Application of Deep Learning for Studying NMDA Receptors.

Artificial intelligence underwent remarkable advancement in the past decade, revolutionizing our way of thinking and unlocking unprecedented opportunities across various fields, including drug development. The emergence of large pretrained models, such as ChatGPT, has even begun to demonstrate human-level performance in certain tasks.However, the difficulties of deploying and utilizing AI and pretrained model for nonexpert limited its practical use. To overcome this challenge, here we presented three highly accessible online tools based on a large pretrained model for chemistry, the Uni-Mol, for drug development against CNS diseases, including those targeting NMDA receptor: the blood-brain barrier (BBB) permeability prediction, the quantitative structure-activity relationship (QSAR) analysis system, and a versatile interface of the AI-based molecule generation model named VD-gen. We believe that these resources will effectively bridge the gap between cutting-edge AI technology and NMDAR experts, facilitating rapid and rational drug development.

Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors.

Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.

A python based algorithmic approach to optimize sulfonamide drugs via mathematical modeling.

This article explores the structural properties of eleven distinct chemical graphs that represent sulfonamide drugs using topological indices by developing python algorithm. To find significant relationships between the topological characteristics of these networks and the characteristics of the associated sulfonamide drugs. We use quantitative structure-property relationship (QSPR) approaches. In order to model and forecast these correlations and provide insights into the structure-activity relationships that are essential for drug design and optimization, linear regression is a vital tool. A thorough framework for comprehending the molecular characteristics and behavior of sulfonamide drugs is provided by the combination of topological indices, graph theory and statistical models which advances the field of pharmaceutical research and development.

Py-CoMFA, docking, and molecular dynamics simulations of Leishmania (L.) amazonensis arginase inhibitors.

Leishmaniasis is a disease caused by a protozoan of the genus Leishmania, affecting millions of people, mainly in tropical countries, due to poor social conditions and low economic development. First-line chemotherapeutic agents involve highly toxic pentavalent antimonials, while treatment failure is mainly due to the emergence of drug-resistant strains. Leishmania arginase (ARG) enzyme is vital in pathogenicity and contributes to a higher infection rate, thus representing a potential drug target. This study helps in designing ARG inhibitors for the treatment of leishmaniasis. Py-CoMFA (3D-QSAR) models were constructed using 34 inhibitors from different chemical classes against ARG from L. (L.) amazonensis (LaARG). The 3D-QSAR predictions showed an excellent correlation between experimental and calculated pIC50 values. The molecular docking study identified the favorable hydrophobicity contribution of phenyl and cyclohexyl groups as substituents in the enzyme allosteric site. Molecular dynamics simulations of selected protein-ligand complexes were conducted to understand derivatives' interaction modes and affinity in both active and allosteric sites. Two cinnamide compounds, 7g and 7k, were identified, with similar structures to the reference 4h allosteric site inhibitor. These compounds can guide the development of more effective arginase inhibitors as potential antileishmanial drugs.

Deep eutectic solvent self-assembled reverse nanomicelles for transdermal delivery of sparingly soluble drugs.

BACKGROUND: Transdermal delivery of sparingly soluble drugs is challenging due to their low solubility and poor permeability. Deep eutectic solvent (DES)/or ionic liquid (IL)-mediated nanocarriers are attracting increasing attention. However, most of them require the addition of auxiliary materials (such as surfactants or organic solvents) to maintain the stability of formulations, which may cause skin irritation and potential toxicity. RESULTS: We fabricated an amphiphilic DES using natural oxymatrine and lauric acid and constructed a novel self-assembled reverse nanomicelle system (DES-RM) based on the features of this DES. Synthesized DESs showed the broad liquid window and significantly solubilized a series of sparingly soluble drugs, and quantitative structure-activity relationship (QSAR) models with good prediction ability were further built. The experimental and molecular dynamics simulation elucidated that the self-assembly of DES-RM was adjusted by noncovalent intermolecular forces. Choosing triamcinolone acetonide (TA) as a model drug, the skin penetration studies revealed that DES-RM significantly enhanced TA penetration and retention in comparison with their corresponding DES and oil. Furthermore, in vivo animal experiments demonstrated that TA@DES-RM exhibited good anti-psoriasis therapeutic efficacy as well as biocompatibility. CONCLUSIONS: The present study offers innovative insights into the optimal design of micellar nanodelivery system based on DES combining experiments and computational simulations and provides a promising strategy for developing efficient transdermal delivery systems for sparingly soluble drugs.

Integrating mathematical approaches (IMAS): Novel methodology for predicting dermal absorption rates of chemicals under finite dose conditions.

Quantitative structure permeation relationship (QSPR) models have gained prominence in recent years owing to their capacity to elucidate the influence of physicochemical properties on the dermal absorption of chemicals. These models facilitate the prediction of permeation coefficient (Kp) values, indicating the skin permeability of a chemical under infinite dose conditions. Conversely, obtaining dermal absorption rates (DAs) under finite dose conditions, which are crucial for skin product safety evaluation, remains a challenge when relying solely on Kp predictions from QSPR models. One proposed resolution involves using Kroes' methodology, categorizing DAs based on Kp values; however, refinement becomes necessary owing to discreteness in the obtained values. We previously developed a mathematical model using Kp values obtained from in vitro dermal absorption tests to predict DAs. The present study introduces a new methodology, Integrating Mathematical Approaches (IMAS), which combines QSPR models and our mathematical model to predict DAs for risk assessments without conducting in vitro dermal absorption tests. Regarding 40 chemicals (76.1 ≤ MW ≤ 220; -1.4 ≤ Log Ko/w ≤ 3.1), IMAS showed that 65.0% (26/40) predictions of DA values were accurate to within twofold of the observed values in finite dose experiments. Compared to Kroes' methodology, IMAS notably mitigated overestimation, particularly for hydrophilic chemicals with water solubility exceeding 57.0 mg/cm3. These findings highlight the value of IMAS as a tool for skin product risk assessments, particularly for hydrophilic compounds.

Reactivities of acrylamide warheads toward cysteine targets: a QM/ML approach to covalent inhibitor design.

Covalent inhibition offers many advantages over non-covalent inhibition, but covalent warhead reactivity must be carefully balanced to maintain potency while avoiding unwanted side effects. While warhead reactivities are commonly measured with assays, a computational model to predict warhead reactivities could be useful for several aspects of the covalent inhibitor design process. Studies have shown correlations between covalent warhead reactivities and quantum mechanic (QM) properties that describe important aspects of the covalent reaction mechanism. However, the models from these studies are often linear regression equations and can have limitations associated with their usage. Applications of machine learning (ML) models to predict covalent warhead reactivities with QM descriptors are not extensively seen in the literature. This study uses QM descriptors, calculated at different levels of theory, to train ML models to predict reactivities of covalent acrylamide warheads. The QM/ML models are compared with linear regression models built upon the same QM descriptors and with ML models trained on structure-based features like Morgan fingerprints and RDKit descriptors. Experiments show that the QM/ML models outperform the linear regression models and the structure-based ML models, and literature test sets demonstrate the power of the QM/ML models to predict reactivities of unseen acrylamide warhead scaffolds. Ultimately, these QM/ML models are effective, computationally feasible tools that can expedite the design of new covalent inhibitors.

Exploring chemical space, scaffold diversity, and activity landscape of spleen tyrosine kinase active inhibitors.

This study aims to comprehensively characterize 576 inhibitors targeting Spleen Tyrosine Kinase (SYK), a non-receptor tyrosine kinase primarily found in haematopoietic cells, with significant relevance to B-cell receptor function. The objective is to gain insights into the structural requirements essential for potent activity, with implications for various therapeutic applications. Through chemoinformatic analyses, we focus on exploring the chemical space, scaffold diversity, and structure-activity relationships (SAR). By leveraging ECFP4 and MACCS fingerprints, we elucidate the relationship between chemical compounds and visualize the network using RDKit and NetworkX platforms. Additionally, compound clustering and visualization of the associated chemical space aid in understanding overall diversity. The outcomes include identifying consensus diversity patterns to assess global chemical space diversity. Furthermore, incorporating pairwise activity differences enhances the activity landscape visualization, revealing heterogeneous SAR patterns. The dataset analysed in this work has three activity cliff generators, CHEMBL3415598, CHEMBL4780257, and CHEMBL3265037, compounds with high affinity to SYK are very similar to compounds analogues with reasonable potency differences. Overall, this study provides a critical analysis of SYK inhibitors, uncovering potential scaffolds and chemical moieties crucial for their activity, thereby advancing the understanding of their therapeutic potential.

Structure-Based Design of Novel Thiazolone[3,2-a]pyrimidine Derivatives as Potent RNase H Inhibitors for HIV Therapy.

Ribonuclease H (RNase H) was identified as an important target for HIV therapy. Currently, no RNase H inhibitors have reached clinical status. Herein, a series of novel thiazolone[3,2-a]pyrimidine-containing RNase H inhibitors were developed, based on the hit compound 10i, identified from screening our in-house compound library. Some of these derivatives exhibited low micromolar inhibitory activity. Among them, compound 12b was identified as the most potent inhibitor of RNase H (IC50 = 2.98 µM). The experiment of magnesium ion coordination was performed to verify that this ligand could coordinate with magnesium ions, indicating its binding ability to the catalytic site of RNase H. Docking studies revealed the main interactions of this ligand with RNase H. A quantitative structure activity relationship (QSAR) was also conducted to disclose several predictive mathematic models. A molecular dynamics simulation was also conducted to determine the stability of the complex. Taken together, thiazolone[3,2-a]pyrimidine can be regarded as a potential scaffold for the further development of RNase H inhibitors.

Structure-property modeling of coumarins and coumarin-related compounds in pharmacotherapy of cancer by employing graphical topological indices.

Coumarins, a subgroup of colorless and crystalline oxygenated heterocyclic compounds originally discovered in the plant Dipteryx odorata, were the subject of a recent study investigating their quantitative structure-activity relationship (QSAR) in cancer pharmacotherapy. This study utilized graph theoretical molecular descriptors, also known as topological indices, as a numerical representation method for the chemical structures embedded in molecular graphs. These descriptors, derived from molecular graphs, play a pivotal role in quantitative structure-property relationship (QSPR) analysis. In this paper, intercorrelation between the Balban index, connective eccentric index, eccentricity connectivity index, harmonic index, hyper Zagreb index, first path Zagreb index, second path Zagreb index, Randic index, sum connectivity index, graph energy and Laplacian energy is studied on the set of molecular graphs of coumarins. It is found that the pairs of degree-based indices are highly intercorrelated. The use of these molecular descriptors in structure-boiling point modeling was analyzed. Finally, the curve-linear regression between considered molecular descriptors with physicochemical properties of coumarins and coumarin-related compounds is obtained.

Molecular Mechanistic Insights into Dipeptidyl Peptidase-IV Inhibitory Peptides to Decipher the Structural Basis of Activity.

Dipeptidyl peptidase-IV (DPP-IV) inhibiting peptides have attracted increased attention because of their possible beneficial effects on glycemic homeostasis. However, the structural basis underpinning their activities has not been well understood. This study combined computational and in vitro investigations to explore the structural basis of DPP-IV inhibitory peptides. We first superimposed the Xaa-Pro-type peptide-like structures from several crystal structures of DPP-IV ligand-protein complexes to analyze the recognition interactions of DPP-IV to peptides. Thereafter, a small set of Xaa-Pro-type peptides was designed to explore the effect of key interactions on inhibitory activity. The intramolecular interaction of Xaa-Pro-type peptides at the first and third positions from the N-terminus was pivotal to their inhibitory activities. Residue interactions between DPP-IV and residues of the peptides at the fourth and fifth positions of the N-terminus contributed significantly to the inhibitory effect of Xaa-Pro-type tetrapeptides and pentapeptides. Based on the interaction descriptors, quantitative structure-activity relationship (QSAR) studies with the DPP-IV inhibitory peptides resulted in valid models with high R2 values (0.90 for tripeptides; 0.91 for tetrapeptides and pentapeptides) and Q2 values (0.33 for tripeptides; 0.68 for tetrapeptides and pentapeptides). Taken together, the structural information on DPP-IV and peptides in this study facilitated the development of novel DPP-IV inhibitory peptides.

Characteristic structures of liquid crystal monomers in EI-MS analysis and the potential application in suspect screening.

Liquid crystal monomers (LCMs) are of emerging concern due to their ubiquitous presence in indoor and outdoor environments and their potential negative impacts on human health and ecosystems. Suspect screening approaches have been developed to monitor thousands of LCMs that could enter the environment, but an updated suspect list of LCMs is difficult to maintain given the rapid development of material innovations. To facilitate suspect screening for LCMs, in-silico mass fragmentation model and quantitative structure-activity relationship (QSPR) models were applied to predict electron ionization (EI) mass spectra of LCMs. The in-silico model showed limited predictive power for EI mass spectra, while the QSPR models trained with 437 published mass spectra of LCMs achieved an acceptable absolute error of 12 percentage points in predicting the relative intensity of the molecular ion, but failed to predict the mass-to-charge ratio of the base peak. A total of 41 characteristic structures were identified from an updated suspect list of 1606 LCMs. Multi-phenyl groups form the rigid cores of 85% of LCMs and produce 154 characteristic peaks in EI mass spectra. Monitoring the characteristic structures and fragments of LCMs may help identify new LCMs with the same rigid cores as those in the suspect list.

Identification of DprE1 inhibitors for tuberculosis through integrated in-silico approaches.

Decaprenylphosphoryl-ß-D-ribose-2'-epimerase (DprE1), a crucial enzyme in the process of arabinogalactan and lipoarabinomannan biosynthesis, has become the target of choice for anti-TB drug discovery in the recent past. The current study aims to find the potential DprE1 inhibitors through in-silico approaches. Here, we built the pharmacophore and 3D-QSAR model using the reported 40 azaindole derivatives of DprE1 inhibitors. The best pharmacophore hypothesis (ADRRR_1) was employed for the virtual screening of the chEMBL database. To identify prospective hits, molecules with good phase scores (> 2.000) were further evaluated by molecular docking studies for their ability to bind to the DprE1 enzyme (PDB: 4KW5). Based on their binding affinities (< - 9.0 kcal/mole), the best hits were subjected to the calculation of free-binding energies (Prime/MM-GBSA), pharmacokinetic, and druglikeness evaluations. The top 10 hits retrieved from these results were selected to predict their inhibitory activities via the developed 3D-QSAR model with a regression coefficient (R2) value of 0.9608 and predictive coefficient (Q2) value of 0.7313. The induced fit docking (IFD) studies and in-silico prediction of anti-TB sensitivity for these top 10 hits were also implemented. Molecular dynamics simulations (MDS) were performed for the top 5 hit molecules for 200 ns to check the stability of the hits with DprE1. Based on their conformational stability throughout the 200 ns simulation, hit 2 (chEMBL_SDF:357100) was identified as the best hit against DprE1 with an accepted safety profile. The MD results were also in accordance with the docking score, MM-GBSA value, and 3D-QSAR predicted activity. The hit 2 molecule, (N-(3-((2-(((1r,4r)-4-(dimethylamino)cyclohexyl)amino)-9-isopropyl-9H-purin-6-yl)amino)phenyl)acrylamide) could serve as a lead for the discovery of a novel DprE1 inhibiting anti-TB drug.

Development of a Robust Read-Across Model for the Prediction of Biological Potency of Novel Peroxisome Proliferator-Activated Receptor Delta Agonists.

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure-activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform's graphical user interface (GUI).

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

Potential antioxidant, α-glucosidase, butyrylcholinesterase and acetylcholinesterase inhibitory activities of major constituents isolated from Alpinia officinarum hance rhizomes: computational studies and in vitro validation.

Alpinia officinarum is a commonly used spice with proven folk uses in various traditional medicines. In the current study, six compounds were isolated from its rhizomes, compounds 1-3 were identified as diarylheptanoids, while 4-6 were identified as flavonoids and phenolic acids. The isolated compounds were subjected to virtual screening against α-glucosidase, butyrylcholinesterase (BChE), and acetylcholinesterase (AChE) enzymes to evaluate their potential antidiabetic and anti-Alzheimer's activities. Molecular docking and dynamics studies revealed that 3 exhibited a strong binding affinity to human a α- glucosidase crystal structure compared to acarbose. Furthermore, 2 and 5 demonstrated high potency against AChE. The virtual screening results were further supported by in vitro assays, which assessed the compounds' effects on α-glucosidase, cholinesterases, and their antioxidant activities. 5-Hydroxy-7-(4-hydroxy-3-methoxyphenyl)-1-phenylheptan-3-one (2) showed potent antioxidant effect in both ABTs and ORAC assays, while p-hydroxy cinnamic acid (6) was the most potent in the ORAC assay. In contrary, kaempferide (4) and galangin (5) showed the most potent effect in metal chelation assay. 5-Hydroxy-1,7-diphenylhepta-4,6-dien-3-one (3) and 6 revealed the most potent effect as α-glucosidase inhibitors where compound 3 showed more potent effect compared to acarbose. Galangin (5) revealed a higher selectivity to BChE, while 2 showed the most potent activity to (AChE).

Anti-inflammatory action of new hybrid N-acyl-[1,2]dithiolo-[3,4-c]quinoline-1-thione.

Most of pharmaceutical agents display a number of biological activities. It is obvious that testing even one compound for thousands of biological activities is not practically possible. A computer-aided prediction is therefore the method of choice in this case to select the most promising bioassays for particular compounds. Using the PASS Online software, we determined the probable anti-inflammatory action of the 12 new hybrid dithioloquinolinethiones derivatives. Chemical similarity search in the World-Wide Approved Drugs (WWAD) and DrugBank databases did not reveal close structural analogues with the anti-inflammatory action. Experimental testing of anti-inflammatory activity of the synthesized compounds in the carrageenan-induced inflammation mouse model confirmed the computational predictions. The anti-inflammatory activity of the studied compounds (2a, 3a-3k except for 3j) varied between 52.97% and 68.74%, being higher than the reference drug indomethacin (47%). The most active compounds appeared to be 3h (68.74%), 3k (66.91%) and 3b (63.74%) followed by 3e (61.50%). Thus, based on the in silico predictions a novel class of anti-inflammatory agents was discovered.

Influence of molecular descriptors of plant volatilomics on fumigant action against the three major stored product beetle pests.

Plant volatilomics such as essential oils (EOs) and volatile phytochemicals (PCs) are known as potential natural sources for the development of biofumigants as an alternative to conventional fumigant pesticides. This present work was aimed to evaluate the fumigant toxic effect of five selected EOs (cinnamon, garlic, lemon, orange, and peppermint) and PCs (citronellol, limonene, linalool, piperitone, and terpineol) against the Callosobruchus maculatus, Sitophilus oryzae, and Tribolium castaneum adults. Furthermore, for the estimation of the relationship between molecular descriptors and fumigant toxicity of plant volatiles, quantitative structural activity relationship (QSAR) models were developed using principal component analysis and multiple linear regression. Amongst the tested EOs, garlic EO was found to be the most toxic fumigant. The PCs toxicity analysis revealed that terpineol, limonene, linalool, and piperitone as potential fumigants to C. maculatus (< 20 µL/L air of LC50), limonene and piperitone as potential fumigants to T. castaneum (14.35 and 154.11 µL/L air of LC50, respectively), and linalool and piperitone as potential fumigants to S. oryzae (192.27 and 69.10 µL/L air of LC50, respectively). QSAR analysis demonstrated the role of various molecular descriptors of EOs and PCs on the fumigant toxicity in insect pest species. In specific, dipole and Randic index influence the toxicity in C. maculatus, molecular weight and maximal projection area influence the toxicity in S. oryzae, and boiling point and Dreiding energy influence the toxicity in T. castaneum. The present findings may provide insight of a new strategy to select effective EOs and/or PCs against stored product insect pests.

3D-QSAR-Directed Synthesis of Halogenated Coumarin-3-Hydrazide Derivatives: Unveiling Their Potential as SDHI Antifungal Agents.

The pursuit of new succinate dehydrogenase (SDH) inhibitors is a leading edge in fungicide research and development. The use of 3D quantitative structure-activity relationship (3D-QSAR) models significantly enhances the development of compounds with potent antifungal properties. In this study, we leveraged the natural product coumarin as a molecular scaffold to synthesize 74 novel 3-coumarin hydrazide derivatives. Notably, compounds 4ap (0.28 µg/mL), 6ae (0.32 µg/mL), and 6ah (0.48 µg/mL) exhibited exceptional in vitro effectiveness against Rhizoctonia solani, outperforming the commonly used fungicide boscalid (0.52 µg/mL). Furthermore, compounds 4ak (0.88 µg/mL), 6ae (0.61 µg/mL), 6ah (0.65 µg/mL), and 6ak (1.11 µg/mL) showed significant activity against Colletotrichum orbiculare, surpassing both the SDHI fungicide boscalid (43.45 µg/mL) and the broad-spectrum fungicide carbendazim (2.15 µg/mL). Molecular docking studies and SDH enzyme assays indicate that compound 4ah may serve as a promising SDHI fungicide. Our ongoing research aims to refine this 3D-QSAR model further, enhance molecular design, and conduct additional bioactivity assays.

Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulation.

GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibitors. In this study, multiclass classification-based QSAR models were developed using diverse machine learning algorithms. These models were built from curated compounds with experimentally derived GRK5 inhibitory activity. Additionally, a pharmacophore model was constructed using active compounds from the dataset. Among the models, the SVM-based approach proved most effective and was initially used to screen DrugBank compounds within the applicability domain. Compounds showing significant GRK5 inhibitory potential underwent evaluation for key pharmacophoric features. Prospective compounds were subjected to molecular docking to assess binding affinity towards GRK5's key active site amino acid residues. Stability at the binding site was analyzed through 200 ns molecular dynamics simulations. MM-GBSA analysis quantified individual free energy components contributing to the total binding energy with respect to binding site residues. Metadynamics analysis, including PCA, FEL, and PDF, provided crucial insights into conformational changes of both apo and holo forms of GRK5 at defined energy states. The study identifies DB02844 (S-Adenosyl-1,8-Diamino-3-Thiooctane) and DB13155 (Esculin) as promising GRK5 inhibitors, warranting further in vitro and in vivo validation studies.