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Special Issue "Computational Methods for Drug Discovery and Design"

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 30 December 2019

Special Issue Editor

Guest Editor
Prof. Julio Caballero

Centro de Bioinformática y Simulación Molecular (CBSM), Facultad de Ingeniería, Universidad de Talca, Casilla 747, Talca 3460000, Chile
Website | E-Mail
Phone: 56 71 201 662
Interests: molecular modeling; molecular simulations; computational biochemistry; computer-aided drug design; protein kinase inhibitors

Special Issue Information

Dear Colleagues,

In recent decades, drug design processes have been often assisted by computational methods. Such methods have been crucial to sustain the current development of medicinal chemistry research. It is rare to see a medicinal chemistry project without the support of computational methods belonging to the fields of pharmaceutical modeling, molecular modeling and simulation, cheminformatics, bioinformatics, computational chemistry, and biochemistry. These methods encompass tools that contribute to the finding of novel drugs or the processing of available information for creating useful knowledge about the interactions between bioactive ligands and their biological targets.

In this Special Issue, we are seeking original articles, short communications, or review articles focusing on the use of computational methods for drug design processes. Papers employing the computational methods available for in silico drug design, such as docking, molecular dynamics, QSAR, pharmacophore modeling, virtual screening, free energy calculations, density functional theory applications, and QM/MM, are welcome. Papers combining both experimental and computational studies are also desired.

Prof. Julio Caballero
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Molecular modeling
  • Molecular simulation
  • Computer-aided drug design
  • Docking
  • Molecular dynamics
  • QSAR
  • Virtual screening

Published Papers (1 paper)

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Research

Open AccessArticle
Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs
Molecules 2019, 24(7), 1258; https://doi.org/10.3390/molecules24071258
Received: 1 February 2019 / Revised: 9 March 2019 / Accepted: 14 March 2019 / Published: 31 March 2019
PDF Full-text (1027 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted [...] Read more.
The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds; a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds. Full article
(This article belongs to the Special Issue Computational Methods for Drug Discovery and Design)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: A novel strategy for prediction of human plasma protein binding using machine learning techniques
Author: Xiaoqiang Xiang
Abstract: Plasma protein binding (PPB) is a key player of drug ADME (absorption, distribution, metabolism, elimination) behavior which strongly influences drug efficacy and toxicity. As the drug discovery enters the era of rational drug design, it is desired to use in silico model to rapidly predict PPB to achieve initial screening prior to further time-consuming and costly in vitro and in vivo experimental assay. Most previous QSAR models of PPB were constructed relying on data from about 2000 compounds. In this study, a large training set containing more than 5000 compounds was used to build a global QSAR model of PPB aiming to cover compound of big structure diversity. Three types of molecular descriptors (2-D, 3-D and fingerprints) were calculated by an open-source software of PaDEL-Descriptor and two commercial software of ADMET Predictor and Dragon 7.0.Due to highly biased data distribution, PPB was transformed to logarithmic scale (fu,p_log) and a pseudo-equilibrium constant parameter (lnKa) previously proposed. However, we found that the two transformations only led to the improvement of prediction accuracy for the high binding compounds whereas the prediction accuracy was reduced for compounds of low binding. Therefore, we herein proposed a novel strategy for PPB prediction. In order to achieve even distribution, the data was divided into three levels by the threshold value of PPB setting to 0.8, 0.4 and employed to construct individual models for each level. Prior to the exact prediction of PPB, one classification model was constructed using boost tree algorithms to define the PPB of a certain compound to be low, median or high category. Five nonlinear machine learning techniques were utilized for individual PPB model construction of each level and further combined to generate an average consensus model. The two transformations of PPB were also employed and compared for model construction. Overall, the performance of models improved both in high binding level and low binding level. The best model yielded much lower mean absolute error (MAE) ranging from 0.036 to 0.039 for high level and 0.085 to 0.099 for low level than ever published. Meanwhile, no significant improvement was achieved for the compounds with median PPB values. The models also performed excellent in some compounds from traditional Chinese medicine. The applicability domain was defined based on the molecular descriptors to limit the scope of prediction and estimate prediction accuracy. In conclusion, this study developed a novel strategy to construct robust model of PPB prediction which could be used by chemists to evaluate the ADME behavior of candidate compounds efficiently and make structure modification in the early stage of drug development.

Title: The performance of MM/GBSA and umbrella sampling to estimate peptide binding affinities: A case study of peptide binding in PDZ domain
Author: Lu Zhang
Abstract: Molecular interactions between protein and ligand play central role in regulating and controlling the function of proteins. Binding affinity is one common measurement to quantify the protein-ligand interactions. Nowadays, popular approaches in estimating binding free energies include the molecular mechanics combined with Generalized Born Surface Area (MM/GBSA) or Poisson-Boltzmann Surface Area (MM/PBSA), as well as umbrella sampling, which have provided useful insights into the drug design and biomolecular engineering. PDZ domain serves as a commonly-used drug target for peptide therapeutics and herein we have applied three approaches to estimate the binding affinities of several peptides with PDZ domain. Our results demonstrate all methods have their own merits. Umbrella sampling can provide the binding process but turns out to be computationally expensive. Both MM/GBSA and MM/PBSA overestimate the binding free energies, but they can offer correct rankings of the binding affinities of the peptides. Therefore, MM/GBSA and MM/PBSA may serve as a quick ranking tool if absolute binding free energies are not required, while umbrella sampling is irreplaceable if the binding process is the focus.

Title: Computer-aided discovery of small molecule TOX1 inhibitors as potential therapeutics for skin cancer
Authors: Vibudh Agrawal 1,2, Mingwan Su 3, Yuanshen Huang 3, Michael Hsing 1, Youwen Zhou 3,* and Artem Cherkasov 1,*
Abstract: Skin cancer is the most common malignancy, with over 80,000 cases diagnosed in Canada each year, more than 5,000 of which are melanoma (the mostly deadly form). We have recently identified Thymocyte selection-associated high mobility group box protein (TOX) as a potential drug target in skin cancer. There are currently no small molecules to directly inhibit TOX. We aim to address this unmet problem by developing anti-TOX therapeutics with the use of computer-aided drug discovery technology.
The available NMR-resolved structure of TOX protein from the PDB database (ID: 2CO9) has been used to model DNA-binding HMG box domain. To investigate the druggability of the corresponding protein-DNA interface of TOX, we have performed a pilot in silico screening of 200,000 small molecules probe compounds using molecular docking software Glide and identified ‘hot spots’ for drug-binding on the HMG box. We then performed a full-scale virtual screening of 7.6 million drug-like small molecules that are available from the latest ZINC15 database using our computational protocols. A total of 127 top candidate compounds have been selected for wet-lab validation. Of those, experimental in vitro screening has identified several potential small molecules that can selectively inhibit TOX protein.

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