Computer-aided Drug Design Research Articles

An Overview of Molecular Docking

The computational modeling of structural complexes formed by two or more interacting molecules is known as molecular docking. Prediction of an interesting three-dimensional structure is the primary goal of molecular docking. Software for molecular docking is mostly employed in the development of drugs. Molecules and the simple use of structural databases caused damage to an important mechanism. Several expensive tools for drug design and research are provided by molecular docking. Simple molecular prediction as well as rapid access to structural databases have become important components on the medicinal chemist's desktop. Virtual screening is the most important contribution of molecular docking. Numerous docking programs were used to visualize the three-dimensional structure of the molecule, and different computational techniques can be used to analyze docking gain. In structural molecular biology and computer-aided drug design, molecular docking is a key tool. Docking is useful for lead optimization because it can be used to do virtual screening on huge collections of compounds, rate the results, and provide structural ideas for how the ligands affect the target.

Calculated hydration free energies become less accurate with increases in molecular weight

In order for computer-aided drug design to fulfil its long held promise of delivering new medicines faster and cheaper, extensive development and validation work must be done first. This pertains particularly to molecular dynamics force fields where one important aspect–the hydration free energy (HFE) of small molecules–is often insufficiently analyzed. While most benchmarking studies report excellent accuracies of calculated hydration free energies–usually within 2 kcal/mol of experimental values–we find that deeper analysis reveals significant shortcomings. Herein, we report a dependence of HFE prediction errors on ligand molecular weight–the higher the weight, the bigger the prediction error and the higher the probability the calculated result is erroneous by a large amount. We show that in the drug-like molecular weight region, HFE predictions can easily be off by 5 kcal/mol or more. This is likely to be highly problematic in a drug discovery and development setting. We make our HFE results and molecular descriptors freely and fully available in order to encourage deeper analysis of future molecular dynamics results and facilitate development of the next generation of force fields.

Chemical Profiling, In Silico Pharmacokinetics Analysis and Molecular Docking Study of Phytocompounds of Homeopathy Medicine Echinacea angustifolia Q against Suppurative Bacterial Proteins

Background Computer-aided drug design is the emerging field for identification of drug action on its target organs. The pharmacokinetic study helps find out drug ADMET (absorption, distribution, metabolism, excretion, and toxicity) and drug likeness properties. PyRx software helps with molecular docking of the bioactive compounds on their target organ within a short period of time. In homeopathy, Echinacea angustifolia is one of the most commonly used drugs for microbial infective skin lesions used in internal administration as well as an external application. Objective This study was aimed at identifying the compounds present in Echinacea angustifolia Q through liquid chromatography mass spectrometry (LC-MS), ADMET, and drug likeness properties, followed by molecular docking by PyRx software to recognize the interaction between drug compounds against 12 target microbial proteins of commonly infective suppurative bacteria. Materials and Methods An analysis of the phytoconstituents of Echinacea angustifolia Q was performed using the Agilent 1290 Infinity LC system coupled to the Agilent 6545 Accurate Mass Quadrupole Time-of-Flight (QTOF) with Agilent Jet Stream Thermal Gradient Technology. In silico ADMET and drug likeness properties were assessed by free online tools, followed by a detailed docking study by PyRx software. The drug compounds with the lower glide docking score are considered to have the best binding affinity. Result The five compounds of Echinacea angustifolia Q satisfy all properties of ADMET and drug likeness. The three compounds of Echinacea angustifolia Q have low glide score of >-7.0 kcal/mol against the three microbial target proteins of Salmonella enterica, Proteus mirabilis, and Staphylococcus aureus. Conclusion Echinacea angustifolia Q has fulfilled the ADMET and drug-likeness properties for internal administration as well as external application for various suppurative bacterial infective disorders, which were confirmed through molecular docking. Therefore, Echinacea angustifolia Q may function as a potential therapeutic agent for the treatment of illnesses associated with antimicrobial resistance.

From Nature to Drug: Overview and CADD Approach of Anacardic Acid to Propose their Biological Potential

Anacardic acids are natural compounds found in various plant families, such as Anacardiaceae, Geraniaceae, Ginkgoaceae, and Myristicaceae, among others. Several activities have been reported regarding these compounds, including antibacterial, antioxidant, anticancer, anti-inflammatory, and antiviral activities, showing the potential therapeutic applicability of these compounds. From a chemical point of view, they are structurally made up of salicylic acids substituted by an alkyl chain containing unsaturated bonds, which can vary in number and position, determining their bioactivity and differentiating them from the various existing forms. Our work aimed to explore the potential of anacardic acids, based on studies that address the bioactivity of these compounds, as well as to establish a greater understanding of the structure-activity relationship of these compounds through in silico methods, with a focus on the elucidation of a possible drug target through the application of computer-aided drug design, CADD.

Molecular hybridization assisted multi-technique approach for designing USP21 inhibitors to halt catalytic triad-mediated nucleophilic attack and suppress pancreatic ductal adenocarcinoma progression: A molecular dynamics study

AimsPancreatic cancer, the 12th-most common cancer, globally, is highly challenging to treat due to its complex epigenetic, metabolic, and genomic characteristics. In pancreatic ductal adenocarcinoma, USP21 acts as an oncogene by stabilizing the long isoform of Transcription Factor 7, thereby activating the Wnt signaling pathway. This study aims to inhibit activation of this pathway through computer-aided drug discovery. Accordingly, four libraries of compounds were designed to target the USP21's catalytic domain (Cys221, His518, Asp534), responsible for its deubiquitinating activity. Main methodsUtilizing an array of computer-aided drug design methodologies, such as molecular docking, virtual screening, principal component analysis, molecular dynamics simulation, and dynamic cross-correlation matrix, the structural and functional characteristics of the USP21-inhibitor complex were examined. Following the evaluation of the binding affinities, 20 potential ligands were selected, and the best ligand was subjected to additional molecular dynamics simulation study. Key findingsThe results indicated that the ligand-bound USP21 exhibited reduced structural fluctuations compared to the unbound form, as evident from RMSD, RMSF, Rg, and SASA graphs. ADMET analysis of the top ligand showed promising pharmacokinetic and pharmacodynamic profiles, good bioavailability, and low toxicity. The stable conformations of the proposed drug when bound to their target cavities indicate a robust binding affinity of −9.3 kcal/mol. The drug exhibits an elevated pKi value of 6.82, a noteworthy pIC50 value of 5.972, and a pKd value of 6.023 proving its high affinity and inhibitory potential towards the target. SignificanceIn-vitro testing of the top compound (MOLHYB-0436) could lead to its use as a potential treatment for pancreatic cancer.

Effect of Protein-Polarized Ligand Charges on Relative Protein Ligand Binding Affinities.

A major challenge in computer-aided drug design is predicting relative binding energies of different molecules to a target protein using fast and accurate free-energy calculation methods. Free-energy calculations are primarily computed by utilizing classical molecular dynamics simulations based on all-atom force fields (FF) to model the interactions in the system. The present standard classical all-atom FFs contain fixed partial charges on the atoms, and hence electrostatic interactions are modeled between them. The parametrization process to determine these partial charges usually relies on quantum mechanics or semiempirical calculations of the molecule in the gas phase or homogeneous water surrounding. These present standard parametrization schemes of the partial charges neglect, therefore, polarization effects from the protein surrounding. The absence of protein polarization effects can lead to significant errors in free-energy calculations in proteins. We present a parametrization scheme for the partial charges of ligands, named protein-induced polarization (PIP) charges, which account for the electrostatic polarization due to the protein surrounding. The scheme involves single-point quantum mechanics/molecular mechanics calculations of the ligand charges in the protein/water surrounding. Using PIP ligand partial charges, we have calculated the relative binding free energies (RBFEs) of well-studied protein-ligand systems. We show here that RBFEs computed with PIP charges are either significantly improved or at least comparable to those computed with nonpolarized standard GAFF charges. Overall, we present a simple-to-use parametrization scheme to include protein polarization in any type of binding free-energy calculations. The parametrization scheme increases the accuracy in RBFE calculations, while it does not add significant computation time to standard parametrization procedures.

Structural optimization and bioactivity evaluation of 2-(Methylcarbonylamino) thiazole derivatives as novel PDE4B inhibitors

Phosphodiesterase-4 (PDE4) is a protease belonging to the phosphodiesterase family, with a specific function of hydrolyzing intracellular cyclic adenosine monophosphate (cAMP). PDE4 is widely distributed across various human tissues and cells, where it plays a pivotal role in modulating intracellular cAMP levels, particularly in immune and inflammatory cells. Consequently, PDE4 inhibitors have been proven to effectively dampen inflammatory responses in these cells, leading to a reduction in the release of pro-inflammatory factors such as lipid mediators, reactive oxygen species (ROS) hydrolases, cytokines, and chemokines. Despite the considerable interest from both academia and pharmaceutical industries in exploiting this target for drug development, only a handful of PDE4 inhibitors are available in the market. The aim of this study was to identify novel PDE4B inhibitors through a combined approach of computer-aided drug design, synthesis, and activity evaluation. The study implemented three phases of structure optimization from the hit compound MR9, which was previously obtained by virtual screening, with reference to structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches. The optimized compound MR9-302 (PDE4B IC50 = 2.02 ± 0.2888 μM) exhibited enhanced inhibitory activity compared to MR9.

Computer-aided discovery of novel SmDHODH inhibitors for schistosomiasis therapy: Ligand-based drug design, molecular docking, molecular dynamic simulations, drug-likeness, and ADMET studies.

Schistosomiasis, also known as bilharzia or snail fever, is a tropical parasitic disease resulting from flatworms of the Schistosoma genus. This often overlooked disease has significant impacts in affected regions, causing enduring morbidity, hindering child development, reducing productivity, and creating economic burdens. Praziquantel (PZQ) is currently the only treatment option for schistosomiasis. Given the potential rise of drug resistance and the limited treatment choices available, there is a need to develop more effective inhibitors for this neglected tropical disease (NTD). In view of this, quantitative structure-activity relationship studies (QSAR), molecular docking, molecular dynamics simulations, drug-likeness, and ADMET predictions were applied to 31 inhibitors of Schistosoma mansoni Dihydroorotate dehydrogenase (SmDHODH). The designed QSAR model demonstrated robust statistical parameters including an R2 of 0.911, R2adj of 0.890, Q2cv of 0.686, R2pred of 0.807, and cR2p of 0.825, confirming its robustness. Compound 26, identified as the most active derivative, emerged as a lead candidate for new potential inhibitors through ligand-based drug design. Subsequently, 12 novel compounds (26A-26L) were designed with enhanced inhibition activity and binding affinity. Molecular docking studies revealed strong and stable interactions, including hydrogen bonding and hydrophobic interactions, between the designed compounds and the target receptor. Molecular dynamics simulations over 100 nanoseconds and MM-PBSA free binding energy (ΔGbind) calculations validated the stability of the two best-designed molecules (26A and 26L). Furthermore, drug-likeness and ADMET prediction analyses affirmed the potential of these designed compounds, suggesting their promise as innovative agents for treating schistosomiasis.

Computer-Aided Drug Design Using the Fragment Molecular Orbital Method: Current Status and Future Applications for SBDD.

Owing to the increasing use of computers, computer-aided drug design (CADD) has become an essential component of drug discovery research. In structure-based drug design (SBDD), including inhibitor design and in silico screening of drug target molecules, concordance with wet experimental data is important to provide insights on unique perspectives derived from calculations. Fragment molecular orbital (FMO) method is a quantum chemical method that facilitates precise energy calculations. Fragmentation method makes it possible to apply the quantum chemical method to biological macromolecules for energy calculation based on the electron behavior. Furthermore, interaction energies calculated on a residue-by-residue basis via fragmentation aid in the analysis of interactions between the target and ligand molecule residues and molecular design. In this review, we outline the recent developments in SBDD and FMO methods and highlight the prospects of developing machine learning approaches for large computational data using the FMO method.

The Importance of Structural Water in HDAC8 for Correct Binding Pose Applied for Drug Design of Anticancer Molecules.

Validating the docking procedure and maintaining the structural water molecules at HDAC8 catalytic site. Molecular docking simulations play a significant role in Computer-Aided Drug Design, contributing to the development of new molecules. To ensure the reliability of these simulations, a validation process called "self-docking or re-docking" is employed, focusing on the binding mode of a ligand co-crystallized with the protein of interest. In this study, several molecular docking studies were conducted using five X-ray structures of HDAC8-ligand complexes from the PDB. Ligands initially complexed with HDAC8 were removed and re-docked onto the free protein, revealing a poor reproduction of the expected binding mode. In response to this, we observed that most HDAC8-ligand complexes contained one to two water molecules in the catalytic site, which were crucial for maintaining the cocrystallized ligand. These water molecules enhance the binding mode of the co-crystallized ligand by stabilizing the proteinligand complex through hydrogen bond interactions between ligand and water molecules. Notably, these interactions are lost if water molecules are removed, as is often done in classical docking methodologies. Considering this, molecular docking simulations were repeated, both with and without one or two conserved water molecules near Zn+2 in the catalytic cavity. Simulations indicated that replicating the native binding pose of co-crystallized ligands on free HDAC8 without these water molecules was challenging, showing greater coordinate displacements (RMSD) compared to those including conserved water molecules from crystals. The study highlighted the importance of conserved water molecules within the active site, as their presence significantly influenced the successful reproduction of the ligands' native binding modes. The results suggest an optimal molecular docking procedure for validating methods suitable for filtering new HDAC8 inhibitors for future experimental assays.

Identification of potential inhibitors against Corynebacterium diphtheriae MtrA response regulator protein; an in-silico drug discovery approach

Corynebacterium diphtheriae is a multi-drug resistant bacteria responsible for the life-threatening respiratory illness, diphtheria which can lead to severe Nervous system disorders, mainly infecting the lungs, heart, and kidneys if left untreated. In the current study, Corynebacterium diphtheriae MtrA response regulator protein was targeted, which regulates a two-component system of bacterial pathogenesis, and initiates DNA replication and cell division. In the current study a computational approach have been described for drug development against C. diphtheriae infections by inhibiting MtrA protein by small molecules acting as potential inhibitors against it. Molecular docking analysis of the equilibrated MtrA protein revealed compound-0.2970, compound-0.3029, and compound-0.3016 from Asinex Library as the promising inhibitors based on their lowest binding energies (−9.8 kJ/mol, −9.2 kJ/mol, and −8.9 kJ/mol), highest gold scores (40.53, 47.41, and 48.41), drug-likeness and pharmacokinetic properties. The MD simulation studies of the identified top-ranked inhibitors at 100 ns elucidated the system stability and fluctuations in the binding pocket of MtrA protein. Molecular Dynamics Simulations of the top three docked complexes further revealed that the standard binding pocket was retained ensuring the system stability. The rearrangements of H-bonds, van der Waals, pi-pi, and solid hydrophobic interactions were also observed. The binding free energy calculations (MM/PBSA and MM/GBSA) suggested a fundamental binding capability of the ligand to the target receptor MtrA. Therefore, the current study has provided excellent candidates acting as potent inhibitors for developing therapeutic drugs against C. diphtheriae infections. However, in vivo and in vitro animal experiments and accurate clinical trials are needed to validate the potential inhibitory effect of these compounds.

Insilico Simulation Studies for Tuberculosis Drug Discovery and its Response

From high treatment and with such precaution measures TB (Tuberculosis) came with leading cause of deaths in the world. For Tuberculosis it increasing drug resistance for different kind of TB like Multidrug-resistant TB and Extensively drug-resistant TB, increasing relapse and in efficiency of drug for TB pathogen.it take longer duration, it affect on human body with infection from HIV and other autoimmune disorder have pushed it to a lethal level and for this we need more effective drugs. With the help of CADD technology we can findout more viable drug molecules for the better treatment of Mycobacterium tuberculosis .Insilico drug design is a more efficient ,time saving and cost effective process. With the help of inslico drug design we can identify/Generate lead molecules that can inhibit the activity of TB. In inslico process we took 4GZR protein which is Crystal structure of the Mycobacterium tuberculosis and lead(ligand) with the support of computer aided drug design (CADD) pipeline and bioinformatics software tools. Key words: Tuberculosis, Insilico Drug Design, Computer Aided Drug Design (CADD), Bioinformatics Tool and Technology.

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.

Antibacterial Activity, Toxicity and Drug-Likeness Profiles of Woodfordia fruticosa-Derived Metabolites Using Computational-Aided Drug Design Platform

This study presents a comprehensive investigation into the phytoconstituents reported from Woodfordia fruticosa (L.) Kurz leaf and flower extracts using gas chromatography-mass spectrometry (GC-MS) analysis, along with some existing phytochemicals, to explore their potential antibacterial properties through molecular docking studies. Followed by bio-assay-guided leave and flower extraction with two solvent systems, i.e., methanol (polar) and petroleum ether (non-polar), was used and further subjected to GC-MS to identify and quantify various secondary metabolites. Based on spectral intensity and volume area, a total of 28 compounds (P1 to P28) have been selected from GC-MS analyses, and an additional 14 compounds (P29 to P42) from previous reports were selected for molecular docking studies against DNA gyrase subunit B (GryB) of Escherichia coli (PDB ID: 7P2M) and Staphylococcus aureus (PDB ID: 5D7R) with novobiocin as the standard. Further, docking score or binding affinity (kcal/mol.) of each ligand were investigated, where the 4,5-dihydro-4,4-undeca methylene-2-phenyl-1,3-oxazin-6-one (P20) with a docking score of -8.4 kcal/mol., from the GC-MS-derived group and the chrysophanol-8-O-β-d-glucopyranoside (P37) with a docking score of -9.7 kcal/mol., from existing phytochemical groups were reported as potential antibacterial. The predicted toxicity and drug-ability profiles also suggested that GC-MS-derived candidates displayed comparatively higher non-toxic profiles but lower drug-likeness profiles than existing groups. This integrative approach explores the phytochemical profiles of W. fruticosa responsible for antibacterial activity of the crude extracts and providing insights into in selection of lead antibacterial agent through cost-effective computer-aided drug design platform to accelerate antibacterial drug discovery with higher chance of experimental success.

The Role of Water Networks in Phosphodiesterase Inhibitor Dissociation and Kinetic Selectivity.

In search of new opportunities to develop Trypanosoma brucei phosphodiesterase B1 (TbrPDEB1) inhibitors that have selectivity over the off-target human PDE4 (hPDE4), different stages of a fragment-growing campaign were studied using a variety of biochemical, structural, thermodynamic, and kinetic binding assays. Remarkable differences in binding kinetics were identified and this kinetic selectivity was explored with computational methods, including molecular dynamics and interaction fingerprint analyses. These studies indicate that a key hydrogen bond between GlnQ.50 and the inhibitors is exposed to a water channel in TbrPDEB1, leading to fast unbinding. This water channel is not present in hPDE4, leading to inhibitors with a longer residence time. The computer-aided drug design protocols were applied to a recently disclosed TbrPDEB1 inhibitor with a different scaffold and our results confirm that shielding this key hydrogen bond through disruption of the water channel represents a viable design strategy to develop more selective inhibitors of TbrPDEB1. Our work shows how computational protocols can be used to understand the contribution of solvent dynamics to inhibitor binding, and our results can be applied in the design of selective inhibitors for homologous PDEs found in related parasites.

SmartCADD: AI-QM Empowered Drug Discovery Platform with Explainability.

Artificial intelligence (AI) has emerged as a pivotal force in enhancing productivity across various sectors, with its impact being profoundly felt within the pharmaceutical and biotechnology domains. Despite AI's rapid adoption, its integration into scientific research faces resistance due to myriad challenges: the opaqueness of AI models, the intricate nature of their implementation, and the issue of data scarcity. In response to these impediments, we introduce SmartCADD, an innovative, open-source virtual screening platform that combines deep learning, computer-aided drug design (CADD), and quantum mechanics methodologies within a user-friendly Python framework. SmartCADD is engineered to streamline the construction of comprehensive virtual screening workflows that incorporate a variety of formerly independent techniques─spanning ADMET property predictions, de novo 2D and 3D pharmacophore modeling, molecular docking, to the integration of explainable AI mechanisms. This manuscript highlights the foundational principles, key functionalities, and the unique integrative approach of SmartCADD. Furthermore, we demonstrate its efficacy through a case study focused on the identification of promising lead compounds for HIV inhibition. By democratizing access to advanced AI and quantum mechanics tools, SmartCADD stands as a catalyst for progress in pharmaceutical research and development, heralding a new era of innovation and efficiency.

In Silico Screening of Phytocompounds from West African Traditional Medicine and Molecular Docking Targeting Dengue Virus Protein NS2B/NS3.

Traditional medicine offers a wide range of application for in silico study techniques. This drug research and development strategy is embryonic in the West African context, particularly in Burkina Faso, which is increasingly faced with emerging diseases such as dengue fever. Circulation of the 4 serotypes of this virus has been documented in the country. This study aims to evaluate the therapeutic potential of phytocompounds contained in the West African pharmacopoeia against dengue virus NS2B/NS3 protein, using computational methods integrating several software packages and databases. Based on a literature review, we identified 191 molecules from 30 plants known for their antiviral effects. Five met the inclusion criteria for molecular docking: patulin from calotropis procera, resiniferonol from Euphorbia poissonii, Securinol A from Flueggea virosa, Shikimic acid and Methyl gallate from Terminalia macroptera. The best binding scores were observed between resiniferonol and the serotypes 1, 2 and 4 NS2B/NS3 protease, with binding energies of -7.4 Kcal/mol, -6.8 Kcal/mol and -7.3 Kcal/mol respectively; while the NS2B/NS3 protease of serotype 3 had the best affinity for securinol A (-7 Kcal/mol). This study points the way to further research in computer aided drug design field and calls for multidisciplinary collaboration to promote West African medicinal plants against health challenges.

Pyridazine Derivatives: Molecular Docking, ADMET Prediction, and Synthesis for Antihypertensive Activity.

The drug discovery and development domain has witnessed remarkable advancements due to the integration of computational methods, particularly Computer-Aided Drug Design (CADD). Discovering and creating new drugs involves structural modifications to enhance their effectiveness and physical attributes. This frequently includes employing semisynthetic techniques to investigate structure-activity relationships thoroughly. Noticeable progress in molecular biology, computational chemistry, combinatorial chemistry, and highthroughput screening is steering transformative changes in the pharmaceutical industry. High blood pressure or hypertension, a significant health issue, elevates the chances of heart, kidney, and brain complications, among other health concerns. It's a leading cause of untimely mortality globally. Therefore, it is important to search for new antihypertensive compounds that have fewer side effects and higher therapeutic activity. Following molecular docking of the pyridazine derivatives, compounds were subjected to In-silico ADMET analysis. Subsequently, a low molecular weight compound was synthesized. Among the synthesized compounds characterization procedures include TLC, FT-IR, 1HNMR, and LC-MS techniques. Compound 8 exhibited the most favorable molecular docking results with alpha A1 and beta 1 adrenergic receptors. Compounds 3, 5, and 6 fulfilled the essential ADMET criteria. Subsequently, Compounds 3, 4, and 5 underwent additional synthesis and characterization procedures, including TLC, FT-IR, 1H-NMR, and LC-MS techniques. Similar behavior was observed in compounds 6, 8, 10, and 11, all violating Pfizer's 3/75 rules in terms of TPAS. Hydrazinolysis of these b-benzoyl propionic acids produced pyridazine, which was utilized in synthesizing pyridazine derivatives. TLC, FT-IR, 1HNMR, and LCMS have characterized the compounds.

Unveiling the potential of novel 5α-reductase inhibitors via ligand based drug design, molecular docking and ADME predictions to manage BPH

Benign prostatic hyperplasia (BPH), a prevalent condition among elderly males characterized by the non-cancerous enlargement of the prostate gland is due to the increased stromal and epithelial cell numbers. The enlargement is driven by an active dihydrotestosterone (DHT), synthesized from its prohormone testosterone under the catalytic effect of 5α-reductase Type-II (5-AR2) enzyme. To reduce the increased DHT levels and ultimately to control prostate growth, inhibition of the 5-AR represent an interesting therapeutic target. The 3D crystallography structure 5-AR2, a member of the steroid 5-AR family belonging to the class of oxidoreductase has been resolved recently and opened the door to develop selective novel inhibitors of 5-AR Type-II isoform using molecular modeling techniques. The Force field and Gaussian-field three-dimensional quantitative structure–activity relationship (3D-QSAR) models have been developed using 42 selective 5-AR2 inhibitors belonging to chemical class of steroidal androstene. Partial least squares analysis produced statistically significant model with R2training = 0.9367, 0.9459 and predictability Q2test = 0.7233, 0.8595. Developed 3D-QSAR model include steric, electrostatic, hydrophobic, and hydrogen bond acceptor and donor field indicators, whereas the potential field contributions indicated the importance of steric feature in governing their biological activity. Protein-ligand interaction pattern analysis revealed that amino acid residues GLU57, ARG114, TYR91 present in the active site of 5-AR2 are crucial for lower energy complexes with good binding affinity. Identified hits were finally adjudged for their druggability by determining in-silico pharmacokinetic parameters. Further, RMSD, binding free energy calculations and post MD analysis demonstrated the enhanced binding affinity with better ΔGbind value (-79.6 ± 3.9 kcal/mol) of identified ligand A-10 than the reference molecule finasteride (-69.13 ± 4.69 kcal/mol). Combined approach of ligand-based and structure-based models have provided an improved understanding of the structural properties of androstene class that may be further used to develop novel 5-AR2 inhibitors to be useful in BPH.

Mining Medicinally Relevant Bioreduction Substrates Inspired by Ligand-Based Drug Design.

Exploring the scope of biocatalytic transformations in the absence of enzyme structures without extensive experimentation is a challenging task. To expand the limited substrate capacity of carrot-mediated bioreduction and hunt for new medicinally relevant ketones with minimum cost of labor and time, we deployed a practical method inspired by ligand-based drug design. Through analyzing collected literature data and building pharmacophore and reactivity prediction models, we screened a self-built virtual library of >8000 ketones bearing the most frequently used N,O,S-heterocycles and functional groups in drug discovery. Representative examples were validated, expanding the bioreduction substrate scope. The public availability of our models alongside the straightforward screening workflow makes it time-, labor-, and cost-saving to evaluate unknown bioreduction substrates for medicinal chemistry applications, especially for a large set of structurally differentiated ketones. Our studies also showcase the novelty of utilizing medicinal chemistry principles to solve a general biocatalysis problem.