Models For Drug Design Research Articles

Harnessing Computational Modeling for Efficient Drug Design Strategies

Abstract: Computational modeling has become a crucial tool in drug design, offering efficiency and cost-effectiveness. This paper discusses the various computational modeling techniques used in drug design and their role in enabling efficient drug discovery strategies. Molecular docking predicts the binding affinity of a small molecule to a target protein, allowing the researchers to identify potential lead compounds and optimize their interactions. Molecular dynamics simulations provide insights into protein-ligand complexes, enabling the exploration of conformational changes, binding free energies, and fundamental protein-ligand interactions. Integrating computational modeling with machine learning algorithms, such as QSAR modeling and virtual screening, enables the prediction of compound properties and prioritizes potential drug candidates. High-performance computing resources and advanced algorithms are essential for accelerating drug design workflows, with parallel computing, cloud computing, and GPU acceleration reducing computational time. The paper also addresses the challenges and limitations of computational modeling in drug design, such as the accuracy of scoring functions, protein flexibility representation, and validation of predictive models. It emphasizes the need for experimental validation and iterative refinement of computational predictions to ensure the reliability and efficacy of designed drugs.

Persistent spectral-based machine learning (PerSpect ML) for protein-ligand binding affinity prediction.

Molecular descriptors are essential to not only quantitative structure-activity relationship (QSAR) models but also machine learning-based material, chemical, and biological data analysis. Here, we propose persistent spectral-based machine learning (PerSpect ML) models for drug design. Different from all previous spectral models, a filtration process is introduced to generate a sequence of spectral models at various different scales. PerSpect attributes are defined as the function of spectral variables over the filtration value. Molecular descriptors obtained from PerSpect attributes are combined with machine learning models for protein-ligand binding affinity prediction. Our results, for the three most commonly used databases including PDBbind-2007, PDBbind-2013, and PDBbind-2016, are better than all existing models, as far as we know. The proposed PerSpect theory provides a powerful feature engineering framework. PerSpect ML models demonstrate great potential to significantly improve the performance of learning models in molecular data analysis.

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Software > Molecular Modeling

Multiscale Modeling in the Clinic: Drug Design and Development.

A wide range of length and time scales are relevant to pharmacology, especially in drug development, drug design and drug delivery. Therefore, multiscale computational modeling and simulation methods and paradigms that advance the linkage of phenomena occurring at these multiple scales have become increasingly important. Multiscale approaches present in silico opportunities to advance laboratory research to bedside clinical applications in pharmaceuticals research. This is achievable through the capability of modeling to reveal phenomena occurring across multiple spatial and temporal scales, which are not otherwise readily accessible to experimentation. The resultant models, when validated, are capable of making testable predictions to guide drug design and delivery. In this review we describe the goals, methods, and opportunities of multiscale modeling in drug design and development. We demonstrate the impact of multiple scales of modeling in this field. We indicate the common mathematical and computational techniques employed for multiscale modeling approaches used in pharmacometric and systems pharmacology models in drug development and present several examples illustrating the current state-of-the-art models for (1) excitable systems and applications in cardiac disease; (2) stem cell driven complex biosystems; (3) nanoparticle delivery, with applications to angiogenesis and cancer therapy; (4) host-pathogen interactions and their use in metabolic disorders, inflammation and sepsis; and (5) computer-aided design of nanomedical systems. We conclude with a focus on barriers to successful clinical translation of drug development, drug design and drug delivery multiscale models.

Sphingolipids: Bioactive Metabolites and Models for Drug Design

Sphingolipids: Bioactive Metabolites and Models for Drug Design

Identification, by RT-PCR, of eight novel I2-conotoxins from the worm-hunting cone snails Conus brunneus, Conus nux, and Conus princeps from the eastern Pacific (Mexico)

Marine snails of the genus Conus (∼500 species) are tropical predators that produce venoms for capturing prey, defense and competitive interactions. These venoms contain 50–200 different peptides (“conotoxins”) that generally comprise 7–40 amino acid residues (including 0–5 disulfide bridges), and that frequently contain diverse posttranslational modifications, some of which have been demonstrated to be important for folding, stability, and biological activity. Most conotoxins affect voltage- and ligand-gated ion channels, G protein-coupled receptors, and neurotransmitter transporters, generally with high affinity and specificity. Due to these features, several conotoxins are used as molecular tools, diagnostic agents, medicines, and models for drug design. Based on the signal sequence of their precursors, conotoxins have been classified into genetic superfamilies, whereas their molecular targets allow them to be classified into pharmacological families. The objective of this work was to identify and analyze partial cDNAs encoding precursors of conotoxins belonging to I superfamily from three vermivorous species of the Mexican Pacific coast: C. brunneus, C. nux and C. princeps. The precursors identified contain diverse numbers of amino acid residues (C. brunneus, 65 or 71; C. nux, 70; C. princeps, 72 or 73), and all include a highly conserved signal peptide, a C-terminal propeptide, and a mature toxin. All the latter have one of the typical Cys frameworks of the I-conotoxins (C-C-CC-CC-C-C). The prepropeptides belong to the I2-superfamily, and encode eight different hydrophilic and acidic mature toxins, rather similar among them, and some of which have similarity with I2-conotoxins targeting voltage- and voltage-and-calcium-gated potassium channels.

Book Review

Abstract Book reviewed in this article: Liposomes in Biomedical Applications (Drug Targeting and Delivery, Volume 6) Edited by Pang N. Shek Microbial Quality Assurance in Cosmetics, Toiletries and Non-Sterile Pharmaceuticals (Second Edition) Edited by R. M. Baird and S. F. Bloomfield Dielectric Analysis of Pharmaceutical Systems Duncan Q. M. Craig Guidebook on Molecular Modeling in Drug Design Edited by N. Claude Cohen

Characterization of the interaction between cationic Erbium (III)–porphyrin complex with bovine serum albumin

The interaction of cationic Erbium (III)–porphyrin complex (Er–Porp) with bovine serum albumin (BSA) has been investigated by fluorescence quenching spectra, UV–vis absorbance, circular dichroism (CD) and three-dimensional (3D) fluorescence spectra. It is proved that the fluorescence quenching of BSA by Er–Porp was mainly for the formation of Er–Porp–BSA complex. The Stern–Volmer quenching constants K SV and corresponding thermodynamic parameters Δ H , Δ G and Δ S were estimated by fluorescence quenching method. The results indicated that the electrostatic and hydrophobic interactions were the predominant intermolecular forces in stabilizing the complex. The binding distance was obtained according to Förster’s non-radiative energy transfer theory. Displacement experiment and the number of binding sites calculation show that the cationic Er–Porp ring can inset in site-I (in subdomain IIA) of BSA. The effect of Er–Porp on the conformation of BSA was observed using CD, UV and 3D fluorescence spectra methods. The results show that the conformation of BSA was changed dramatically in the presence of Er–Porp by binding to the Trp residues of BSA. The interaction between BSA and Er–Porp can be used as a model for drug design and pharmaceutical research.

Parasitic helminths: a pharmacopeia of anti-inflammatory molecules

Infection with parasitic helminths takes a heavy toll on the health and well-being of humans and their domestic livestock, concomitantly resulting in major economic losses. Analyses have consistently revealed bioactive molecules in extracts of helminths or in their excretory/secretory products that modulate the immune response of the host. It is our view that parasitic helminths are an untapped source of immunomodulatory substances that, in pure form, could become new drugs (or models for drug design) to treat disease. Here, we illustrate the range of immunomodulatory molecules in selected parasitic trematodes, cestodes and nematodes, their impact on the immune cells in the host and how the host may recognize these molecules. There are many examples of the partial characterization of helminth-derived immunomodulatory molecules, but these have not yet translated into new drugs, reflecting the difficulty of isolating and fully characterizing proteins, glycoproteins and lipid-based molecules from small amounts of parasite material. However, this should not deter the investigator, since analytical techniques are now being used to accrue considerable structural information on parasite-derived molecules, even when only minute quantities of tissue are available. With the introduction of methodologies to purify and structurally-characterize molecules from small amounts of tissue and the application of high throughput immunological assays, one would predict that an assessment of parasitic helminths will yield a variety of novel drug candidates in the coming years.

Novel toxicity of the unedited GluR2 AMPA receptor subunit dependent on surface trafficking and increased Ca 2+-permeability

RNA editing modifies the GluR2 AMPA receptor subunit pore loop at the Q/R site and limits receptor Ca 2+ permeability. Editing failure is implicated in neurodegenerative diseases, including amyotrophic lateral sclerosis. We show that channels with unedited GluR2 are highly toxic in cultured hippocampal neurons. Toxicity exceeds that of other Ca 2+-permeable AMPA receptor types and is influenced by agonist binding site mutations, ability to desensitize, and extracellular Ca 2+ levels. Significantly, toxicity also depends on GluR2's constitutive surface trafficking, a function dependent on GluR2 C-terminal domain interaction with NSF, a specialized chaperone. We have exploited the interaction between unedited GluR2 and NSF to reduce GluR2 surface levels. We show that a peptide that blocks the GluR2-NSF interaction reduces unedited GluR2 toxicity by reducing receptor surface expression. Peptide block of trafficking provides a model for design of drugs to reduce unedited GluR2 excitotoxicity in neurodegenerative diseases that result from editing failure.

Molecular dynamics studies of alanine racemase: a structural model for drug design.

Alanine racemase (AlaR) is a bacterial enzyme that catalyzes the interconversion of L- and D-alanine, which is an essential constituent of the peptidoglycan layer of the bacterial cell wall and requires pyridoxal 5'-phosphate (PLP) as a cofactor. The enzyme is universal to bacteria, including mycobacteria, making it an attractive target for drug design. To investigate the effects of flexibility on the binding modes of the substrate and an inhibitor and to analyze how the active site is affected by the presence of the substrate versus inhibitor, a molecular dynamics simulation on the full AlaR dimer from Bacillus stearothermophilus (pdb code: 1SFT) with a D-alanine molecule in one active site and the noncovalent inhibitor, propionate, in the second site has been carried out. Within the time scale of the simulation, we show that the active site becomes more stabilized in the presence of substrate versus inhibitor. The results of this simulation are in agreement with the proposed mechanism of alanine racemase reaction in which the substrate carboxyl group directly participates in the catalysis by acting cooperatively with Tyr 265' and Lys 39. A structural water molecule in contact with both substrate and inhibitor (i.e., in both active sites) and bridging residues in both active sites was identified. It shows a remarkably low mobility and does not exchange with bulk water. This water molecule can be taken into account for the design of specific AlaR inhibitors by either utilizing it as a bridging group or displacing it with an inhibitor atom. The results presented here provide insights into the dynamics of the alanine racemase in the presence of substrate/inhibitor, which will be used for the rational design of novel inhibitors.

Guidebook on Molecular Modeling in Drug Design. By N. C. Cohen

first_page settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessBook Review Guidebook on Molecular Modeling in Drug Design. By N. C. Cohen by D. Craik FRACI, Centre for Drug Design & Development, University of Queensland, St Lucia, Australia Molecules 1997, 2(10), 154; https://doi.org/10.3390/21000154 Received: 17 September 1997 / Published: 31 October 1997 Download Download PDF Download PDF with Cover Download XML Download Epub Versions Notes This book is intended as a “guide for advanced students and chemists entering the field of molecular modeling”. The multi-author volume includes chapters which cover the topics of tools used for molecular graphics and modeling, molecular modeling of small molecules, computer assisted new lead design/drug discovery, experimental techniques and data banks, and modeling drug-receptor interactions. The editor is from the Pharmaceutical Division of Ciba-Geigy Ltd and hence has an excellent insight into the practical value of modelling techniques in drug design. The editor’s opening chapter provides an overview of molecular modeling in drug design which helps to illustrate the links between the subsequent chapters on the topics mentioned above. The final chapter is a glossary of terminology commonly used in the field of molecular modeling and drug design. This feature is a useful one and, in combination with the preceding chapters, helps the book to achieve its stated aim. Overall, the book promises to be a valuable source of information for chemists entering the field. One deficiency of the book is that it contains no colour illustrations. Since colour representations are such an integral part of modern modelling and graphics, this is a pity, however the decision not to include colour was probably taken to keep the cost of the book low. © 1997 MDPI. All rights reserved Share and Cite MDPI and ACS Style Craik, D. Guidebook on Molecular Modeling in Drug Design. By N. C. Cohen. Molecules 1997, 2, 154. https://doi.org/10.3390/21000154 AMA Style Craik D. Guidebook on Molecular Modeling in Drug Design. By N. C. Cohen. Molecules. 1997; 2(10):154. https://doi.org/10.3390/21000154 Chicago/Turabian Style Craik, D. 1997. "Guidebook on Molecular Modeling in Drug Design. By N. C. Cohen" Molecules 2, no. 10: 154. https://doi.org/10.3390/21000154 Find Other Styles Article Metrics No No Article Access Statistics For more information on the journal statistics, click here. Multiple requests from the same IP address are counted as one view.

Book Review: Guidebook on Molecular Modeling in Drug Design. Edited by N. C. Cohen

Book Review: Guidebook on Molecular Modeling in Drug Design. Edited by N. C. Cohen

Molecular recognition: Models for drug design

The review takes examples, mostly from the recent literature, to illustrate how an understanding of physico-chemical properties and an appreciation of the molecular shape and electronic properties can lead to a better insight into molecular recognition processes. The techniques used to generate 3-dimensional structures of molecules and the influence this information has had on the drug design cycle, are briefly discussed.

Molecular Modeling in Drug Design

Molecular Modeling in Drug Design

Molecular modeling in drug design.

Molecular recognition plays a central role in biological systems and is the basis of the specificity seen in antigen-antibody, substrate-enzyme, hormone-receptor, and drug-receptor interactions. The importance of this problem had been recognized by scientists like Pasteur and Ehrlich at the end of the nineteenth century. Structure-activity relations in which one probes the basis of recognition by chemically modifying one of the partners form a significant segment of medicinal chemistry. Attempts to correlate activity with physical properties such as solubility, refractive index, etc., are a reasonable approach to definining the characteristic properties associated with activity in a given biological assay (Hansch 1966). In most systems, these correlations reflect the effective concentration of the drug in the critical biophase rather than the critical features necessary for bimolecular recognition. We would like to concentrate on what can be deduced regarding bimolecular recognition from structure-activity data.