Ligand-based Approach Research Articles

Development of 1,2,3-triazole hybrids as multi-faced anticancer agents co-targeting EGFR/mTOR pathway and tubulin depolymerization.

Novel 1,2,3-triazole hybrids bearing various substituents have been synthesized as potential anticancer agents. Ligand-based approach has been adopted to design these compounds relying on the hybridization of 1,2,3-triazole with α,β-unsaturated carbonyl, 5- and 6-membered heterocyclic scaffolds. All synthesized members were investigated for their cytotoxic potency against nine types comprising 60 panels of human cancerous cells by the US National Cancer Institute: Development Therapeutic Program (US_NCI_DTP). Among the tested members, 4b, 4e, and 4h showed prominent cytotoxic effects (> 80% growth inhibition: GI) on a wide panel of tested cancer cell lines, mainly melanoma and colorectal cancer redeeming their selection for five dose testing. Presenting low nanomolar GI50 concentrations, two representative potent anticancer compounds 4b and 4e were subjected to cytotoxicity testing on colon normal cell (FHC) to investigate their safety window and they showed less toxicity to normal cells at the concentration required to produce anticancer effect. Furthermore, 4b and 4e were exposed to additional mechanistic studies in colorectal cancer cell HCT-116 suggesting multifaceted mechanisms of action. A study into the effects of cytotoxic chemicals 4b and 4e on cell cycle progression regulation showed triggered the arrest of cell cycles during the G1 and S phases. Moreover, 4b and 4e caused cell death mainly through apoptosis the thing that has been reinforced by the elevated Bax: Bcl2 ratio, as well as concentrations of caspases 3 and 9 within HCT-116. Further, both compounds showed prominent inhibition profiles against tubulin polymerization as well as EGFR catalytic activity reaching down to low-digit micromolar and sub-micromolar concentrations, respectively, as compared to positive reference controls. Compounds' impacts on gene expression of cancer-associated and EGFR-downstream signaling markers including TNFα, IL-6, and mTOR, were explored in HCT-116 highlighted significant downregulations versus the untreated cells. Docking studies demonstrated the specific fit of 4b and 4e into EGFR and the colchicine binding site of tubulin.

Integrated application of target-based and ligand-based drug-designing approaches for the identification of novel caspase-6 inhibitors

Caspase-6 (CASP6) is an effector caspase that has been marked to possess various pathological attributes associated with neurodegeneration. It is widely expressed in the neurodegenerative brain and peripheral tissues. It plays a vital role in apoptotic cell death and also performs non-apoptotic functions like axon pruning which contribute to the degeneration of neurons. Increment in active CASP6 levels in the cerebrospinal fluid has been observed during inflammation and has been linked to the early onset of Alzheimer’s disease (AD). In the current study, a novel CASP6 inhibitor was identified with the help of integrated target-based and ligand-based drug-designing approaches. Various molecular features of US9 (PDB ID 8EG6) were used to generate models. The pharmacophore models were evaluated using the EF value, GH score, and percentage yield to select the best-suited model. The best model was used to screen the ZINC-15 database to obtain virtual hits. The undesirable compounds were eliminated using various nodes in KNIME workflow. The resulting compounds were further subjected to docking-based virtual screening (DBVS) to find the lead compounds. Further, the molecular docking studies were carried out in three stages, followed by pharmacokinetic property prediction and toxicity studies. The top two virtual hits, i.e. ZINC000012563650 and ZINC000069415222, were considered for molecular dynamics simulation studies. Compound ZINC000069415222 was found to possess better stability, drug-like properties, and lower toxicity under simulated conditions. Thus, ZINC000069415222 was identified as a potential CASP6 inhibitor that could be further explored experimentally as an anti-AD drug.

Proposition of In silico Pharmacophore Models for Malaria: A Review.

In the field of medicinal chemistry, the concept of pharmacophore refers to the specific region of a molecule that possesses essential structural and chemical characteristics for binding to a receptor and eliciting biological activity. Understanding the pharmacophore is crucial for drug research and development, as it allows the design of new drugs. Malaria, a widespread disease, is commonly treated with chloroquine and artemisinin, but the emergence of parasite resistance limits their effectiveness. This study aims to explore computer simulations to discover a specific pharmacophore for Malaria, providing new alternatives for its treatment. A literature review was conducted, encompassing articles proposing a pharmacophore for Malaria, gathered from the "Web of Science" database, with a focus on recent publications to ensure up-to-date analysis. The selected articles employed diverse methods, including ligand-based and structurebased approaches, integrating molecular structure and biological activity data to yield comprehensive analyses. Affinity evaluation between the proposed pharmacophore and the target receptor involved calculating free energy to quantify their interaction. Multiple linear regression was commonly utilized, though it is sensitive to multicollinearity issues. Another recurrent methodology was the use of the Schrödinger package, employing tools such as the Phase module and the OPLS force field for interaction analysis. Pharmacophore model proposition allows threedimensional representations guiding the synthesis and design of new biologically active compounds, offering a promising avenue for discovering therapeutic agents to combat Malaria.

Discovery of Novel Thiazole-Based SIRT2 Inhibitors as Anticancer Agents: Molecular Modeling, Chemical Synthesis and Biological Assays.

The search and development of effective sirtuin small molecule inhibitors (SIRTIs) continues to draw great attention due to their wide range of pharmacological applications. Based on SIRTs' involvement in different biological pathways, their ligands were investigated for many diseases, such as cancer, neurodegenerative disorders, diabetes, cardiovascular diseases and autoimmune diseases. The elucidation of a substantial number of SIRT2-ligand complexes is steering the identification of novel and more selective modulators. Among them, SIRT2 in the presence of the SirReal2 analog series was the most studied. On this basis, we recently reported structure-based analyses leading to the discovery of thiazole-based compounds acting as SIRT2 inhibitors (T1, SIRT2 IC50 = 17.3 µM). Herein, ligand-based approaches followed by molecular docking simulations allowed us to evaluate in silico a novel small series of thiazoles (3a-3d and 5a, 5d) as putative SIRT2 inhibitors. Results from the computational studies revealed comparable molecular interaction fields (MIFs) and docking positionings of most of these compounds with respect to reference SIRT2Is. Biochemical and biological assays validated this study and pointed to compound 5a (SIRT2 IC50 = 9.0 µM) as the most interesting SIRT2I that was worthy of further development as an anticancer agent.

Data-augmented machine learning scoring functions for virtual screening of YTHDF1 m6A reader protein

Machine learning is rapidly advancing the drug discovery process, significantly enhancing speed and efficiency. Innovation in computer-aided drug design is primarily driven by structure- and ligand-based approaches. When the number of known inhibitors for a target is limited, data augmentation strategies are often preferred to enhance model performance. In this study, we developed predictive machine learning models for structure-based drug discovery leveraging multiple traditional machine learning algorithms trained with target and ligand dynamics-aware datasets.To illustrate our approach, we present a composite model that combines classification and regression to predict YTHDF1 inhibitors, utilizing PLEC features. YTHDF1, a key m6A reader protein involved in mRNA translation, is implicated in various cancers, making it a promising therapeutic target. Traditional structure-based virtual screening (SBVS) using generic scoring functions has struggled to identify potent YTHDF1 inhibitors due to the protein's unique binding characteristics. To overcome this, we developed YTHDF1-specific machine learning scoring functions (MLSFs) to enhance SBVS efficacy.We employed various data augmentation techniques to generate a comprehensive dataset, incorporating multiple conformations of ligands and the YTHDF1 protein. We have trained 64 YTHDF1-specific MLSFs using four machine learning algorithms and evaluated them on ten test sets, focusing on their predictive and ranking power. Our results demonstrate that the artificial neural network with protein-ligand extended connectivity fingerprints (ANN-PLEC) outperforms other MLSFs, consistently achieving high area under the precision-recall curve (PR-AUC) of 0.87. This method shows promise for targets with limited quantities of active molecules, providing a viable path forward for drug discovery research. The ANN-PLEC scoring function is made freely available on GitHub for other researchers to access and utilize https://github.com/JuniML/SBVS-YTHDF1/.

Hit-to-lead optimization of 4,5-dihydrofuran-3-sulfonyl scaffold against Leishmania amazonensis. Effect of an aliphatic moiety

In line with our objective of designing new antileishmanial compounds for oral use, we report the synthesis and biological evaluation in vitro of original 4,5-dihydrofuran derivatives bearing an amidoxime group. Previous optimization focused on position 3 of the dihydrofuran ring involving aromatic fragments, resulting in the identification of the compound (HIT) 4-(5-benzyl-3-((4-fluorophenyl)sulfonyl)-5-methyl-4,5-dihydrofuran-2-yl)-N′-hydroxybenzimidamide (IC50 = 5.4 ± 1.0 μM, L. amazonensis promastigote, IC50 = 7.9 ± 1.1 μM, L. amazonensis intracellular amastigote). In the present work, position 3 was substituted with an aliphatic moiety. This modification was guided by a ligand-based approach, given the unknown biological target or mechanism of action for this compound. The 4,5-dihydrofuran derivatives were synthesized using microwave-assisted manganese (III) acetate-based oxidative cyclization of linear β-keto-carboxylic and β-keto-sulfone substrates, overcoming synthetic challenges to obtain aliphatic derivatives of 4,5-dihydrofuran-3-carboxamides. Finally, an unexpected and interesting biological activity with the 4,5-dihydrofuran-3-carboxylate (IC50 < 5 μM) against the amastigote form is discussed.

Endogenous cell membrane interactome mapping for the GLP-1 receptor in different cell types.

The GLP-1 receptor, one of the most successful drug targets for the treatment of type 2 diabetes and obesity, is known to engage multiple intracellular signaling proteins. However, it remains less explored how the receptor interacts with proteins on the cell membrane. Here, we present a ligand-based proximity labeling approach to interrogate the native cell membrane interactome for the GLP-1 receptor after agonist simulation. Our study identified several unreported putative cell membrane interactors for the endogenous receptor in either a pancreatic β cell line or a neuronal cell line. We further uncovered new regulators of GLP-1 receptor-mediated signaling and insulinotropic responses in β cells. Additionally, we obtained a time-resolved cell membrane interactome map for the receptor in β cells. Therefore, our study provides a new approach that is generalizable to map endogenous cell membrane interactomes for G-protein-coupled receptors to decipher the molecular basis of their cell-type-specific functional regulation.

Functionalised Ligand-Based Nanomaterial Drug Targeting Approaches for Colorectal Cancer Therapy

: Cancer refers to a condition in which abnormal cells uncontrollably divide, resulting in the destruction of tissues. In colorectal cancer, uncontrolled cell proliferation takes place in the rectum or colon. Most colorectal tumors start as adenomas, a form of polyp that can develop into cancer within the rectum or colon. Symptoms of colorectal cancer include chronic diarrhea or constipation, bleeding from the rectum, bloody stools, change in appetite, weight loss, etc. Risk factors associated with colorectal cancer are smoking, obesity, and low physical activity. Colorectal cancer can be treated depending on size, location, and the spread of the cancer. Treatment includes surgery where the lymph node is dissected and a colectomy is performed. Chemotherapy and radiation therapies are other treatment options, but the main disadvantage is that these treatments have nonspecific avenues. Apart from killing cancerous cells, they damage healthy cells, too. Therefore, with the help of nanotechnology, drug-containing nanoparticles can be created with the aid of nanocarriers, which are stronger, more durable, and site-specific. The cancerous cells can be actively targeted by formulating nanoparticles loaded with anticancer drugs and functionalising the surface by either attaching ligands (peptides, antibodies, and small molecules) or targeting molecules towards receptors that are present on the exterior surface of the cancerous cells. These surfaces functionalised nanoparticles, loaded with anticancer drugs, are significantly upregulated in cancerous cells in contrast to healthy surrounding cells and tissues. This review article is focused on ligand-based drugs targeting colorectal tumours.

Ligand-Based Approach for Multi-Target Drug Discovery: PTML Modeling of Triple-Target Inhibitors.

Cancers are complex multi-genetic diseases that should be tackled in multi-target drug discovery scenarios. Computational methods are of great importance to accelerate the discovery of multi-target anticancer agents. Here, we employed a ligand-based approach by combining a perturbation-theory machine learning model derived from an ensemble of multilayer perceptron networks (PTML-EL-MLP) with the Fragment-Based Topological Design (FBTD) approach to rationally design and predict triple-target inhibitors against the cancerrelated proteins named Tropomyosin Receptor Kinase A (TRKA), poly[ADP-ribose] polymerase 1 (PARP-1), and Insulin-like Growth Factor 1 Receptor (IGF1R). We extracted the chemical and biological data from ChEMBL. We applied the Box- Jenkins approach to generate multi-label topological indices and subsequently created the PTML-EL-MLP model. Our PTML-EL-MLP model exhibited an accuracy of around 80%. The application FBTD permitted the physicochemical and structural interpretation of the PTML-EL-MLP model, thus enabling a) the chemistry-driven analysis of different molecular fragments with a positive influence on the multi-target activity and b) the use of those favorable fragments as building blocks to virtually design four new drug-like molecules. The designed molecules were predicted as triple-target inhibitors against the aforementioned cancer-related proteins. Our study envisages the capabilities of combining PTML modeling with FBTD for the generation of new chemical diversity for multi-target drug discovery in oncology research and beyond.

Pinpointing prime structural attributes of potential MMP-2 inhibitors comprising alkyl/arylsulfonyl pyrrolidine scaffold: a ligand-based molecular modelling approach validated by molecular dynamics simulation analysis

ABSTRACT MMP-2 overexpression is strongly related to several diseases including cancer. However, none of the MMP-2 inhibitors have been marketed as drug candidates due to various adverse effects. Here, a set of sulphonyl pyrrolidines was subjected to validation of molecular modelling followed by binding mode analysis to explore the crucial structural features required for the discovery of promising MMP-2 inhibitors. This study revealed the importance of hydroxamate as a potential zinc-binding group compared to the esters. Importantly, hydrophobic and sterical substituents were found favourable at the terminal aryl moiety attached to the sulphonyl group. The binding interaction study revealed that the S1′ pocket of MMP-2 similar to ‘a basketball passing through a hoop’ allows the aryl moiety for proper fitting and interaction at the active site to execute potential MMP-2 inhibition. Again, the sulphonyl pyrrolidine moiety can be a good fragment necessary for MMP-2 inhibition. Moreover, some novel MMP-2 inhibitors were also reported. They showed the significance of the 3rd position substitution of the pyrrolidine ring to produce interaction inside S2′ pocket. The current study can assist in the design and development of potential MMP-2 inhibitors as effective drug candidates for the management of several diseases including cancers in the future.

Ligand-Based Pharmacophoric Design and Anti-inflammatory Evaluation of Triazole Linked Semisynthetic Labdane Conjugates.

This study employed a ligand-based pharmacophoric approach to design and synthesize 33 novel semisynthetic labdane-appended triazolyl isatins to discover potential anti-inflammatory agents. The anti-inflammatory efficacy of the derivatives was evaluated by their ability to inhibit the production of NO, TNF-α, and IL-6, in lipopolysaccharide-induced RAW264.7 macrophages. The initial screening revealed that compound 7a ((1-(2-(2,3-dioxoindolin-1-yl)ethyl)-1H-1,2,3-triazol-4-yl)methyl (E)-3-formyl-5-((1S,4aS,8aS)-5,5,8a-trimethyl-2-methylenedecahydronaphthalen-1-yl)pent-3-enoate) exhibited an anti-inflammatory effect (NO inhibition, IC50 = 3.13 μΜ), surpassing both the positive control indomethacin (NO inhibition, IC50 = 7.31 μΜ) and the parent compound labdane dialdehyde. Notably, 7a reduced the levels of pro-inflammatory cytokines TNF-α and IL-6 while increasing the levels of the anti-inflammatory cytokine IL-10. Mechanistic studies revealed that 7a downregulated the expression of COX-2 and iNOS by inhibiting the NF-κB signaling pathway. In silico molecular modeling studies on NF-κB proteins support these findings, suggesting that 7a is a promising candidate for developing into a potent anti-inflammatory clinical agent.

Scaffold-Hopped Compound Identification by Ligand-Based Approaches with a Prospective Affinity Test.

Scaffold-hopped (SH) compounds are bioactive compounds structurally different from known active compounds. Identifying SH compounds in the ligand-based approaches has been a central issue in medicinal chemistry, and various molecular representations of scaffold hopping have been proposed. However, appropriate representations for SH compound identification remain unclear. Herein, the ability of SH compound identification among several representations was fairly evaluated based on retrospective validation and prospective demonstration. In the retrospective validation, the combinations of two screening algorithms and four two- and three-dimensional molecular representations were compared using controlled data sets for the early identification of SH compounds. We found that the combination of the support vector machine and extended connectivity fingerprint with bond diameter 4 (SVM-ECFP4) and SVM and the rapid overlay of chemical structures (SVM-ROCS) showed a relatively high performance. The compounds that were highly ranked by SVM-ROCS did not share substructures with the active training compounds, while those ranked by SVM-ECFP4 were mostly recombinant. In the prospective demonstration, 93 SH compounds were prepared by screening the Namiki database using SVM-ROCS, targeting ABL1 inhibitors. The primary screening using surface plasmon resonance suggested five active compounds; however, in the competitive binding assays with adenosine triphosphate, no hits were found.

Identification of ligands binding to MB327-PAM-1, a binding pocket relevant for resensitization of nAChRs

Desensitization of nicotinic acetylcholine receptors (nAChRs) can be induced by overstimulation with acetylcholine (ACh) caused by an insufficient degradation of ACh after poisoning with organophosphorus compounds (OPCs). Currently, there is no generally applicable treatment for OPC poisoning that directly targets the desensitized nAChR. The bispyridinium compound MB327, an allosteric modulator of nAChR, has been shown to act as a resensitizer of nAChRs, indicating that drugs binding directly to nAChRs can have beneficial effects after OPC poisoning. However, MB327 also acts as an inhibitor of nAChRs at higher concentrations and can thus not be used for OPC poisoning treatment. Consequently, novel, more potent resensitizers are required. To successfully design novel ligands, the knowledge of the binding site is of utmost importance. Recently, we performed in silico studies to identify a new potential binding site of MB327, MB327-PAM-1, for which a more affine ligand, UNC0646, has been described. In this work, we performed ligand-based screening approaches to identify novel analogs of UNC0646 to help further understand the structure-affinity relationship of this compound class. Furthermore, we used structure-based screenings and identified compounds representing four new chemotypes binding to MB327-PAM-1. One of these compounds, cycloguanil, is the active metabolite of the antimalaria drug proguanil and shows a higher affinity towards MB327-PAM-1 than MB327. Furthermore, cycloguanil can reestablish the muscle force in soman-inhibited rat muscles. These results can act as a starting point to develop more potent resensitizers of nAChR and to close the gap in the treatment after OPC poisoning.

The Histone Deacetylase Family: Structural Features and Application of Combined Computational Methods.

Histone deacetylases (HDACs) are crucial in gene transcription, removing acetyl groups from histones. They also influence the deacetylation of non-histone proteins, contributing to the regulation of various biological processes. Thus, HDACs play pivotal roles in various diseases, including cancer, neurodegenerative disorders, and inflammatory conditions, highlighting their potential as therapeutic targets. This paper reviews the structure and function of the four classes of human HDACs. While four HDAC inhibitors are currently available for treating hematological malignancies, numerous others are undergoing clinical trials. However, their non-selective toxicity necessitates ongoing research into safer and more efficient class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches, such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships, and structure-based virtual screening (molecular docking). Moreover, recent developments in the field of molecular dynamics simulations, combined with Poisson-Boltzmann/molecular mechanics generalized Born surface area techniques, have improved the prediction of ligand binding affinity. In this review, we delve into the ways in which these methods have contributed to designing and identifying HDAC inhibitors.

A Computational Approach for the Discovery of Novel DNA Methyltransferase Inhibitors.

Nowadays, the explosion of knowledge in the field of epigenetics has revealed new pathways toward the treatment of multifactorial diseases, rendering the key players of the epigenetic machinery the focus of today's pharmaceutical landscape. Among epigenetic enzymes, DNA methyltransferases (DNMTs) are first studied as inhibition targets for cancer treatment. The increasing clinical interest in DNMTs has led to advanced experimental and computational strategies in the search for novel DNMT inhibitors. Considering the importance of epigenetic targets as a novel and promising pharmaceutical trend, the present study attempted to discover novel inhibitors of natural origin against DNMTs using a combination of structure and ligand-based computational approaches. Particularly, a pharmacophore-based virtual screening was performed, followed by molecular docking and molecular dynamics simulations in order to establish an accurate and robust selection methodology. Our screening protocol prioritized five natural-derived compounds, derivatives of coumarins, flavones, chalcones, benzoic acids, and phenazine, bearing completely diverse chemical scaffolds from FDA-approved "Epi-drugs". Their total DNMT inhibitory activity was evaluated, revealing promising results for the derived hits with an inhibitory activity ranging within 30-45% at 100 µM of the tested compounds.

In-silico screening and identification of glycomimetic as potential human sodium-glucose co-transporter 2 inhibitor

Sodium-glucose co-transporter 2 (SGLT2) is one of the important targets against type II diabetes mellitus. A typical SGLT2 inhibitor acts by inhibiting glucose reabsorption, thus lowering the blood glucose level. Unlike SGLT1, SGLT2 is responsible for almost 90% glucose reabsorption from glomerular filtrate. The current SGLT2 inhibitors include gliflozins, often prescribed as second or third-line agents in diabetes mellitus. The SGLT2 inhibitors also benefit patients with heart and kidney disease. Due to instability issues with the natural O-aryl glycoside analogues C-glycoside analogues were developed and showed improved stability. Despite enhanced bioavailability and selectivity of newer derivatives, some serious side effects are associated with gliflozin analogues. At the present study, we applied in-silico approaches to find new glycomimetic compounds as potent SGLT2 inhibitors that could show improvement in side effects associated with current analogues. This work applied both ligand-based and structure-based drug approaches to find potential compounds. We developed a 3D-QSAR method to screen potential inhibitors from a library of ten thousand compounds and performed docking studies. The compounds were ranked based on predicted pIC50 and docking score. An initial screening of five thousand compounds was conducted, and the subsequently selected top 12 compounds were based on binding free energy calculations. These selected compounds were subjected to molecular dynamics (MD) simulations. Remarkably, our simulations identified nine compounds that exhibited significant and sustained binding affinity compared to the co-crystallized Empagliflozin. Collectively, considering the anticipated pharmacokinetic profiles and toxicity assessments, several of these compounds emerged as promising candidates for further in-depth evaluation.

Semaglutide as a Possible Calmodulin Binder: Ligand-Based Computational Analyses and Relevance to Its Associated Reward and Appetitive Behaviour Actions

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has gained considerable attention as a therapeutic agent for type 2 diabetes mellitus and obesity. Despite its clinical success, the precise mechanisms underlying its pharmacological effects remain incompletely understood. In this study, we employed ligand-based drug design strategies to investigate potential off-target interactions of semaglutide. Through a comprehensive in silico screening of semaglutide’s structural properties against a diverse panel of proteins, we have identified calmodulin (CaM) as a putative novel target of semaglutide. Molecular docking simulations revealed a strong interaction between semaglutide and CaM, characterized by favourable binding energies and a stable binding pose. Further molecular dynamics simulations confirmed the stability of the semaglutide–CaM complex, emphasizing the potential for a physiologically relevant interaction. In conclusion, our ligand-based drug design approach has uncovered calmodulin as a potential novel target of semaglutide. This discovery sheds light on the complex pharmacological profile of semaglutide and offers a promising direction for further research into the development of innovative therapeutic strategies for metabolic disorders. The CaM, and especially so the CaMKII, system is central in the experience of both drug- and natural-related reward. It is here hypothesized that, due to semaglutide binding, the reward pathway-based calmodulin system may be activated, and/or differently regulated. This may result in the positive semaglutide action on appetitive behaviour. Further studies are required to confirm these findings.

Proline-derived quinoline formamide compounds as human urate transporter 1 inhibitors with potent uric acid-lowering activities

We report the design and synthesis of a series of proline-derived quinoline formamide compounds as human urate transporter 1 (URAT1) inhibitors via a ligand-based pharmacophore approach. Structure−activity relationship studies reveal that the replacement of the carboxyl group on the polar fragment with trifluoromethanesulfonamide and substituent modification at the 6-position of the quinoline ring greatly improve URAT1 inhibitory activity compared with lesinurad. Compounds 21c, 21e, 24b, 24c, and 23a exhibit potent activities against URAT1 with IC50 values ranging from 0.052 to 0.56 μM. Furthermore, compound 23a displays improved selectivity towards organic anion transporter 1 (OAT1), good microsomal stability, low potential for genotoxicity and no inhibition of the hERG K+ channel. Compounds 21c and 23a, which have superior pharmacokinetic properties, also demonstrate significant uric acid-lowering activities in a mouse model of hyperuricemia. Notably, 21c also exhibits moderate anti-inflammatory activity related to the gout inflammatory pathway. Compounds 21c and 23a with superior druggability are potential candidates for the treatment of hyperuricemia and gout.

Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction.

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

Virtual screening and pharmacokinetics analysis of inhibitors against tuberculosis: Structure and ligand-based approach

Life-threatening diseases like tuberculosis have raised concerns in the medical and scientific communities. The damage-causing disease makes the scientific community employ the in-silico approach for design of new inhibitors that can inhibit or retard the havoc caused by this deadly disease. The insilico approach was used in this study to create a mathematical model with promising molecular properties, and receptors from the library were used to screen compounds and estimate the kinetic ability of the screened inhibitors that can cure this disease. 2D molecular properties evolved in the built model with high predictive ability. Three inhibitors x, y, and z emerged with better and higher molecular properties, the lowest binding energy (and higher binding affinity), and a better pharmacokinetic assessment compared to the template used in designing the effective compounds, with binding affinities of -15.56 kcal/mol, -18.51 kcal/mol, and -18.58 kcal/mol, respectively. Virtual screening of these compounds showed that they have good binding energy and excellent docking positions with the inhibiting potential of the receptor. Also, pharmacokinetic predictions and ADMET, depict orally active ability of the inhibitors, possess good human intestinal absorption, and violate none of the RO5 as potential drug candidates to cure this disease. Hence, further laboratory tests are recommended for these to determine their toxicities and biological assays.