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Computational Approaches in Drug Discovery and Design

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

Deadline for manuscript submissions: 31 May 2025 | Viewed by 12752

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Prof. Dr. Shijun Zhong

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Guest Editor

School of Bioengineering, Dalian University of Technology, Dalian, China
Interests: protein-ligand interaction; scoring function; force field parameter; gaussian basis set; QSAR; quantum chemical computation; molecular dynamics simulation; molecular docking

Special Issue Information

Dear Colleagues,

Various computational methods have been applied to medicinal chemistry for drug design and discovery, continuously enhancing the performance of computer-aided drug design (CADD). The calculations of protein–ligand bindings, relying on the three-dimensional structure of the target protein, are associated with structure-based drug design which uses molecular docking, molecular dynamics simulation, and quantum chemistry. The searching and design of similar molecular structures based on well-identified ligand forms another category, called ligand-based drug design which is linked to quantitative structure–activity relationship (QSAR) and pharmacophore modeling. Computations were also applied to further validate drug molecules for absorption, distribution, metabolism, excretion, and toxicity (ADMET), as well as druglikeness and pan-assay interference compounds (PAINS). With the help of available information and well-built databases, machine learning methods can be used in any stage of drug design and development. This Special Issue welcomes the submission of any study on the development of computational methods and algorithms, databases, and informatics, as well as approaches combining or integrating multiple computational treatments used in drug design and discovery. Case studies using any new methods or combined approaches are also welcome.

Prof. Dr. Shijun Zhong
Guest Editor

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Keywords

combined and integrated approaches in CADD
QM/MM and quantum chemical computations for protein–ligand binding
scoring function
binding free energy
target identification
molecular docking molecular dynamics simulation
machine learning or deep learning
data fitting
database
pattern recognition
druglikeness
drug design or drug discovery

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Published Papers (6 papers)

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Research

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14 pages, 2513 KiB

Open AccessArticle

Identification of Novel PPARγ Partial Agonists Based on Virtual Screening Strategy: In Silico and In Vitro Experimental Validation

by Yu-E Lian, Mei Wang, Lei Ma, Wei Yi, Siyan Liao, Hui Gao and Zhi Zhou

Molecules 2024, 29(20), 4881; https://doi.org/10.3390/molecules29204881 - 15 Oct 2024

Viewed by 734

Abstract

Thiazolidinediones (TZDs) including rosiglitazone and pioglitazone function as peroxisome proliferator-activated receptor gamma (PPARγ) full agonists, which have been known as a class to be among the most effective drugs for the treatment of type 2 diabetes mellitus (T2DM). However, side effects of TZDs such as fluid retention and weight gain are associated with their full agonistic activities toward PPARγ induced by the AF-2 helix-involved “locked” mechanism. Thereby, this study aimed to obtain novel PPARγ partial agonists without direct interaction with the AF-2 helix. Through performing virtual screening of the Targetmol L6000 Natural Product Library and utilizing molecular dynamics (MD) simulation, as well as molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) analysis, four compounds including tubuloside b, podophyllotoxone, endomorphin 1 and paliperidone were identified as potential PPARγ partial agonists. An in vitro TR-FRET competitive binding assay showed podophyllotoxone displayed the optimal binding affinity toward PPARγ among the screened compounds, exhibiting IC₅₀ and ki values of 27.43 µM and 9.86 µM, respectively. Further cell-based transcription assays were conducted and demonstrated podophyllotoxone’s weak agonistic activity against PPARγ compared to that of the PPARγ full agonist rosiglitazone. These results collectively demonstrated that podophyllotoxone could serve as a PPARγ partial agonist and might provide a novel candidate for the treatment of various diseases such as T2DM. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)

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15 pages, 3267 KiB

Open AccessArticle

Structure-Based Virtual Screening for Methyltransferase Inhibitors of SARS-CoV-2 nsp14 and nsp16

by Kejue Wu, Yinfeng Guo, Tiefeng Xu, Weifeng Huang, Deyin Guo, Liu Cao and Jinping Lei

Molecules 2024, 29(10), 2312; https://doi.org/10.3390/molecules29102312 - 15 May 2024

Cited by 1 | Viewed by 1562

Abstract

The ongoing COVID-19 pandemic still threatens human health around the world. The methyltransferases (MTases) of SARS-CoV-2, specifically nsp14 and nsp16, play crucial roles in the methylation of the N7 and 2′-O positions of viral RNA, making them promising targets for the development of antiviral drugs. In this work, we performed structure-based virtual screening for nsp14 and nsp16 using the screening workflow (HTVS, SP, XP) of Schrödinger 2019 software, and we carried out biochemical assays and molecular dynamics simulation for the identification of potential MTase inhibitors. For nsp14, we screened 239,000 molecules, leading to the identification of three hits A1–A3 showing N7-MTase inhibition rates greater than 60% under a concentration of 50 µM. For the SAM binding and nsp10-16 interface sites of nsp16, the screening of 210,000 and 237,000 molecules, respectively, from ZINC15 led to the discovery of three hit compounds B1–B3 exhibiting more than 45% of 2′-O-MTase inhibition under 50 µM. These six compounds with moderate MTase inhibitory activities could be used as novel candidates for the further development of anti-SARS-CoV-2 drugs. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)

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16 pages, 3170 KiB

Open AccessArticle

NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer

by Xiaojuan Shen, Tao Zeng, Nianhang Chen, Jiabo Li and Ruibo Wu

Molecules 2024, 29(8), 1867; https://doi.org/10.3390/molecules29081867 - 19 Apr 2024

Viewed by 1498

Abstract

Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO models were trained under different task scenarios by recognizing syntactic patterns and structure–property relationships. We further explored the performance of NIMO in structure-guided, activity-oriented, and pocket-based molecule generation tasks. Our results show that NIMO had excellent performance for molecule generation from scratch and structure optimization from a scaffold. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)

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15 pages, 4370 KiB

Open AccessArticle

Water Exchange from the Buried Binding Sites of Cytochrome P450 Enzymes 1A2, 2D6, and 3A4 Correlates with Conformational Fluctuations

by Olgun Guvench

Molecules 2024, 29(2), 494; https://doi.org/10.3390/molecules29020494 - 19 Jan 2024

Cited by 1 | Viewed by 1229

Abstract

Human cytochrome P450 enzymes (CYPs) are critical for the metabolism of small-molecule pharmaceuticals (drugs). As such, the prediction of drug metabolism by and drug inhibition of CYP activity is an important component of the drug discovery and design process. Relative to the availability of a wide range of experimental atomic-resolution CYP structures, the development of structure-based CYP activity models has been limited. To better characterize the role of CYP conformational fluctuations in CYP activity, we perform multiple microsecond-scale all-atom explicit-solvent molecular dynamics (MD) simulations on three CYP isoforms, 1A2, 2D6, and 3A4, which together account for the majority of CYP-mediated drug metabolism. The MD simulations employ a variety of positional restraints, ranging from keeping all CYP atoms close to their experimentally determined coordinates to allowing full flexibility. We find that, with full flexibility, large fluctuations in the CYP binding sites correlate with efficient water exchange from these buried binding sites. This is especially true for 1A2, which, when restrained to its crystallographic conformation, is unable to exchange water between the binding site and bulk solvent. These findings imply that, in addition to crystal structures, a representative ensemble of conformational states ought to be included when developing structure-based CYP activity models. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)

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23 pages, 10614 KiB

Open AccessArticle

In Silico and In Vitro Identification of 1,8-Dihydroxy-4,5-dinitroanthraquinone as a New Antibacterial Agent against Staphylococcus aureus and Enterococcus faecalis

by Juliana Amorim, Viviana Vásquez, Andrea Cabrera, Maritza Martínez and Juan Carpio

Molecules 2024, 29(1), 203; https://doi.org/10.3390/molecules29010203 - 29 Dec 2023

Cited by 1 | Viewed by 1483

Abstract

Increasing rates of bacterial resistance to antibiotics are a growing concern worldwide. The search for potential new antibiotics has included several natural products such as anthraquinones. However, comparatively less attention has been given to anthraquinones that exhibit functional groups that are uncommon in nature. In this work, 114 anthraquinones were evaluated using in silico methods to identify inhibitors of the enzyme phosphopantetheine adenylyltransferase (PPAT) of Staphylococcus aureus, Enterococcus faecalis, and Escherichia coli. Virtual screenings based on molecular docking and the pharmacophore model, molecular dynamics simulations, and free energy calculations pointed to 1,8-dihydroxy-4,5-dinitroanthraquinone (DHDNA) as the most promising inhibitor. In addition, these analyses highlighted the contribution of the nitro group to the affinity of this anthraquinone for the nucleotide-binding site of PPAT. Furthermore, DHDNA was active in vitro towards Gram-positive bacteria with minimum inhibitory concentration (MIC) values of 31.25 µg/mL for S. aureus and 62.5 µg/mL for E. faecalis against both antibiotic-resistant isolates and reference strains but was ineffective against E. coli. Experiments on kill-time kinetics indicated that, at the tested concentrations, DHDNA produced bacteriostatic effects on both Gram-positive bacteria. Overall, our results present DHDNA as a potential PPAT inhibitor, showing antibacterial activity against antibiotic-resistant isolates of S. aureus and E. faecalis, findings that point to nitro groups as key to explaining these results. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)

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Review

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21 pages, 2119 KiB

Open AccessReview

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges

by Xin Qi, Yuanchun Zhao, Zhuang Qi, Siyu Hou and Jiajia Chen

Molecules 2024, 29(4), 903; https://doi.org/10.3390/molecules29040903 - 18 Feb 2024

Cited by 8 | Viewed by 5156

Abstract

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field. Full article

(This article belongs to the Special Issue Computational Approaches in Drug Discovery and Design)

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