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Role of Computer Aided Drug Design in Drug Development

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 31531

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Dr. Ruoxu Gu

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Guest Editor

College of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
Interests: computer aided drug design; molecular dynamics simulations of biological molecules; ion channel; membrane biophysics

Prof. Dr. Zunnan Huang

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Guest Editor

School of Pharmacy, Guangdong Medical University, Zhanjiang, China
Interests: virtual screening; drug design; target fishing; data mining; network pharmacology; meta-analysis; bioinformatics; molecular biology

Dr. Fengxu Wu

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Guest Editor

School of Pharmaceutical Sciences, Hubei University of Medicine, Shiyan, China
Interests: structure-based drug design; ligand-based drug design; virtual screening; fragment-based drug design; machine learning

Special Issue Information

Dear Colleagues,

Due to the progresses of computational techniques and powers, computer-aided approaches have become efficient complements to experiments and accelerated drug development significantly. These approaches, including both structural based molecular modeling techniques and data driven methods, are involved in almost every stage of the drug design. For instance, molecular modeling such as in silico docking, molecular dynamics simulations, and de novo drug design utilize structural information to design or optimize leading compounds. Data mining and bioinformatics methods can be used for disease-targeted drug prediction based on dysregulated genes. Network pharmacology is able to investigate leading compound (drug) treatments on the signal pathways and identify novel drug targets. Quantitative structure property relationship methods are developed to predict the absorption, distribution, metabolism, elimination, and toxicity (ADMET) of leading compounds. In recently years, statistical- and artificial intelligence-based data driven methods use massive biological data to predict drug targets, ligand-receptor interactions and pharmaceutical properties of leading compounds. In this regard, we organize this Special Issue, “Role of Computer-Aided Drug Design in Drug Development”, to publish current state-of-the-art computer-aided drug development papers ranging from methods development to their applications. The contributed papers may be focused on the following topics:

Design and optimization of ligands based on molecular modeling techniques
High throughput prediction of drug targets, novel leading compounds, drug repurposing, and ADMET properties based on machine learning and artificial intelligence
Prediction and validation of novel drugs and their targets by bioinformatics approaches and network pharmacology methods
Mechanism of ligand-receptor interactions
Advances in the design of nanoscale drug carriers

Dr. Ruoxu Gu
Prof. Dr. Zunnan Huang
Dr. Fengxu Wu
Guest Editors

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Keywords

computer-aided drug design
molecular modeling and simulations
data driven predictions
machine learning and artificial intelligence
ligand-protein interactions
drug carriers

Published Papers (10 papers)

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Editorial

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3 pages, 176 KiB

Open AccessEditorial

Role of Computer-Aided Drug Design in Drug Development

by Ruoxu Gu, Fengxu Wu and Zunnan Huang

Molecules 2023, 28(20), 7160; https://doi.org/10.3390/molecules28207160 - 19 Oct 2023

Cited by 3 | Viewed by 1571

Abstract

The introduction of computational techniques to pharmaceutical chemistry and molecular biology in the 20th century has changed the way people develop drugs [...] Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

Research

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21 pages, 8858 KiB

Open AccessArticle

LSD1-Based Reversible Inhibitors Virtual Screening and Binding Mechanism Computational Study

by Zhili Yin, Shaohui Liu, Xiaoyue Yang, Mengguo Chen, Jiangfeng Du, Hongmin Liu and Longhua Yang

Molecules 2023, 28(14), 5315; https://doi.org/10.3390/molecules28145315 - 10 Jul 2023

Cited by 1 | Viewed by 1617

Abstract

As one of the crucial targets of epigenetics, histone lysine-specific demethylase 1 (LSD1) is significant in the occurrence and development of various tumors. Although several irreversible covalent LSD1 inhibitors have entered clinical trials, the large size and polarity of the FAD-binding pocket and undesired toxicity have focused interest on developing reversible LSD1 inhibitors. In this study, targeting the substrate-binding pocket of LSD1, structure-based and ligand-based virtual screenings were adopted to expand the potential novel structures with molecular docking and pharmacophore model strategies, respectively. Through drug-likeness evaluation, ADMET screening, molecular dynamics simulations, and binding free energy screening, we screened out one and four hit compounds from the databases of 2,029,554 compounds, respectively. Generally, these hit compounds can be divided into two categories, amide (Lig2 and Comp2) and 1,2,4-triazolo-4,3-α-quinazoline (Comp3, Comp4, Comp7). Among them, Comp4 exhibits the strongest binding affinity. Finally, the binding mechanisms of the hit compounds were further calculated in detail by the residue free energy decomposition. It was found that van der Waals interactions contribute most to the binding, and FAD is also helpful in stabilizing the binding and avoiding off-target effects. We believe this work not only provides a solid theoretical foundation for the design of LSD1 substrate reversible inhibitors, but also expands the diversity of parent nucleus, offering new insights for synthetic chemists. Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

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21 pages, 7536 KiB

Open AccessArticle

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein–Ligand Interaction Prediction

by Haiping Zhang, Konda Mani Saravanan and John Z. H. Zhang

Molecules 2023, 28(12), 4691; https://doi.org/10.3390/molecules28124691 - 10 Jun 2023

Cited by 6 | Viewed by 2293

Abstract

The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical–chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein–ligand interaction and can be used in many important large-scale virtual screening application scenarios. Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

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17 pages, 4006 KiB

Open AccessArticle

Binding and Dynamics Demonstrate the Destabilization of Ligand Binding for the S688Y Mutation in the NMDA Receptor GluN1 Subunit

by Jake Zheng Chen, William Bret Church, Karine Bastard, Anthony P. Duff and Thomas Balle

Molecules 2023, 28(10), 4108; https://doi.org/10.3390/molecules28104108 - 15 May 2023

Cited by 1 | Viewed by 1448

Abstract

Encephalopathies are brain dysfunctions that lead to cognitive, sensory, and motor development impairments. Recently, the identification of several mutations within the N-methyl-D-aspartate receptor (NMDAR) have been identified as significant in the etiology of this group of conditions. However, a complete understanding of the underlying molecular mechanism and changes to the receptor due to these mutations has been elusive. We studied the molecular mechanisms by which one of the first mutations within the NMDAR GluN1 ligand binding domain, Ser688Tyr, causes encephalopathies. We performed molecular docking, randomly seeded molecular dynamics simulations, and binding free energy calculations to determine the behavior of the two major co-agonists: glycine and D-serine, in both the wild-type and S688Y receptors. We observed that the Ser688Tyr mutation leads to the instability of both ligands within the ligand binding site due to structural changes associated with the mutation. The binding free energy for both ligands was significantly more unfavorable in the mutated receptor. These results explain previously observed in vitro electrophysiological data and provide detailed aspects of ligand association and its effects on receptor activity. Our study provides valuable insight into the consequences of mutations within the NMDAR GluN1 ligand binding domain. Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

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18 pages, 4642 KiB

Open AccessArticle

In Vitro Evaluation of In Silico Screening Approaches in Search for Selective ACE2 Binding Chemical Probes

by Alexey V. Rayevsky, Andrii S. Poturai, Iryna O. Kravets, Alexander E. Pashenko, Tatiana A. Borisova, Ganna M. Tolstanova, Dmitriy M. Volochnyuk, Petro O. Borysko, Olga B. Vadzyuk, Diana O. Alieksieieva, Yuliana Zabolotna, Olga Klimchuk, Dragos Horvath, Gilles Marcou, Sergey V. Ryabukhin and Alexandre Varnek

Molecules 2022, 27(17), 5400; https://doi.org/10.3390/molecules27175400 - 24 Aug 2022

Cited by 1 | Viewed by 1791

Abstract

New models for ACE2 receptor binding, based on QSAR and docking algorithms were developed, using XRD structural data and ChEMBL 26 database hits as training sets. The selectivity of the potential ACE2-binding ligands towards Neprilysin (NEP) and ACE was evaluated. The Enamine screening collection (3.2 million compounds) was virtually screened according to the above models, in order to find possible ACE2-chemical probes, useful for the study of SARS-CoV2-induced neurological disorders. An enzymology inhibition assay for ACE2 was optimized, and the combined diversified set of predicted selective ACE2-binding molecules from QSAR modeling, docking, and ultrafast docking was screened in vitro. The in vitro hits included two novel chemotypes suitable for further optimization. Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

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20 pages, 12688 KiB

Open AccessArticle

In Silico Repurposed Drugs against Monkeypox Virus

by Hilbert Yuen In Lam, Jia Sheng Guan and Yuguang Mu

Molecules 2022, 27(16), 5277; https://doi.org/10.3390/molecules27165277 - 18 Aug 2022

Cited by 45 | Viewed by 4657

Abstract

Monkeypox is an emerging epidemic of concern. The disease is caused by the monkeypox virus and an increasing global incidence with a 2022 outbreak that has spread to Europe amid the COVID-19 pandemic. The new outbreak is associated with novel, previously undiscovered mutations and variants. Currently, the US Food and Drug Administration (FDA) approved poxvirus treatment involves the use of tecovirimat. However, there is otherwise limited pharmacopoeia and research interest in monkeypox. In this study, virtual screening and molecular dynamics were employed to explore the potential repurposing of multiple drugs previously approved by the FDA or other jurisdictions for other applications. Several drugs are predicted to tightly bind to viral proteins, which are crucial in viral replication, including molecules which show high potential for binding the monkeypox D13L capsid protein, whose inhibition has previously been demonstrated to suppress viral replication. Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

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19 pages, 3360 KiB

Open AccessArticle

Identification of Potential Parkinson’s Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network

by Jie Liu, Dongdong Peng, Jinlong Li, Zong Dai, Xiaoyong Zou and Zhanchao Li

Molecules 2022, 27(15), 4780; https://doi.org/10.3390/molecules27154780 - 26 Jul 2022

Cited by 4 | Viewed by 1817

Abstract

Parkinson’s disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson’s disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision–recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies. Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

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22 pages, 5989 KiB

Open AccessArticle

Identification of MiRNA–Disease Associations Based on Information of Multi-Module and Meta-Path

by Zihao Li, Xing Huang, Yakun Shi, Xiaoyong Zou, Zhanchao Li and Zong Dai

Molecules 2022, 27(14), 4443; https://doi.org/10.3390/molecules27144443 - 11 Jul 2022

Cited by 1 | Viewed by 1527

Abstract

Cumulative research reveals that microRNAs (miRNAs) are involved in many critical biological processes including cell proliferation, differentiation and apoptosis. It is of great significance to figure out the associations between miRNAs and human diseases that are the basis for finding biomarkers for diagnosis and targets for treatment. To overcome the time-consuming and labor-intensive problems faced by traditional experiments, a computational method was developed to identify potential associations between miRNAs and diseases based on the graph attention network (GAT) with different meta-path mode and support vector (SVM). Firstly, we constructed a multi-module heterogeneous network based on the meta-path and learned the latent features of different modules by GAT. Secondly, we found the average of the latent features with weight to obtain a final node representation. Finally, we characterized miRNA–disease-association pairs with the node representation and trained an SVM to recognize potential associations. Based on the five-fold cross-validation and benchmark datasets, the proposed method achieved an area under the precision–recall curve (AUPR) of 0.9379 and an area under the receiver–operating characteristic curve (AUC) of 0.9472. The results demonstrate that our method has an outstanding practical application performance and can provide a reference for the discovery of new biomarkers and therapeutic targets. Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

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Review

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36 pages, 2032 KiB

Open AccessReview

In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets

by Jianbo Liao, Qinyu Wang, Fengxu Wu and Zunnan Huang

Molecules 2022, 27(20), 7103; https://doi.org/10.3390/molecules27207103 - 20 Oct 2022

Cited by 17 | Viewed by 7679

Abstract

Target identification is an important step in drug discovery, and computer-aided drug target identification methods are attracting more attention compared with traditional drug target identification methods, which are time-consuming and costly. Computer-aided drug target identification methods can greatly reduce the searching scope of experimental targets and associated costs by identifying the diseases-related targets and their binding sites and evaluating the druggability of the predicted active sites for clinical trials. In this review, we introduce the principles of computer-based active site identification methods, including the identification of binding sites and assessment of druggability. We provide some guidelines for selecting methods for the identification of binding sites and assessment of druggability. In addition, we list the databases and tools commonly used with these methods, present examples of individual and combined applications, and compare the methods and tools. Finally, we discuss the challenges and limitations of binding site identification and druggability assessment at the current stage and provide some recommendations and future perspectives. Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

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21 pages, 1446 KiB

Open AccessReview

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

by Md Rifat Hasan, Ahad Amer Alsaiari, Burhan Zain Fakhurji, Mohammad Habibur Rahman Molla, Amer H. Asseri, Md Afsar Ahmed Sumon, Moon Nyeo Park, Foysal Ahammad and Bonglee Kim

Molecules 2022, 27(13), 4169; https://doi.org/10.3390/molecules27134169 - 29 Jun 2022

Cited by 21 | Viewed by 5791

Abstract

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible. Full article

(This article belongs to the Special Issue Role of Computer Aided Drug Design in Drug Development)

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