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Molecules
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Molecular Docking in Drug Discovery: Methods and Applications
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Molecular Docking in Drug Discovery: Methods and Applications

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 34222

Special Issue Editor

Prof. Dr. Chung F. Wong

E-Mail Website
Guest Editor
Department of Chemistry and Biochemistry，University of Missouri-St. Louis, St Louis, MO, USA
Interests: computer-aided drug discovery; simulation of biological systems; protein kinases; protein phosphatases; drug-binding kinetics; computational genomics; molecular dynamics; Brownian dynamics; molecular docking; molecular sensitivity analysis

Special Issue Information

Dear Colleagues,

Molecular docking has been a useful tool to aid drug discovery. It helps to find new hits from compound libraries, to optimize drug leads, to suggest docking poses to rationalize experimental data or to give insights into new synthesis. Molecular docking can do these better now with new methodologies and new computer technologies. Machine learning has been used to refine scoring functions, to post-process docking results to improve predictions, and to speedup virtual screening. Some models now account for receptor flexibility. Web servers are available to help users perform docking. GPU-computing is leveraged to screen large compound libraries. Nevertheless, many gaps remain. This special issue invites contributions that further improve or evaluate molecular docking for drug discovery. Insightful applications are also welcomed.

Prof. Dr. Chung F. Wong
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 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. 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 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

  • Molecular docking
  • drug discovery
  • machine learning
  • receptor flexibility
  • benchmark docking performance
  • applicability domain
  • GPU in docking

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Published Papers (4 papers)

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Research

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15 pages, 491 KiB  
Article
Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-Generated Protein–Ligand Structures: Towards Per-Target Scoring Functions
by Francesco Pellicani, Diego Dal Ben, Andrea Perali and Sebastiano Pilati
Molecules 2023, 28(4), 1661; https://doi.org/10.3390/molecules28041661 - 9 Feb 2023
Cited by 7 | Viewed by 6581
Abstract
In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present [...] Read more.
In recent years, machine learning has been proposed as a promising strategy to build accurate scoring functions for computational docking finalized to numerically empowered drug discovery. However, the latest studies have suggested that over-optimistic results had been reported due to the correlations present in the experimental databases used for training and testing. Here, we investigate the performance of an artificial neural network in binding affinity predictions, comparing results obtained using both experimental protein–ligand structures as well as larger sets of computer-generated structures created using commercial software. Interestingly, similar performances are obtained on both databases. We find a noticeable performance suppression when moving from random horizontal tests to vertical tests performed on target proteins not included in the training data. The possibility to train the network on relatively easily created computer-generated databases leads us to explore per-target scoring functions, trained and tested ad-hoc on complexes including only one target protein. Encouraging results are obtained, depending on the type of protein being addressed. Full article
(This article belongs to the Special Issue Molecular Docking in Drug Discovery: Methods and Applications)
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25 pages, 13579 KiB  
Article
Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations
by Lars Elend, Luise Jacobsen, Tim Cofala, Jonas Prellberg, Thomas Teusch, Oliver Kramer and Ilia A. Solov’yov
Molecules 2022, 27(13), 4020; https://doi.org/10.3390/molecules27134020 - 22 Jun 2022
Cited by 22 | Viewed by 3771
Abstract
Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an [...] Read more.
Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates. Full article
(This article belongs to the Special Issue Molecular Docking in Drug Discovery: Methods and Applications)
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Review

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14 pages, 798 KiB  
Review
Open-Source Browser-Based Tools for Structure-Based Computer-Aided Drug Discovery
by Ann Wang and Jacob D. Durrant
Molecules 2022, 27(14), 4623; https://doi.org/10.3390/molecules27144623 - 20 Jul 2022
Cited by 9 | Viewed by 4388
Abstract
We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability [...] Read more.
We here outline the importance of open-source, accessible tools for computer-aided drug discovery (CADD). We begin with a discussion of drug discovery in general to provide context for a subsequent discussion of structure-based CADD applied to small-molecule ligand discovery. Next, we identify usability challenges common to many open-source CADD tools. To address these challenges, we propose a browser-based approach to CADD tool deployment in which CADD calculations run in modern web browsers on users’ local computers. The browser app approach eliminates the need for user-initiated download and installation, ensures broad operating system compatibility, enables easy updates, and provides a user-friendly graphical user interface. Unlike server apps—which run calculations “in the cloud” rather than on users’ local computers—browser apps do not require users to upload proprietary information to a third-party (remote) server. They also eliminate the need for the difficult-to-maintain computer infrastructure required to run user-initiated calculations remotely. We conclude by describing some CADD browser apps developed in our lab, which illustrate the utility of this approach. Aside from introducing readers to these specific tools, we are hopeful that this review highlights the need for additional browser-compatible, user-friendly CADD software. Full article
(This article belongs to the Special Issue Molecular Docking in Drug Discovery: Methods and Applications)
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24 pages, 2046 KiB  
Review
Protein–Ligand Docking in the Machine-Learning Era
by Chao Yang, Eric Anthony Chen and Yingkai Zhang
Molecules 2022, 27(14), 4568; https://doi.org/10.3390/molecules27144568 - 18 Jul 2022
Cited by 82 | Viewed by 16898
Abstract
Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein–ligand scoring function. In this review, we give a broad overview of recent scoring [...] Read more.
Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein–ligand scoring function. In this review, we give a broad overview of recent scoring function development, as well as the docking-based applications in drug discovery. We outline the strategies and resources available for structure-based VS and discuss the assessment and development of classical and machine learning protein–ligand scoring functions. In particular, we highlight the recent progress of machine learning scoring function ranging from descriptor-based models to deep learning approaches. We also discuss the general workflow and docking protocols of structure-based VS, such as structure preparation, binding site detection, docking strategies, and post-docking filter/re-scoring, as well as a case study on the large-scale docking-based VS test on the LIT-PCBA data set. Full article
(This article belongs to the Special Issue Molecular Docking in Drug Discovery: Methods and Applications)
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