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Computer-Aided Drug Design Strategies

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 4904

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Special Issue Editor

Dr. Giulio Poli

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Guest Editor

Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy
Interests: drug design; molecular modeling; virtual screening; hit identification; lead optimization; molecular docking; molecular dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer-aided drug design is nowadays a widespread research field that is continuously taking ground, raising more and more interest in the scientific community. This is undoubtedly due to the increasing use of machine learning (ML) and other artificial intelligence (AI) approaches in drug design and discovery. AI models demonstrated their reliability in a plethora of applications, including the prediction of physical-chemical properties and biological activities of small molecules, in silico evaluation of ADME and toxicological profiles of chemicals, as well as protein structures and ligand–protein complex prediction. Nevertheless, thanks to the constant improvement of computation power and efficiency, classic ligand-based and receptor-based in silico strategies still provide invaluable help in drug discovery, design and optimization. Long molecular dynamics (MD) simulations, allowing us to evaluate complex protein motions are now becoming more affordable, and MD studies for evaluating stability and potential binding affinity of predicted ligand–protein complexes are now commonly performed in hit finding, hit-to-lead and lead optimization campaigns. Similarly, other techniques such as molecular docking, pharmacophore modeling and ligand-similarity searches, allow us to screen wider and wider chemical libraries in affordable times, thus still representing robust and reliable weapons within the arsenal of computational medicinal chemists.

In this context, this Special Issue is focused on the development of all kinds of in silico approaches and molecular modeling techniques, as well as on their application to all possible stages of the drug design and development process. Scientists are invited to submit original research and review articles in which computational strategies play a key role. This includes the development and/or application of any ligand-based and receptor-based technique, either alone or combined together, for virtual screening protocols aimed at hit identification; the development and/or application of machine learning models for the prediction of physical-chemical, pharmacodynamics, pharmacokinetic and toxicological profiles of small molecules; docking and MD simulation studies for evaluating ligand–protein interactions and/or protein dynamics, and to assist the structural optimization of modulators of pharmaceutically relevant targets; homology modeling, QSAR, cheminformatics and any other strategy involving in silico methods applied to drug design.

Dr. Giulio Poli
Guest Editor

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Keywords

artificial intelligence
machine learning
cheminformatics
docking
molecular dynamics
homology modeling
pharmacophore modeling
QSAR
ligand-based similarity

Published Papers (6 papers)

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Research

16 pages, 1919 KiB

Open AccessArticle

Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria

by Natalia Morales, Elizabeth Valdés-Muñoz, Jaime González, Paulina Valenzuela-Hormazábal, Jonathan M. Palma, Christian Galarza, Ángel Catagua-González, Osvaldo Yáñez, Alfredo Pereira and Daniel Bustos

Int. J. Mol. Sci. 2024, 25(8), 4303; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25084303 - 13 Apr 2024

Viewed by 583

Abstract

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the “chemical family type” attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems. Full article

(This article belongs to the Special Issue Computer-Aided Drug Design Strategies)

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19 pages, 20804 KiB

Open AccessArticle

Triple Generative Self-Supervised Learning Method for Molecular Property Prediction

by Lei Xu, Leiming Xia, Shourun Pan and Zhen Li

Int. J. Mol. Sci. 2024, 25(7), 3794; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25073794 - 28 Mar 2024

Viewed by 496

Abstract

Molecular property prediction is an important task in drug discovery, and with help of self-supervised learning methods, the performance of molecular property prediction could be improved by utilizing large-scale unlabeled dataset. In this paper, we propose a triple generative self-supervised learning method for molecular property prediction, called TGSS. Three encoders including a bi-directional long short-term memory recurrent neural network (BiLSTM), a Transformer, and a graph attention network (GAT) are used in pre-training the model using molecular sequence and graph structure data to extract molecular features. The variational auto encoder (VAE) is used for reconstructing features from the three models. In the downstream task, in order to balance the information between different molecular features, a feature fusion module is added to assign different weights to each feature. In addition, to improve the interpretability of the model, atomic similarity heat maps were introduced to demonstrate the effectiveness and rationality of molecular feature extraction. We demonstrate the accuracy of the proposed method on chemical and biological benchmark datasets by comparative experiments. Full article

(This article belongs to the Special Issue Computer-Aided Drug Design Strategies)

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21 pages, 20950 KiB

Open AccessArticle

Integrated Computational Approaches for Drug Design Targeting Cruzipain

by Aiman Parvez, Jeong-Sang Lee, Waleed Alam, Hilal Tayara and Kil To Chong

Int. J. Mol. Sci. 2024, 25(7), 3747; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25073747 - 27 Mar 2024

Viewed by 534

Abstract

Cruzipain inhibitors are required after medications to treat Chagas disease because of the need for safer, more effective treatments. Trypanosoma cruzi is the source of cruzipain, a crucial cysteine protease that has driven interest in using computational methods to create more effective inhibitors. We employed a 3D-QSAR model, using a dataset of 36 known inhibitors, and a pharmacophore model to identify potential inhibitors for cruzipain. We also built a deep learning model using the Deep purpose library, trained on 204 active compounds, and validated it with a specific test set. During a comprehensive screening of the Drug Bank database of 8533 molecules, pharmacophore and deep learning models identified 1012 and 340 drug-like molecules, respectively. These molecules were further evaluated through molecular docking, followed by induced-fit docking. Ultimately, molecular dynamics simulation was performed for the final potent inhibitors that exhibited strong binding interactions. These results present four novel cruzipain inhibitors that can inhibit the cruzipain protein of T. cruzi. Full article

(This article belongs to the Special Issue Computer-Aided Drug Design Strategies)

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16 pages, 4245 KiB

Open AccessArticle

Prediction of Human Pharmacokinetics of E0703, a Novel Radioprotective Agent, Using Physiologically Based Pharmacokinetic Modeling and an Interspecies Extrapolation Approach

by Yun-Xuan Ge, Zhuo Zhang, Jia-Yi Yan, Zeng-Chun Ma, Yu-Guang Wang, Cheng-Rong Xiao, Xiao-Mei Zhuang and Yue Gao

Int. J. Mol. Sci. 2024, 25(5), 3047; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25053047 - 06 Mar 2024

Viewed by 566

Abstract

E0703, a new steroidal compound optimized from estradiol, significantly increased cell proliferation and the survival rate of KM mice and beagles after ionizing radiation. In this study, we characterize its preclinical pharmacokinetics (PK) and predict its human PK using a physiologically based pharmacokinetic (PBPK) model. The preclinical PK of E0703 was studied in mice and Rhesus monkeys. Asian human clearance (CL) values for E0703 were predicted from various allometric methods. The human PK profiles of E0703 (30 mg) were predicted by the PBPK model in Gastro Plus software 9.8 (SimulationsPlus, Lancaster, CA, USA). Furthermore, tissue distribution and the human PK profiles of different administration dosages and forms were predicted. The 0.002 L/h of CL and 0.005 L of V_ss in mice were calculated and optimized from observed PK data. The plasma exposure of E0703 was availably predicted by the CL using the simple allometry (SA) method. The plasma concentration–time profiles of other dosages (20 and 40 mg) and two oral administrations (30 mg) were well-fitted to the observed values. In addition, the PK profile of target organs for E0703 exhibited a higher peak concentration (C_max) and AUC than plasma. The developed E0703-PBPK model, which is precisely applicable to multiple species, benefits from further clinical development to predict PK in humans. Full article

(This article belongs to the Special Issue Computer-Aided Drug Design Strategies)

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13 pages, 1549 KiB

Open AccessArticle

Target Prediction by Multiple Virtual Screenings: Analyzing the SARS-CoV-2 Phenotypic Screening by the Docking Simulations Submitted to the MEDIATE Initiative

by Silvia Gervasoni, Candida Manelfi, Sara Adobati, Carmine Talarico, Akash Deep Biswas, Alessandro Pedretti, Giulio Vistoli and Andrea R. Beccari

Int. J. Mol. Sci. 2024, 25(1), 450; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms25010450 - 29 Dec 2023

Cited by 1 | Viewed by 998

Abstract

Phenotypic screenings are usually combined with deconvolution techniques to characterize the mechanism of action for the retrieved hits. These studies can be supported by various computational analyses, although docking simulations are rarely employed. The present study aims to assess if multiple docking calculations can prove successful in target prediction. In detail, the docking simulations submitted to the MEDIATE initiative are utilized to predict the viral targets involved in the hits retrieved by a recently published cytopathic screening. Multiple docking results are combined by the EFO approach to develop target-specific consensus models. The combination of multiple docking simulations enhances the performances of the developed consensus models (average increases in EF1% value of 40% and 25% when combining three and two docking runs, respectively). These models are able to propose reliable targets for about half of the retrieved hits (31 out of 59). Thus, the study emphasizes that docking simulations might be effective in target identification and provide a convincing validation for the collaborative strategies that inspire the MEDIATE initiative. Disappointingly, cross-target and cross-program correlations suggest that common scoring functions are not specific enough for the simulated target. Full article

(This article belongs to the Special Issue Computer-Aided Drug Design Strategies)

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25 pages, 7949 KiB

Open AccessArticle

Exploring the Binding Effects of Natural Products and Antihypertensive Drugs on SARS-CoV-2: An In Silico Investigation of Main Protease and Spike Protein

by Kalliopi Moschovou, Maria Antoniou, Eleni Chontzopoulou, Konstantinos D. Papavasileiou, Georgia Melagraki, Antreas Afantitis and Thomas Mavromoustakos

Int. J. Mol. Sci. 2023, 24(21), 15894; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms242115894 - 02 Nov 2023

Cited by 3 | Viewed by 1049

Abstract

In this in silico study, we conducted an in-depth exploration of the potential of natural products and antihypertensive molecules that could serve as inhibitors targeting the key proteins of the SARS-CoV-2 virus: the main protease (Mpro) and the spike (S) protein. By utilizing Induced Fit Docking (IFD), we assessed the binding affinities of the molecules under study to these crucial viral components. To further comprehend the stability and molecular interactions of the “protein-ligand” complexes that derived from docking studies, we performed molecular dynamics (MD) simulations, shedding light on the molecular basis of potential drug candidates for COVID-19 treatment. Moreover, we employed Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculations on all “protein-ligand” complexes, underscoring the robust binding capabilities of rosmarinic acid, curcumin, and quercetin against Mpro, and salvianolic acid b, rosmarinic acid, and quercetin toward the S protein. Furthermore, in order to expand our search for potent inhibitors, we conducted a structure similarity analysis, using the Enalos Suite, based on the molecules that indicated the most favored results in the in silico studies. The Enalos Suite generated 115 structurally similar compounds to salvianolic acid, rosmarinic acid, and quercetin. These compounds underwent IFD calculations, leading to the identification of two salvianolic acid analogues that exhibited strong binding to all the examined binding sites in both proteins, showcasing their potential as multi-target inhibitors. These findings introduce exciting possibilities for the development of novel therapeutic agents aiming to effectively disrupt the SARS-CoV-2 virus lifecycle. Full article

(This article belongs to the Special Issue Computer-Aided Drug Design Strategies)

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