Ligand-based Drug Design Methods Research Articles

Rational design of novel phenol ether derivatives as non-covalent proteasome inhibitors through 3D-QSAR, molecular docking and ADMET prediction

Aim: The purpose of this paper is to use different structures and ligand-based drug design methods properly to provide theoretical guidance for the design of novel non-covalent proteasome inhibitors, and conduct theoretical analysis of the binding interaction mode between receptors and ligands. At the same time, the pharmacokinetic (PK) prediction, drug-likeness, and synthesis prediction were made for the screened novel drugs. Therefore, potentially attractive non-covalent proteasome inhibitors with low toxicity could be found as anticancer drugs. Methods: In this work, computer-aided drug design methods, including quantitative structure-activity relationship (QSAR), molecular docking, absorption, distribution, metabolism, excretion, and toxicology (ADMET) prediction, and drug-likeness prediction methods were performed. Results: In this study, the structure-activity relationship (SAR) of a series of non-covalent proteasome inhibitors were studied and the optimal comparative molecular field analysis (CoMFA; Q2 = 0.574, r2 = 0.999, r2pred = 0.755) and comparative molecular similarity indices analysis (CoMSIA)-SEHA (Q2 = 0.584, r2 = 0.989, r2pred = 0.921) models were obtained. According to the results of the QSAR model, some vital clues were found that would effectively enhance the biological activity of the compound. Based on these clues, 24 novel non-covalent proteasome inhibitors (D01–D24) were finally designed and screened. While the binding models between proteasome [protein data bank (PDB) code: 3MG6] and three representative compounds (15, 20, and D24) were also analyzed by using the molecular docking method. The results suggested that hydrogen bond and hydrophobic interaction played a key role in binding interaction between the receptor and ligand. In addition, the results of ADMET prediction indicated that the new designed compounds had reasonable PK parameters and drug-like properties. Conclusions: These statistical results can provide theoretical guidance for structural optimization, design, and synthesis of more effective non-covalent proteasome inhibitors in the future.

Ligand-based discovery of coronavirus main protease inhibitors using MACAW molecular embeddings

Ligand-based drug design methods are thought to require large experimental datasets to become useful for virtual screening. In this work, we propose a computational strategy to design novel inhibitors of coronavirus main protease, Mpro. The pipeline integrates publicly available screening and binding affinity data in a two-stage machine-learning model using the recent MACAW embeddings. Once trained, the model can be deployed to rapidly screen large libraries of molecules in silico. Several hundred thousand compounds were virtually screened and 10 of them were selected for experimental testing. From these 10 compounds, 8 showed a clear inhibitory effect on recombinant Mpro, with half-maximal inhibitory concentration values (IC50) in the range 0.18–18.82 μM. Cellular assays were also conducted to evaluate cytotoxic, haemolytic, and antiviral properties. A promising lead compound against coronavirus Mpro was identified with dose-dependent inhibition of virus infectivity and minimal toxicity on human MRC-5 cells.

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process.

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

3D Pharmacophore-Based Discovery of Novel KV10.1 Inhibitors with Antiproliferative Activity.

Simple SummaryA novel structural class of inhibitors of the voltage-gated potassium channel KV10.1 was discovered by a ligand-based drug design method using a 3D pharmacophore model. The virtual screening hit compound ZVS-08 inhibited the channel in a voltage-dependent manner consistent with the action of a gating modifier. Structure–activity relationship studies revealed a nanomolar KV10.1 inhibitor that is selective for some KV and NaV channels but exhibits significant inhibition of the hERG channel. KV10.1 inhibitor 1 inhibited the growth of the MCF-7 cell line expressing high levels of KV10.1 and low levels of hERG more potently than the Panc1 cell line (no KV10.1 and high hERG expression). Moreover, the KV10.1 inhibitor 1 induced significant apoptosis in tumour spheroids of Colo-357 cells. This study may provide a basis for the use of computational drug design methods for the discovery of novel KV10.1 inhibitors as new promising anticancer drugs.(1) Background: The voltage-gated potassium channel KV10.1 (Eag1) is considered a near- universal tumour marker and represents a promising new target for the discovery of novel anticancer drugs. (2) Methods: We utilized the ligand-based drug discovery methodology using 3D pharmacophore modelling and medicinal chemistry approaches to prepare a novel structural class of KV10.1 inhibitors. Whole-cell patch clamp experiments were used to investigate potency, selectivity, kinetics and mode of inhibition. Anticancer activity was determined using 2D and 3D cell-based models. (3) Results: The virtual screening hit compound ZVS-08 discovered by 3D pharmacophore modelling exhibited an IC50 value of 3.70 µM against KV10.1 and inhibited the channel in a voltage-dependent manner consistent with the action of a gating modifier. Structural optimization resulted in the most potent KV10.1 inhibitor of the series with an IC50 value of 740 nM, which was potent on the MCF-7 cell line expressing high KV10.1 levels and low hERG levels, induced significant apoptosis in tumour spheroids of Colo-357 cells and was not mutagenic. (4) Conclusions: Computational ligand-based drug design methods can be successful in the discovery of new potent KV10.1 inhibitors. The main problem in the field of KV10.1 inhibitors remains selectivity against the hERG channel, which needs to be addressed in the future also with target-based drug design methods.

Application of Virtual Screening Approaches for the Identification of Small Molecule Inhibitors of the Methyllysine Reader Protein Spindlin1.

Computer-based approaches represent a powerful tool which helps to identify and optimize lead structures in the process of drug discovery. Computer-aided drug design techniques (CADD) encompass a large variety of methods which are subdivided into structure-based (SBDD) and ligand-based drug design (LBDD) methods. Several approaches have been successfully used over the last three decades in different fields. Indeed also in the field of epigenetics, virtual screening (VS) studies and structure-based approaches have been applied to identify novel chemical modulators of epigenetic targets as well as to predict the binding mode of active ligands and to study the protein dynamics.In this chapter, an iterative VS approach using both SBDD and LBDD methods, which was successful in identifying Spindlin1 inhibitors, will be described. All protocol steps, starting from structure-based pharmacophore modeling, protein and database preparation along with docking and similarity search, will be explained in details.

Discovering New Acetylcholinesterase Inhibitors by Mining the Buzhongyiqi Decoction Recipe Data.

Myasthenia gravis (MG) is a neuromuscular disease that is conventionally treated with acetylcholinesterase (AChE) inhibitors, which may not fully remove the symptom for many reasons. When AChE inhibitors do not work, Chinese patients turn to Chinese medicine, such as the Buzhongyiqi decoction (BD), to treat MG. By elucidating the relations between the herbs of the Buzhongyiqi decoction recipe and AChE inhibitors with structure-based and ligand-based drug design methods and chemoinformatics approaches, we have found the key active components of BD. Using these key active components as templates, we have discovered five new AChE inhibitors through virtual screening of a commercial compound library. The new AChE inhibitors have been confirmed with Ellman assays. This study demonstrates that lead identification can be inspired by elucidating Chinese medicine. Since BD is a mixture, further studies against other drug targets are needed.

Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling.

3-D ligand conformations are required for most ligand-based drug design methods, such as pharmacophore modeling, shape-based screening, and 3-D QSAR model building. Many studies of conformational search methods have focused on the reproduction of crystal structures (i.e. bioactive conformations); however, for ligand-based modeling the key question is how to generate a ligand alignment that produces the best results for a given query molecule. In this work, we study different conformation generation modes of ConfGen and the impact on virtual screening (Shape Screening and e-Pharmacophore) and QSAR predictions (atom-based and field-based). In addition, we develop a new search method, called common scaffold alignment, that automatically detects the maximum common scaffold between each screening molecule and the query to ensure identical coordinates of the common core, thereby minimizing the noise introduced by analogous parts of the molecules. In general, we find that virtual screening results are relatively insensitive to the conformational search protocol; hence, a conformational search method that generates fewer conformations could be considered "better" because it is more computationally efficient for screening. However, for 3-D QSAR modeling we find that more thorough conformational sampling tends to produce better QSAR predictions. In addition, significant improvements in QSAR predictions are obtained with the common scaffold alignment protocol developed in this work, which focuses conformational sampling on parts of the molecules that are not part of the common scaffold.

3D-QSAR Modeling of Non-peptide Antagonists for the Human Luteinizing Hormone-releasing Hormone Receptor

The application of ligand-based drug design methods such as quantitative structure-activity relationship (QSAR) is a mandatory issue in the design of luteinizing hormone-releasing hormone (LHRH) receptor antagonists because the lack of information on the molecular structure for this target protein. The relationship between the structures and the antagonistic activities of 128 non-peptide antagonists for the LHRH receptor were modeled by using the classic QSAR methods comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best model included CoMSIA steric, electrostatic, hydrophobic and hydrogen bond donor fields, had a Q2 value of 0.780 and predicted adequately the activity of external compounds. The tridimensional contour maps generated were used to identify the key structural requirements responsible for a high biological activity of the compounds. These features should represent the ligand features involved in interactions with the target protein that modulate their potency as antagonists.

Ligand-based drug design for human endothelin converting enzyme-1 inhibitors

The endothelin converting enzyme-1 (ECE-1) is a zinc binding protein involved in the generation of a 21 amino acid endothelin-1 (ET-1) peptide. The mitogenic action of ET-1 has been shown in several cell types and produced by several human cancer cell lines. We have used the ligand-based drug design methods to understand the structure–activity relationship for inhibitor design of this important multi-disease target. The molecular docking of thiol-based inhibitors into the active site of ECE-1 identified several nonbonding interactions that stabilize the protein-inhibitor complex. The S1 and S1′ sub-sites could accommodate larger hydrophobic substitutions on the inhibitor with highest activity (molecule 16). From the 3D-QSAR studies, the electrostatic and steric descriptors that correlate with the activity of the inhibitors were identified. In the best pharmacophore, the hydrogen bond donor feature at the electronegative sulfur atom of inhibitor indicates that chelation with the Zn-ion is essential in the S1 sub-site, besides the other features. Together, the results from these diverse methods are complementary to each other and provide guidelines for the design of better ECE-1 inhibitors.

Pharmacophoric Models and 3D QSAR Studies of the Adenosine Receptor Ligands

Computational methodologies are used to increase the efficiency of drug discovery process by rendering the design of new drug candidates more rapid and cost-efficient. In silico techniques can be divided in two main groups. Structure-based drug design procedures can be applied (such as docking simulations) if the target is known from experimental (i.e., X-ray crystallographic studies, NMR studies) or theoretical sources (receptor structure built by homology modeling techniques). Otherwise, ligand-based drug design methods can be used (e.g., QSAR or 3D QSAR models, pharmacophoric models) based on the analysis of a number of ligands known to act with a common mechanism of action. Adenosine receptors (ARs) are a family of G-protein coupled receptors (GPCRs) of great interest as targets for therapeutic intervention. Due partly to the lack of reliable adenosine receptor structures, ligand-based drug discovery methods remain the major computational molecular modeling approach applied in the research of new AR ligands. The scope of this review is to summarize the results on pharmacophoric models and 3D QSAR studies concerning AR ligands. In particular, the review will focus on the use of such ligand-based computational techniques for the identification of new AR ligands and/or for their optimization.

Using a pharmacophore representation concept to elucidate molecular similarity of dopamine antagonists

The pharmacophoric concept plays an important role in ligand-based drug design methods to describe the similarity and diversity of molecules, and could also be exploited as a molecular representation scheme. A three-point pharmacophore method was used as a molecular representation perception. This procedure was implemented for dopamine antagonists of the D(2) receptor subtype. The molecular structures of the antagonists included in this analysis were categorized into two structurally distinct classes. Using structural superposition with internal energy minimization, two pharmacophore models were deduced. Based on these two models other D(2) antagonists that fulfil them were derived and studied. This procedure aided the identification of the common 3D patterns present in diverse molecules that act at the same biological target and the extraction of a common molecular framework for the two structural classes. The pharmacophoric information was found to be suitable for guiding superposition of structurally diverse molecules, using a more biologically meaningful selection of the targeting points.

AWD 12-281

Airway diseases such as bronchial asthma and chronic obstructive pulmonary disease (COPD) are chronic inflammatory diseases whose prevalence is increasing. Current research concerned with developing effective treatments for these conditions have focused on the search for alternatives to the standard corticosteroid antiinflammatory therapy. Selective phosphodiesterase 4 (PDE4) inhibitors have received a considerable amount of attention due to their ability to suppress the functions of several cell types involved in allergic and inflammatory disorders. Tne selective PDE4 inhibitor AWD 12-281 is the result of a pharmacophore-based synthesis program wherein the optimization process was supported by ligand-based drug design methods. AWD 12-281 was selected for further development for its high affinity and selectivity for the human PDE4 isoenzyme and due to its potent activity and excellent tolerability in models of allergic rhinitis, asthma and COPD, especially after topical treatment.