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Biomolecules
Special Issues
Computer Aided Drug Discovery
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Computer Aided Drug Discovery

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (30 January 2023) | Viewed by 11103

Special Issue Editors

Prof. Dr. Umesh Desai

E-Mail Website
Guest Editor
1. Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
2. Drug Discovery and Development, Institute for Structural Biology, Virginia Commonwealth University, Richmond, VA 23219, USA
Interests: drug discovery; chemical biology; biological macromolecules; glycosaminoglycans; coagulation factors; cancer; viral infection; bio-mimetic design; enzyme mechanisms; computational biology; high throughput screening
Special Issues, Collections and Topics in MDPI journals
Dr. Daniel Afosah

E-Mail Website
Guest Editor
1. Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
2. Drug Discovery and Development, Institute for Structural Biology, Virginia Commonwealth University, Richmond, VA 23219, USA
Interests: application of GAGs and GAG mimetics in thrombopoiesis, thrombosis, and inflammation; studies of GAG-protein interactions; photoaffinity labeling technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the Special Issue in Biomolecules titled "Computer-Aided Drug Discovery".

As you know, newer approaches, including mass-spectrometry-based omics (genomics, proteomics, glycomics, lipidomics), high-throughput screening, flow chemistry and cryo-electron microscopy, are expanding the repertoire and reach of scientists to speed up the process of computer-aided drug discovery (CADD). This Special Issue is an effort to bring together the diversity of experts in this field to present major advances in the use of computational tools (molecular modeling (MM), molecular dynamics (MD), machine learning (artificial intelligence (AI)), and virtual high-throughput screening (vHTS)) to perform structure- or pharmacophore-based drug discovery.

This collection will highlight advances in MM, MD, AI, vHTS, and others to aid CADD. The issue will present the use of CADD in identifying hits and leads, as well as scaffold hopping, to gain an upper hand in terms of intellectual property. The use of CADD for lead optimization, which is usually associated with drug development, will also be of interest. Likewise, CADD’s application to identification putative adverse consequences over and above those weeded out through the use of algorithms that inform on potential instability, aggregation, and reactivity of compounds will be of interest. Overall, this Special Issue on CADD will be interested in disseminating any and all aspects of computational approaches and strategies that help discover new drug or drug-like molecules.

As an expert working in this area, we welcome your contribution to this Special Issue. You may contribute an original research article and a review. We look forward to receiving your contributions.

Prof. Dr. Umesh Desai
Dr. Daniel Afosah
Guest Editors

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 submissions that pass pre-check are 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. Biomolecules is an international peer-reviewed open access monthly 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 2700 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

  • machine learning
  • molecular modeling
  • molecular dynamics
  • high-throughput screening
  • structure–activity relationships
  • algorithms

Published Papers (5 papers)

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Research

20 pages, 4921 KiB  
Article
Assessing Genetic Algorithm-Based Docking Protocols for Prediction of Heparin Oligosaccharide Binding Geometries onto Proteins
by Samuel G. Holmes and Umesh R. Desai
Biomolecules 2023, 13(11), 1633; https://doi.org/10.3390/biom13111633 - 09 Nov 2023
Viewed by 2527
Abstract
Although molecular docking has evolved dramatically over the years, its application to glycosaminoglycans (GAGs) has remained challenging because of their intrinsic flexibility, highly anionic character and rather ill-defined site of binding on proteins. GAGs have been treated as either fully “rigid” or fully [...] Read more.
Although molecular docking has evolved dramatically over the years, its application to glycosaminoglycans (GAGs) has remained challenging because of their intrinsic flexibility, highly anionic character and rather ill-defined site of binding on proteins. GAGs have been treated as either fully “rigid” or fully “flexible” in molecular docking. We reasoned that an intermediate semi-rigid docking (SRD) protocol may be better for the recapitulation of native heparin/heparan sulfate (Hp/HS) topologies. Herein, we study 18 Hp/HS–protein co-complexes containing chains from disaccharide to decasaccharide using genetic algorithm-based docking with rigid, semi-rigid, and flexible docking protocols. Our work reveals that rigid and semi-rigid protocols recapitulate native poses for longer chains (5→10 mers) significantly better than the flexible protocol, while 2→4-mer poses are better predicted using the semi-rigid approach. More importantly, the semi-rigid docking protocol is likely to perform better when no crystal structure information is available. We also present a new parameter for parsing selective versus non-selective GAG–protein systems, which relies on two computational parameters including consistency of binding (i.e., RMSD) and docking score (i.e., GOLD Score). The new semi-rigid protocol in combination with the new computational parameter is expected to be particularly useful in high-throughput screening of GAG sequences for identifying promising druggable targets as well as drug-like Hp/HS sequences. Full article
(This article belongs to the Special Issue Computer Aided Drug Discovery)
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11 pages, 1501 KiB  
Article
AI-Aided Search for New HIV-1 Protease Ligands
by Roberto Arrigoni, Luigi Santacroce, Andrea Ballini and Luigi Leonardo Palese
Biomolecules 2023, 13(5), 858; https://doi.org/10.3390/biom13050858 - 18 May 2023
Cited by 2 | Viewed by 1485
Abstract
The availability of drugs capable of blocking the replication of microorganisms has been one of the greatest triumphs in the history of medicine, but the emergence of an ever-increasing number of resistant strains poses a serious problem for the treatment of infectious diseases. [...] Read more.
The availability of drugs capable of blocking the replication of microorganisms has been one of the greatest triumphs in the history of medicine, but the emergence of an ever-increasing number of resistant strains poses a serious problem for the treatment of infectious diseases. The search for new potential ligands for proteins involved in the life cycle of pathogens is, therefore, an extremely important research field today. In this work, we have considered the HIV-1 protease, one of the main targets for AIDS therapy. Several drugs are used today in clinical practice whose mechanism of action is based on the inhibition of this enzyme, but after years of use, even these molecules are beginning to be interested by resistance phenomena. We used a simple artificial intelligence system for the initial screening of a data set of potential ligands. These results were validated by docking and molecular dynamics, leading to the identification of a potential new ligand of the enzyme which does not belong to any known class of HIV-1 protease inhibitors. The computational protocol used in this work is simple and does not require large computational power. Furthermore, the availability of a large number of structural information on viral proteins and the presence of numerous experimental data on their ligands, with which it is possible to compare the results obtained with computational methods, make this research field the ideal terrain for the application of these new computational techniques. Full article
(This article belongs to the Special Issue Computer Aided Drug Discovery)
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11 pages, 1557 KiB  
Communication
AmberMDrun: A Scripting Tool for Running Amber MD in an Easy Way
by Zhi-Wei Zhang and Wen-Cai Lu
Biomolecules 2023, 13(4), 635; https://doi.org/10.3390/biom13040635 - 31 Mar 2023
Cited by 1 | Viewed by 3092
Abstract
MD simulations have been widely applied and become a powerful tool in the field of biomacromolecule simulations and computer-aided drug design, etc., which can estimate binding free energy between receptor and ligand. However, the inputs and force field preparation for performing Amber MD [...] Read more.
MD simulations have been widely applied and become a powerful tool in the field of biomacromolecule simulations and computer-aided drug design, etc., which can estimate binding free energy between receptor and ligand. However, the inputs and force field preparation for performing Amber MD is somewhat complicated, and challenging for beginners. To address this issue, we have developed a script for automatically preparing Amber MD input files, balancing the system, performing Amber MD for production, and predicting receptor-ligand binding free energy. This script is open-source, extensible and can support customization. The core code is written in C++ and has a Python interface, providing both efficient performance and convenience. Full article
(This article belongs to the Special Issue Computer Aided Drug Discovery)
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13 pages, 3868 KiB  
Article
Predicting Potent Compounds Using a Conditional Variational Autoencoder Based upon a New Structure–Potency Fingerprint
by Tiago Janela, Kosuke Takeuchi and Jürgen Bajorath
Biomolecules 2023, 13(2), 393; https://doi.org/10.3390/biom13020393 - 18 Feb 2023
Cited by 1 | Viewed by 1726
Abstract
Prediction of the potency of bioactive compounds generally relies on linear or nonlinear quantitative structure–activity relationship (QSAR) models. Nonlinear models are generated using machine learning methods. We introduce a novel approach for potency prediction that depends on a newly designed molecular fingerprint (FP) [...] Read more.
Prediction of the potency of bioactive compounds generally relies on linear or nonlinear quantitative structure–activity relationship (QSAR) models. Nonlinear models are generated using machine learning methods. We introduce a novel approach for potency prediction that depends on a newly designed molecular fingerprint (FP) representation. This structure–potency fingerprint (SPFP) combines different modules accounting for the structural features of active compounds and their potency values in a single bit string, hence unifying structure and potency representation. This encoding enables the derivation of a conditional variational autoencoder (CVAE) using SPFPs of training compounds and apply the model to predict the SPFP potency module of test compounds using only their structure module as input. The SPFP–CVAE approach correctly predicts the potency values of compounds belonging to different activity classes with an accuracy comparable to support vector regression (SVR), representing the state-of-the-art in the field. In addition, highly potent compounds are predicted with very similar accuracy as SVR and deep neural networks. Full article
(This article belongs to the Special Issue Computer Aided Drug Discovery)
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19 pages, 4021 KiB  
Article
Drug Repositioning Based on the Enhanced Message Passing and Hypergraph Convolutional Networks
by Weihong Huang, Zhong Li, Yanlei Kang, Xinghuo Ye and Wenming Feng
Biomolecules 2022, 12(11), 1666; https://doi.org/10.3390/biom12111666 - 10 Nov 2022
Cited by 4 | Viewed by 1646
Abstract
Drug repositioning, an important method of drug development, is utilized to discover investigational drugs beyond the originally approved indications, expand the application scope of drugs, and reduce the cost of drug development. With the emergence of increasingly drug-disease-related biological networks, the challenge still [...] Read more.
Drug repositioning, an important method of drug development, is utilized to discover investigational drugs beyond the originally approved indications, expand the application scope of drugs, and reduce the cost of drug development. With the emergence of increasingly drug-disease-related biological networks, the challenge still remains to effectively fuse biological entity data and accurately achieve drug-disease repositioning. This paper proposes a new drug repositioning method named EMPHCN based on enhanced message passing and hypergraph convolutional networks (HGCN). It firstly constructs the homogeneous multi-view information with multiple drug similarity features and then extracts the intra-domain embedding of drugs through the combination of HGCN and channel attention mechanism. Secondly, inter-domain information of known drug-disease associations is extracted by graph convolutional networks combining node and edge embedding (NEEGCN), and a heterogeneous network composed of drugs, proteins and diseases is built as an important auxiliary to enhance the inter-domain message passing of drugs and diseases. Besides, the intra-domain embedding of diseases is also extracted through HGCN. Ultimately, intra-domain and inter-domain embeddings of drugs and diseases are integrated as the final embedding for calculating the drug-disease correlation matrix. Through 10-fold cross-validation on some benchmark datasets, we find that the AUPR of EMPHCN reaches 0.593 (T1) and 0.526 (T2), respectively, and the AUC achieves 0.887 (T1) and 0.961 (T2) respectively, which shows that EMPHCN has an advantage over other state-of-the-art prediction methods. Concerning the new disease association prediction, the AUC of EMPHCN through the five-fold cross-validation reaches 0.806 (T1) and 0.845 (T2), which are 4.3% (T1) and 4.0% (T2) higher than the second best existing methods, respectively. In the case study, EMPHCN also achieves satisfactory results in real drug repositioning for breast carcinoma and Parkinson’s disease. Full article
(This article belongs to the Special Issue Computer Aided Drug Discovery)
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