MM/GBSA computations and molecular dynamics simulations offered insightful information about the stability of the binding between inhibitors and CYP3A4 or JAK3. Through the integration of various computational approaches, this study successfully identified potential inhibitor candidates for additional investigation and efficiently screened compounds. The findings contribute to our knowledge of enzyme-inhibitor interactions and may help us create more effective treatments for autoimmune conditions like rheumatoid arthritis.