Drug Design Techniques Research Articles

Recent Applications of In Silico Approaches for Studying Receptor Mutations Associated with Human Pathologies

In recent years, the advent of computational techniques to predict the potential activity of a drug interacting with a receptor or to predict the structure of unidentified proteins with aberrant characteristics has significantly impacted the field of drug design. We provide a comprehensive review of the current state of in silico approaches and software for investigating the effects of receptor mutations associated with human diseases, focusing on both frequent and rare mutations. The reported techniques include virtual screening, homology modeling, threading, docking, and molecular dynamics. This review clearly shows that it is common for successful studies to integrate different techniques in drug design, with docking and molecular dynamics being the most frequently used techniques. This trend reflects the current emphasis on developing novel therapies for diseases resulting from receptor mutations with the recently discovered AlphaFold algorithm as the driving force.

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.

Antimicrobial activity, toxicity prediction, computational investigation, and molecular docking studies of 2-thiophenecarbonitrile

DFT and Molecular docking are pivotal computational techniques in modern chemistry and drug design. This work investigates the electronic structure and reactivity of 2-thiophenecarbonitrile (2TCN) with an emphasis on important factors such HOMO-LUMO energy gap, MEP, Mulliken atomic charges, natural population analysis, and Mutiwfn ELF, LTD, ALIE, and RDG analysis performed using DFT. The MEP and FMO studies were calculated in various solvents like acetonitrile, water, gas, and methanol. The anti-inflammatory and antioxidant investigations revealed substantial activities by 2TCN. Additionally, molecular docking studies are performed to elucidate the binding interaction between the compound and target proteins, providing insights into its potential therapeutic mechanisms. The results demonstrate the binding energies, interaction residues, and the most favorable docking poses. This approach underscores the integration of theoretical and computational methods in advancing molecule design and therapeutic discovery.

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.

Discovery of Novel Neo-Clerodane Derivatives as Potent Dual-Functional Antiosteoporosis Agents through Targeting Peroxisome Proliferator-Activated Receptor-γ.

A library of 31 natural neo-clerodanes isolated from Ajuga decumbens was assayed for antiosteoporosis. This results in 18 neo-clerodane osteoclastogenesis inhibitors, and compound 3 prevents bone loss in vivo. Further mechanistic studies demonstrated that these compounds inhibit osteoporosis by antagonizing peroxisome proliferator-activated receptor-γ (PPARγ). We designed and synthesized 17 compounds by chemically modifying the natural neo-clerodane 19 (highly potent and the major composition of A. decumbens extract) by means of structure-based drug design techniques. Among these neo-clerodane derivatives, compound 34 is the most potent osteoporosis inhibitor with a 46-fold improvement in inhibiting osteoclastogenesis (IC50 = 0.042 vs 1.92 μM), 11-fold increased activity in PPARγ antagonism (EC50 = 0.75 vs 8.35 μM), 66-fold enhancement in receptor affinity (KD = 0.27 vs 17.7 μM), and enhanced osteogenic promotion compared to 19. This underscores the potential of neo-clerodane diterpenoids as promising leads for osteoporosis treatment by targeting PPARγ.

Cheminformatics-based analysis identified (Z)-2-(2,5-dimethoxy benzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one as an inhibitor of Marburg replication by interacting with NP

The highly pathogenic Marburg virus (MARV) is a member of the Filoviridae family, a non-segmented negative-strand RNA virus. This article represents the computer-aided drug design (CADD) approach for identifying drug-like compounds that prevent the MARV virus disease by inhibiting nucleoprotein, which is responsible for their replication. This study used a wide range of in silico drug design techniques to identify potential drugs. Out of 368 natural compounds, 202 compounds passed ADMET, and molecular docking identified the top two molecules (CID: 1804018 and 5280520) with a high binding affinity of −6.77 and −6.672 kcal/mol, respectively. Both compounds showed interactions with the common amino acid residues SER_216, ARG_215, TYR_135, CYS_195, and ILE_108, which indicates that lead compounds and control ligands interact in the common active site/catalytic site of the protein. The negative binding free energies of CID: 1804018 and 5280520 were −66.01 and −31.29 kcal/mol, respectively. Two lead compounds were re-evaluated using MD modeling techniques, which confirmed CID: 1804018 as the most stable when complexed with the target protein. PC3 of the (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) was 8.74 %, whereas PC3 of the 2′-Hydroxydaidzein (CID: 5280520) was 11.25 %. In this study, (Z)-2-(2,5-dimethoxybenzylidene)-6-(2-(4-methoxyphenyl)-2-oxoethoxy) benzofuran-3(2H)-one (CID: 1804018) unveiled the significant stability of the proteins' binding site in ADMET, Molecular docking, MM-GBSA and MD simulation analysis studies, which also showed a high negative binding free energy value, confirming as the best drug candidate which is found in Angelica archangelica which may potentially inhibit the replication of MARV nucleoprotein.

Crystallographic fragment screen of the c-di-AMP-synthesizing enzyme CdaA from Bacillus subtilis.

Crystallographic fragment screening has become a pivotal technique in structure-based drug design, particularly for bacterial targets with a crucial role in infectious disease mechanisms. The enzyme CdaA, which synthesizes an essential second messenger cyclic di-AMP (c-di-AMP) in many pathogenic bacteria, has emerged as a promising candidate for the development of novel antibiotics. To identify crystals suitable for fragment screening, CdaA enzymes from Streptococcus pneumoniae, Bacillus subtilis and Enterococcus faecium were purified and crystallized. Crystals of B. subtilis CdaA, which diffracted to the highest resolution of 1.1 Å, were used to perform the screening of 96 fragments, yielding data sets with resolutions spanning from 1.08 to 1.87 Å. A total of 24 structural hits across eight different sites were identified. Four fragments bind to regions that are highly conserved among pathogenic bacteria, specifically the active site (three fragments) and the dimerization interface (one fragment). The coordinates of the three active-site fragments were used to perform an in silico drug-repurposing screen using the OpenEye suite and the DrugBank database. This screen identified tenofovir, an approved drug, that is predicted to interact with the ATP-binding region of CdaA. Its inhibitory potential against pathogenic E. faecium CdaA has been confirmed by ITC measurements. These findings not only demonstrate the feasibility of this approach for identifying lead compounds for the design of novel antibacterial agents, but also pave the way for further fragment-based lead-optimization efforts targeting CdaA.

Uncovering the antidiabetic potential of heart-friendly and diuretic bioactive compounds through computer-based drug design

Avicenna, a pioneer of modern medicine, recommended diuretic therapy to treat diabetes. Like Avicenna's approach, current medicine frequently prescribes oral antidiabetic pills with diuretic and hypoglycemic effects by blocking the absorption of sodium and glucose. To this end, the paper sought natural compounds with potential antidiabetic, cardioprotective, and diuretic properties through computer-based drug design (CADD) techniques, targeting the inhibition of SGLT2 proteins. We identified several bioactive compounds from various sources exhibiting potential multifunctionality through high-throughput virtual screening (HTVS) of vast compound libraries. Subsequent molecular docking and dynamics simulations were employed to assess these compounds' binding efficacy and stability with their respective targets, alongside ADMET prediction, to evaluate their pharmacokinetic and safety profiles. The top hits, phenylalanyltryptophan, tyrosyl-tryptophan, tyrosyl-tyrosine, celecoxib, and DIBOA trihexose, had superior docking scores ranging from −11,4 to −9,8 kcal/mol. The molecular dynamics simulations displayed steady interactions between target proteins and biocompounds throughout 100 ns without significant conformational shifts. These findings lay the groundwork for lead optimization and preclinical testing. This meticulous process ensures the safety and efficacy of potential treatments, marking a meaningful step toward developing innovative treatments for managing diabetes and its associated health complications.

Quantitative structure-activity relationships modelling in antimicrobial peptides design

Antimicrobial peptides are antimicrobial agents with high bioactivity and low bacterial resistance, which inspire scientists to recognize AMPs as appealing candidates for microbial infection treatments. QSAR modeling is one of the most important techniques in computer-aided drug design that is typically beneficial to promoting the design of AMPs. This paper presents an overview about breakthroughs and achievements in AMPs designed by QSAR modeling in recent 10 years, and primarily talks about 6 successful AMPs discoveries covering different QSAR models and solving various antimicrobial problems such as biological activity, cytotoxicity and bacterial resistance, which is responsible for future developments in new generation of AMPs and optimization in QSAR modeling.

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.

Glycine-Conjugated α-Mangostins as Potential Estrogen Receptor Alpha (ERα) Antagonists through Pharmacophore Modeling, Docking Analysis, and Molecular Dynamics Simulations

Natural compounds have demonstrated good biological activity when combined with certain amino acids. For example, a glycine-conjugated glycyrrhetinic acid exhibits heightened efficiency against MCF7 cancer cells. Consequently, a molecular modeling analysis is conducted to construct glycine-conjugated α-mangostins and investigate their potential. According to pharmacophore modeling using the ligand-based drug design technique, only two glycine-conjugated α-mangostins conform to the pharmacophore features. The docking simulation results show that the Am1Gly conjugate can interact with the estrogen receptor-α (ERα) with a binding energy of −10.91 kcal/mol. This interaction is further supported by molecular dynamics simulations performed over a 200 ns timeframe. Based on molecular dynamics modeling using the MMPBSA method, the binding affinity of Am1Gly (ΔGTotal = −48.79 kcal/mol) is determined. The results of this analysis indicate that Am1Gly might function as an antagonist to estrogen receptors.

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

Discovery of HDM2010, a highly potent PTPN2 inhibitor for immunotherapy.

e15021 Background: Protein tyrosine phosphatase non-receptor type 2 (PTPN2), a negative regulator of JAK–STAT pathways that plays a crucial role in the immune system, has been identified as a promising target for immunotherapy. Genetic deletion of PTPN2 in either tumor cells or host immune cells promotes the anti-tumor immunity. In addition, PTPN2 inhibitors, alone or in combination with anti-PD1 therapy, significantly reduced tumor growth and enhanced survival in several subcutaneous tumour models. These findings demonstrate that PTPN2 inhibitors may have clinical benefits for immunotherapy-resistant tumors. Herein, the discovery of a series of novel PTPN2 inhibitors and their implications for immunotherapy are reported. Methods: We have successfully developed a series of small molecule inhibitors that specifically target PTPN2/N1. Molecular design was based on a novel backbone and incorporating both structure-based drug design (SBDD) and computational chemistry techniques. To evaluate the efficacy of our inhibitors, PTPN2 phosphatase activity was monitored using the small-molecule substrate DiFMUP in a fluorescence assay format in vitro. Tumor cells were treated with IFNγ and the cell viability was measured. Pharmacokinetic studies were conducted in mice, with serial blood samples analyzed by liquid chromatography−mass spectrometry (LC−MS)/MS. Pharmacokinetic parameters were calculated by noncompartmental analysis. Results: Based on the co-crystal structure of this target reported and multiple previous research projects, we have designed a multitude of novel compounds with the employment of computer-aided drug design (CADD) and scaffold hopping. Through a comprehensive evaluation of the in vitro and in vivo parameters, we have identified a series of compounds displaying potent PTPN2 inhibition (IC50 < 10 nM). Moreover, the compounds displayed remarkable efficacy in killing tumor cells to IFNγ treatment (IC50 < 100 nM). Artificial intelligence was utilized to conduct assessment and prediction of ADME properties, improving the drug-ability including permeability and physicochemical properties. Currently, a series of compounds demonstrating outstanding potency and good pharmacokinetic profiles, with lower hERG blockage (hERG IC50 > 30 µM), were synthesized and characterized. Among these hit compounds, a leading compound with PTPN2 IC50 = 1.1 nM and tumor cells IC50 = 79.5 nM was identified for further optimization and evaluation. Conclusions: We reported our work on discovering HDM2010, in which a series of potent PTPN2 inhibitors with prominent PK and safety profile were synthesized and characterized. Further optimization including in vivo experiments will lead to a preclinical candidate (PCC) selection.

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.

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

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

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.

DMXP: A De Novo Small-Molecule 3D Structure Predictor with Graph Attention Networks.

Generating the three-dimensional (3D) structure of small molecules is crucial in both structure- and ligand-based drug design. Structure-based drug design needs bioactive conformations of compounds for lead identification and optimization. Ligand-based drug design techniques, such as 3D shape similarity search, 3D pharmacophore model, 3D-QSAR, etc., all require high-quality small-molecule ligand conformations to obtain reliable results. Although predicting a small molecular bioactive conformer requires information from the receptor, a crystal structure of the molecule is a proper approximation to its bioactive conformer in a specific receptor because the binding pose of a small molecule in its receptor's binding pockets should be energetically close to the crystal structures. This study presents a de novo small molecular structure predictor (dMXP) with graph attention networks based on crystal data derived from the Cambridge Structural Database (CSD) combined with molecular electrostatic information calculated by density-functional theory (DFT). Two featuring strategies (topological and atomic partial change features) were employed to explore the relation between these features and the 3D crystal structure of a small molecule. These features were then assembled to construct the holistic 3D crystal structure of a molecule. Molecular graphs were encoded using a graph attention mechanism to deal with the issues of the inconsistencies of local substructures contributing to the entire molecular structure. The root-mean-square deviation (RMSDs) of approximately 80% dMXP predicted structures and the native binding poses within receptors are less than 2.0 Å.

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.