Molecular Modeling: Computer-Aided Drug Design

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Pharmaceutical Processes".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 7293

Special Issue Editor

Department of Animal Science, National Chung Hsing University, Taichung, City 40227, Taiwan
Interests: bioinformatics biology; computational chemistry; drug discovery; molecular simulation

Special Issue Information

Dear Colleagues,

Drug discovery is a resource intensive, challenging and time consuming process which typically requires a series of complex methods to make effective and safe medications.  It also requires the integration of knowledge and expertise from multiple disciplines to be successful.  Among those interdisciplinary efforts, computational techniques are expected to improve the efficiency of drug development and to speed up the discovery process.  Computer-aided Drug Design (CADD) is an approach widely utilized to productively yield hit or lead compounds which possess the potential to be biologically active candidates for further test.  We are interested in articles that discuss the current cutting edge CADD methodologies to tackle the ongoing innovation crisis faced by drug discovery.  Topics of interest include, but are not limited to, the following:

  • Introduction of novel virtual screening method to screen a large compound library for active compounds.
  • Study of Quantitative Structure-Activity Relationship (QSAR) to gain insight into structural details of active compounds and to optimize the physicochemical properties of candidate compounds.
  • Development of fragment-based approach to form a nucleating site of a molecular entity.
  • Application of machine learning to aid the identification of compounds which are promising to be active to target proteins.
  • Web-based programs for performing computational drug discovery with freely accessible facility.
  • Implementation of network pharmacology-based methods/tools to predict and analyze possible polypharmacology of a test compound.

Prof. Kun-Yi Hsin
Guest Editor

Manuscript Submission Information

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Keywords

  • Molecular Interaction
  • Molecular Simulation
  • Computational Drug Design
  • Network Pharmacology
  • Machine Learning

Published Papers (3 papers)

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Research

15 pages, 4022 KiB  
Article
In Silico Analysis of Plant Flavonoids as Potential Inhibitors of Newcastle Disease Virus V Protein
by Waseem Sarwar, Iram Liaqat, Tahira Yasmeen, Nazia Nahid, Saad Alkahtani, Ahmed A. Al-Qahtani, Muhammad Shah Nawaz-ul-Rehman and Muhammad Mubin
Processes 2022, 10(5), 935; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10050935 - 09 May 2022
Cited by 2 | Viewed by 1916
Abstract
Newcastle disease is a viral infection causing serious economic losses to the global poultry industry. The V protein of Newcastle disease virus (NDV) is a pathogenicity determinant having various functions such as the suppression of apoptosis and replication of the NDV. This study [...] Read more.
Newcastle disease is a viral infection causing serious economic losses to the global poultry industry. The V protein of Newcastle disease virus (NDV) is a pathogenicity determinant having various functions such as the suppression of apoptosis and replication of the NDV. This study was designed to assess the resistance potential of plant flavonoids against the V protein of Newcastle disease virus. Sequence analysis was performed using EXPASY and ProtParam tools. To build the three-dimensional structure of V protein, a homology-modeling method was used. Plant flavonoids with formerly reported therapeutic benefits were collected from different databases to build a library for virtual screening. Docking analysis was performed using the modeled structure of V protein on MOE software. Interaction analysis was also performed by MOE to explain the results of docking. Sequence analysis and physicochemical properties showed that V protein is negatively charged, acidic in nature, and relatively unstable. The 3D structure of the V protein showed eight β-pleated sheets, three helices, and ten coiled regions. Based on docking score, ten flavonoids were selected as potential inhibitors of V protein. Furthermore, a common configuration was obtained among these ten flavonoids. The interaction analysis also identified the atoms involved in every interaction of flavonoid and V protein. Molecular dynamics (MD) simulation confirmed the stability of two compounds, quercetin-7-O-[α-L-rhamnopyranosyl(1→6)-β-D-galactopyranoside] and luteolin 7-O-neohesperidoside, at 100 ns with V protein. The identified compounds through molecular docking and MD simulation could have potential as NDV-V protein inhibitor after further validation. This study could be useful for the designing of anti-NDV drugs. Full article
(This article belongs to the Special Issue Molecular Modeling: Computer-Aided Drug Design)
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9 pages, 1210 KiB  
Article
MRlogP: Transfer Learning Enables Accurate logP Prediction Using Small Experimental Training Datasets
by Yan-Kai Chen, Steven Shave and Manfred Auer
Processes 2021, 9(11), 2029; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9112029 - 13 Nov 2021
Cited by 3 | Viewed by 2595
Abstract
Small molecule lipophilicity is often included in generalized rules for medicinal chemistry. These rules aim to reduce time, effort, costs, and attrition rates in drug discovery, allowing the rejection or prioritization of compounds without the need for synthesis and testing. The availability of [...] Read more.
Small molecule lipophilicity is often included in generalized rules for medicinal chemistry. These rules aim to reduce time, effort, costs, and attrition rates in drug discovery, allowing the rejection or prioritization of compounds without the need for synthesis and testing. The availability of high quality, abundant training data for machine learning methods can be a major limiting factor in building effective property predictors. We utilize transfer learning techniques to get around this problem, first learning on a large amount of low accuracy predicted logP values before finally tuning our model using a small, accurate dataset of 244 druglike compounds to create MRlogP, a neural network-based predictor of logP capable of outperforming state of the art freely available logP prediction methods for druglike small molecules. MRlogP achieves an average root mean squared error of 0.988 and 0.715 against druglike molecules from Reaxys and PHYSPROP. We have made the trained neural network predictor and all associated code for descriptor generation freely available. In addition, MRlogP may be used online via a web interface. Full article
(This article belongs to the Special Issue Molecular Modeling: Computer-Aided Drug Design)
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6 pages, 2568 KiB  
Communication
SimilarityLab: Molecular Similarity for SAR Exploration and Target Prediction on the Web
by Steven Shave and Manfred Auer
Processes 2021, 9(9), 1520; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9091520 - 27 Aug 2021
Cited by 1 | Viewed by 1872
Abstract
Exploration of chemical space around hit, experimental, and known active compounds is an important step in the early stages of drug discovery. In academia, where access to chemical synthesis efforts is restricted in comparison to the pharma-industry, hits from primary screens are typically [...] Read more.
Exploration of chemical space around hit, experimental, and known active compounds is an important step in the early stages of drug discovery. In academia, where access to chemical synthesis efforts is restricted in comparison to the pharma-industry, hits from primary screens are typically followed up through purchase and testing of similar compounds, before further funding is sought to begin medicinal chemistry efforts. Rapid exploration of druglike similars and structure–activity relationship profiles can be achieved through our new webservice SimilarityLab. In addition to searching for commercially available molecules similar to a query compound, SimilarityLab also enables the search of compounds with recorded activities, generating consensus counts of activities, which enables target and off-target prediction. In contrast to other online offerings utilizing the USRCAT similarity measure, SimilarityLab’s set of commercially available small molecules is consistently updated, currently containing over 12.7 million unique small molecules, and not relying on published databases which may be many years out of date. This ensures researchers have access to up-to-date chemistries and synthetic processes enabling greater diversity and access to a wider area of commercial chemical space. All source code is available in the SimilarityLab source repository. Full article
(This article belongs to the Special Issue Molecular Modeling: Computer-Aided Drug Design)
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