1
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Miljković F, Bajorath J. Kinase Drug Discovery: Impact of Open Science and Artificial Intelligence. Mol Pharm 2024. [PMID: 39240193 DOI: 10.1021/acs.molpharmaceut.4c00659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2024]
Abstract
Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.
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Affiliation(s)
- Filip Miljković
- Medicinal Chemistry, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, SE-43183 Gothenburg, Sweden
| | - Jürgen Bajorath
- Department of Life Science Informatics and Data Science, B-IT, Lamarr Institute for Machine Learning and Artificial Intelligence, LIMES Program Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, 53115 Bonn, Germany
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2
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Manchev YT, Burn MJ, Popelier PLA. Ichor: A Python library for computational chemistry data management and machine learning force field development. J Comput Chem 2024. [PMID: 39215569 DOI: 10.1002/jcc.27477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Revised: 07/09/2024] [Accepted: 07/18/2024] [Indexed: 09/04/2024]
Abstract
We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.
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Affiliation(s)
- Yulian T Manchev
- Department of Chemistry, The University of Manchester, Manchester, UK
| | - Matthew J Burn
- Department of Chemistry, The University of Manchester, Manchester, UK
| | - Paul L A Popelier
- Department of Chemistry, The University of Manchester, Manchester, UK
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3
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Gholap AD, Uddin MJ, Faiyazuddin M, Omri A, Gowri S, Khalid M. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput Biol Med 2024; 178:108702. [PMID: 38878397 DOI: 10.1016/j.compbiomed.2024.108702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/12/2024] [Accepted: 06/01/2024] [Indexed: 07/24/2024]
Abstract
Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.
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Affiliation(s)
- Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar, Maharashtra, 401404, India.
| | - Md Jasim Uddin
- Department of Pharmaceutical Technology, Faculty of Pharmacy, Universiti Malaya, 50603, Kuala Lumpur, Malaysia.
| | - Md Faiyazuddin
- School of Pharmacy, Al-Karim University, Katihar, Bihar, 854106, India; Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Tamil Nadu, India.
| | - Abdelwahab Omri
- Department of Chemistry and Biochemistry, The Novel Drug and Vaccine Delivery Systems Facility, Laurentian University, Sudbury, ON, P3E 2C6, Canada.
| | - S Gowri
- PG & Research, Department of Physics, Cauvery College for Women, Tiruchirapalli, Tamil Nadu, 620018, India
| | - Mohammad Khalid
- James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK; Sunway Centre for Electrochemical Energy and Sustainable Technology (SCEEST), School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, 47500 Selangor Darul Ehsan, Malaysia; University Centre for Research and Development, Chandigarh University, Mohali, Punjab, 140413, India.
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4
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Yu M, Shen Z, Zhang S, Zhang Y, Zhao H, Zhang L. The active components of Erzhi wan and their anti-Alzheimer's disease mechanisms determined by an integrative approach of network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulation. Heliyon 2024; 10:e33761. [PMID: 39027618 PMCID: PMC11255520 DOI: 10.1016/j.heliyon.2024.e33761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 06/21/2024] [Accepted: 06/26/2024] [Indexed: 07/20/2024] Open
Abstract
Erzhi Wan (EZW), a classic Traditional Chinese Medicine formula, has shown promise as a potential therapeutic option for Alzheimer's disease (AD), yet its mechanism remains elusive. Herein, we employed an integrative in-silico approach to investigate the active components and their mechanisms against AD. We screened four active components with blood-brain barrier permeabilities from TCMSP, along with 307 corresponding targets predicted by SwissTargetPrediction, PharmMapper, and TCMbank websites. Then, we retrieved 2260 AD-related targets from Genecards, OMIM, and NCBI databases. Furthermore, we constructed the protein-protein interaction (PPI) network of the intersected targets via the STRING database and performed the GO and KEGG enrichment analyses using the "clusterProfiler" R package. The results showed that the intersected targets were intimately related to the p53/PI3K/Akt signaling pathway, serotonergic synapse, and response to oxygen level. Subsequently, 25 core targets were found differentially expressed in brain regions by bioinformatics analyses of GEO datasets of clinical samples from the Alzdata database. The binding sites and stabilities between the active components and the core targets were investigated by the molecular docking approach using Autodock 4.2.6 software, followed by pocket detection and druggability assessment via the DoGSiteScorer server. The results showed that acacetin, β-sitosterol, and 3-O-acetyldammarenediol-II strongly interacted with the druggable pockets of AR, CASP8, POLB, and PREP. Eventually, the docking results were further cross-referenced with the literature research and validated by 100 ns of molecular dynamics simulations using GROMACS software. Binding free energies were calculated via MM/PBSA strategy combined with interaction entropy. The simulation results indicated stable bindings between four docking pairs including acacetin-AR, acacetin-CASP8, β-sitosterol-POLB, and 3-O-acetyldammarenediol-II-PREP. Overall, our study demonstrated a theoretical basis for how three active components of EZW confer efficacy against AD. It provides a promising reference for subsequent research regarding drug discoveries and clinical applications.
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Affiliation(s)
- Meng Yu
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Zhongqi Shen
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Shaozhi Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Hongwei Zhao
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
| | - Longfei Zhang
- Experimental Center, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250355, China
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5
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Shah SK, Chaple DR, Masand VH, Jawarkar RD, Chaudhari S, Abiramasundari A, Zaki MEA, Al-Hussain SA. Multi-Target In-Silico modeling strategies to discover novel angiotensin converting enzyme and neprilysin dual inhibitors. Sci Rep 2024; 14:15991. [PMID: 38987327 PMCID: PMC11237057 DOI: 10.1038/s41598-024-66230-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/28/2024] [Indexed: 07/12/2024] Open
Abstract
Cardiovascular diseases, including heart failure, stroke, and hypertension, affect 608 million people worldwide and cause 32% of deaths. Combination therapy is required in 60% of patients, involving concurrent Renin-Angiotensin-Aldosterone-System (RAAS) and Neprilysin inhibition. This study introduces a novel multi-target in-silico modeling technique (mt-QSAR) to evaluate the inhibitory potential against Neprilysin and Angiotensin-converting enzymes. Using both linear (GA-LDA) and non-linear (RF) algorithms, mt-QSAR classification models were developed using 983 chemicals to predict inhibitory effects on Neprilysin and Angiotensin-converting enzymes. The Box-Jenkins method, feature selection method, and machine learning algorithms were employed to obtain the most predictive model with ~ 90% overall accuracy. Additionally, the study employed virtual screening of designed scaffolds (Chalcone and its analogues, 1,3-Thiazole, 1,3,4-Thiadiazole) applying developed mt-QSAR models and molecular docking. The identified virtual hits underwent successive filtration steps, incorporating assessments of drug-likeness, ADMET profiles, and synthetic accessibility tools. Finally, Molecular dynamic simulations were then used to identify and rank the most favourable compounds. The data acquired from this study may provide crucial direction for the identification of new multi-targeted cardiovascular inhibitors.
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Affiliation(s)
- Sapan K Shah
- Department of Pharmaceutical Chemistry, Priyadarshini J. L. College of Pharmacy, Hingna Road, Nagpur, 440016, Maharashtra, India.
| | - Dinesh R Chaple
- Department of Pharmaceutical Chemistry, Priyadarshini J. L. College of Pharmacy, Hingna Road, Nagpur, 440016, Maharashtra, India
| | - Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, 444602, Maharashtra, India
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr. Rajendra Gode Institute of Pharmacy, University Mardi Road, Amravati, 444603, India
| | - Somdatta Chaudhari
- Department of Pharmaceutical Chemistry, Modern College of Pharmacy, Nigdi, Pune, India
| | | | - Magdi E A Zaki
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, 11623, Saudi Arabia.
| | - Sami A Al-Hussain
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, 11623, Saudi Arabia
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6
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Myung Y, de Sá AGC, Ascher DB. Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction. Nucleic Acids Res 2024; 52:W469-W475. [PMID: 38634808 PMCID: PMC11223837 DOI: 10.1093/nar/gkae254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 03/20/2024] [Accepted: 04/10/2024] [Indexed: 04/19/2024] Open
Abstract
Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described from four perspectives: absorption, distribution, metabolism and excretion-all of which are closely related to a fifth perspective, toxicity (ADMET). Since obtaining ADMET data from in vitro, in vivo or pre-clinical stages is time consuming and expensive, many efforts have been made to predict ADMET properties via computational approaches. However, the majority of available methods are limited in their ability to provide pharmacokinetics and toxicity for diverse targets, ensure good overall accuracy, and offer ease of use, interpretability and extensibility for further optimizations. Here, we introduce Deep-PK, a deep learning-based pharmacokinetic and toxicity prediction, analysis and optimization platform. We applied graph neural networks and graph-based signatures as a graph-level feature to yield the best predictive performance across 73 endpoints, including 64 ADMET and 9 general properties. With these powerful models, Deep-PK supports molecular optimization and interpretation, aiding users in optimizing and understanding pharmacokinetics and toxicity for given input molecules. The Deep-PK is freely available at https://biosig.lab.uq.edu.au/deeppk/.
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Affiliation(s)
- Yoochan Myung
- School of Chemistry and Molecular Biosciences, The Australian Centre for Ecogenomics, The University of Queensland, Brisbane, Queensland 4072, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
| | - Alex G C de Sá
- School of Chemistry and Molecular Biosciences, The Australian Centre for Ecogenomics, The University of Queensland, Brisbane, Queensland 4072, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Parkville, Victoria 3010, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, The Australian Centre for Ecogenomics, The University of Queensland, Brisbane, Queensland 4072, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria 3004, Australia
- Baker Department of Cardiometabolic Health, The University of Melbourne, Parkville, Victoria 3010, Australia
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7
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Ekins S, Lane TR, Urbina F, Puhl AC. In silico ADME/tox comes of age: twenty years later. Xenobiotica 2024; 54:352-358. [PMID: 37539466 PMCID: PMC10850432 DOI: 10.1080/00498254.2023.2245049] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/05/2023]
Abstract
In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.
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Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Thomas R. Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Ana C. Puhl
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
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8
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Papikinos T, Krokidis M, Vrahatis A, Vlamos P, Exarchos TP. Drug repurposing for obsessive-compulsive disorder using deep learning-based binding affinity prediction models. AIMS Neurosci 2024; 11:203-211. [PMID: 38988885 PMCID: PMC11230860 DOI: 10.3934/neuroscience.2024013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 05/29/2024] [Accepted: 06/04/2024] [Indexed: 07/12/2024] Open
Abstract
Obsessive-compulsive disorder (OCD) is a chronic psychiatric disease in which patients suffer from obsessions compelling them to engage in specific rituals as a temporary measure to alleviate stress. In this study, deep learning-based methods were used to build three models which predict the likelihood of a molecule interacting with three biological targets relevant to OCD, SERT, D2, and NMDA. Then, an ensemble model based on those models was created which underwent external validation on a large drug database using random sampling. Finally, case studies of molecules exhibiting high scores underwent bibliographic validation showcasing that good performance in the ensemble model can indicate connection with OCD pathophysiology, suggesting that it can be used to screen molecule databases for drug-repurposing purposes.
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Affiliation(s)
- Thomas Papikinos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Marios Krokidis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Aris Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
| | - Themis P Exarchos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece
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9
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Bayarri G, Andrio P, Gelpí JL, Hospital A, Orozco M. Using interactive Jupyter Notebooks and BioConda for FAIR and reproducible biomolecular simulation workflows. PLoS Comput Biol 2024; 20:e1012173. [PMID: 38900779 PMCID: PMC11189206 DOI: 10.1371/journal.pcbi.1012173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/22/2024] Open
Abstract
Interactive Jupyter Notebooks in combination with Conda environments can be used to generate FAIR (Findable, Accessible, Interoperable and Reusable/Reproducible) biomolecular simulation workflows. The interactive programming code accompanied by documentation and the possibility to inspect intermediate results with versatile graphical charts and data visualization is very helpful, especially in iterative processes, where parameters might be adjusted to a particular system of interest. This work presents a collection of FAIR notebooks covering various areas of the biomolecular simulation field, such as molecular dynamics (MD), protein-ligand docking, molecular checking/modeling, molecular interactions, and free energy perturbations. Workflows can be launched with myBinder or easily installed in a local system. The collection of notebooks aims to provide a compilation of demonstration workflows, and it is continuously updated and expanded with examples using new methodologies and tools.
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Affiliation(s)
- Genís Bayarri
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Pau Andrio
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
| | - Josep Lluís Gelpí
- Barcelona Supercomputing Center (BSC), Barcelona, Spain
- Department of Biochemistry and Biomedicine, University of Barcelona, Barcelona, Spain
| | - Adam Hospital
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Modesto Orozco
- Institute for Research in Biomedicine (IRB Barcelona), the Barcelona Institute of Science and Technology, Barcelona, Spain
- Department of Biochemistry and Biomedicine, University of Barcelona, Barcelona, Spain
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Pathak RK, Jung DW, Shin SH, Ryu BY, Lee HS, Kim JM. Deciphering the mechanisms and interactions of the endocrine disruptor bisphenol A and its analogs with the androgen receptor. JOURNAL OF HAZARDOUS MATERIALS 2024; 469:133935. [PMID: 38442602 DOI: 10.1016/j.jhazmat.2024.133935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/28/2024] [Accepted: 02/29/2024] [Indexed: 03/07/2024]
Abstract
Bisphenol A (BPA) and its various forms used as BPA alternatives in industries are recognized toxic compounds and antiandrogenic endocrine disruptors. These chemicals are widespread in the environment and frequently detected in biological samples. Concerns exist about their impact on hormones, disrupting natural biological processes in humans, together with their negative impacts on the environment and biotic life. This study aims to characterize the interaction between BPA analogs and the androgen receptor (AR) and the effect on the receptor's normal activity. To achieve this goal, molecular docking was conducted with BPA and its analogs and dihydrotestosterone (DHT) as a reference ligand. Four BPA analogs exhibited higher affinity (-10.2 to -8.7 kcal/mol) for AR compared to BPA (-8.6 kcal/mol), displaying distinct interaction patterns. Interestingly, DHT (-11.0 kcal/mol) shared a binding pattern with BPA. ADMET analysis of the top 10 compounds, followed by molecular dynamics simulations, revealed toxicity and dynamic behavior. Experimental studies demonstrated that only BPA disrupts DHT-induced AR dimerization, thereby affecting AR's function due to its binding nature. This similarity to DHT was observed during computational analysis. These findings emphasize the importance of targeted strategies to mitigate BPA toxicity, offering crucial insights for interventions in human health and environmental well-being.
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Affiliation(s)
- Rajesh Kumar Pathak
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
| | - Da-Woon Jung
- Department of Food Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
| | - Seung-Hee Shin
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
| | - Buom-Yong Ryu
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea
| | - Hee-Seok Lee
- Department of Food Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea; Department of Food Safety and Regulatory Science, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea.
| | - Jun-Mo Kim
- Department of Animal Science and Technology, Chung-Ang University, Anseong-si, Gyeonggi-do 17546, Republic of Korea.
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11
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Williams DC, Inala N. Physics-Informed Generative Model for Drug-like Molecule Conformers. J Chem Inf Model 2024; 64:2988-3007. [PMID: 38486425 DOI: 10.1021/acs.jcim.3c01816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
We present a diffusion-based generative model for conformer generation. Our model is focused on the reproduction of the bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on large, synthetic data sets of diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method, high accuracy is achieved for bonded parameters, exceeding that of conventional, knowledge-based methods. Results are also compared to experimental structures from the Protein Databank and the Cambridge Structural Database.
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Affiliation(s)
- David C Williams
- Nobias Therapeutics, Inc., 144 S Whisman Rd, Suite C, Mountain View, California 94041, United States
| | - Neil Inala
- Nobias Therapeutics, Inc., 144 S Whisman Rd, Suite C, Mountain View, California 94041, United States
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12
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Waseem T, Rajput TA, Mushtaq MS, Babar MM, Rajadas J. Computational biology approaches for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:91-109. [PMID: 38789189 DOI: 10.1016/bs.pmbts.2024.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.
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Affiliation(s)
- Tanya Waseem
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Tausif Ahmed Rajput
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | | | - Mustafeez Mujtaba Babar
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan; Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States.
| | - Jayakumar Rajadas
- Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States
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Faloye KO, Tripathi MK, Adesida SA, Oguntimehin SA, Oyetunde YM, Adewole AH, Ogunlowo II, Idowu EA, Olayemi UI, Dosumu OD. Antimalarial potential, LC-MS secondary metabolite profiling and computational studies of Zingiber officinale. J Biomol Struct Dyn 2024; 42:2570-2585. [PMID: 37116195 DOI: 10.1080/07391102.2023.2205949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 04/17/2023] [Indexed: 04/30/2023]
Abstract
Malaria is among the top-ranked parasitic diseases that pose a threat to the existence of the human race. This study evaluated the antimalarial effect of the rhizome of Zingiber officinale in infected mice, performed secondary metabolite profiling and detailed computational antimalarial evaluation through molecular docking, molecular dynamics (MD) simulation and density functional theory methods. The antimalarial potential of Z. officinale was performed using the in vivo chemosuppressive model; secondary metabolite profiling was carried out using liquid chromatography-mass spectrometry (LC-MS). Molecular docking was performed with Autodock Vina while the MD simulation was performed with Schrodinger desmond suite for 100 ns and DFT calculations with B3LYP (6-31G) basis set. The extract showed 64% parasitaemia suppression, with a dose-dependent increase in activity up to 200 mg/kg. The chemical profiling of the extract tentatively identified eight phytochemicals. The molecular docking studies with plasmepsin II and Plasmodium falciparum dihydrofolate reductase-thymidylate synthase (PfDHFR-TS) identified gingerenone A as the hit molecule, and MMGBSA values corroborate the binding energies obtained. The electronic parameters of gingerenone A revealed its significant antimalarial potential. The antimalarial activity elicited by the extract of Z. officinale and the bioactive chemical constituent supports its usage in ethnomedicine.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Kolade O Faloye
- Department of Chemistry, Faculty of Science, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Manish K Tripathi
- Pharmaceutical Chemistry Research Laboratory, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India
| | - Stephen A Adesida
- Department of Pharmacognosy, Faculty of Pharmacy, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Samuel A Oguntimehin
- Department of Pharmacognosy, Faculty of Pharmacy, University of Ibadan, Ibadan, Nigeria
| | - Yemisi M Oyetunde
- Department of Pharmacognosy, Faculty of Pharmacy, University of Ibadan, Ibadan, Nigeria
| | - Adetola H Adewole
- Department of Chemistry, University of Pretoria, Pretoria, South Africa
| | - Ifeoluwa I Ogunlowo
- Department of Pharmacognosy, Faculty of Pharmacy, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Esther A Idowu
- Department of Pharmacognosy, Faculty of Pharmacy, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Uduak I Olayemi
- Department of Pharmacognosy, Faculty of Pharmacy, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Olamide D Dosumu
- Department of Botany, Faculty of Science, Obafemi Awolowo University, Ile-Ife, Nigeria
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Shah S, Chaple D, Masand VH, Zaki MEA, Al-Hussain SA, Shah A, Arora S, Jawarkar R, Tauqeer M. In silico study to recognize novel angiotensin-converting-enzyme-I inhibitors by 2D-QSAR and constraint-based molecular simulations. J Biomol Struct Dyn 2024; 42:2211-2230. [PMID: 37128759 DOI: 10.1080/07391102.2023.2203261] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 04/10/2023] [Indexed: 05/03/2023]
Abstract
Cardiovascular diseases (CVD) such as heart failure, stroke, and hypertension affect 64.3 million people worldwide and are responsible for 30% of all deaths. Primary inhibition of the angiotensin-converting enzyme (ACE) is significant in the management of CVD. In the present study, the genetic algorithm-multiple linear regressions (GA-MLR) method is used to generate highly predictive and statistically significant (R2 = 0.70-0.75, Q2LOO=0.67-0.73, Q2LMO=0.66-0.72, CCCex=0.70-0.78) quantitative structure-activity relationships (QSAR) models conferring to OECD requirements using a dataset of 255 structurally diverse and experimentally validated ACE inhibitors. The models contain simply illustratable Padel, Estate, and PyDescriptors that correlate structural scaffold requisite for ACE inhibition. Also, constraint-based molecular docking reveals an interaction profile between ligands and enzymes which is then correlated with the essential structural features associated with the QSAR models. The QSAR-based virtual screening was utilized to find novel lead molecules from a designed database of 102 thiadiazole derivatives. The Applicability domain (AD), Molecular Docking, Molecular dynamics, and ADMET analysis suggest two compound D24 and D40 are inflexibly linked to the protein binding site and follows drug-likeness properties.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Sapan Shah
- Department of Pharmaceutical Chemistry, Priyadarshini J. L. College of Pharmacy, Nagpur, Maharashtra, India
| | - Dinesh Chaple
- Department of Pharmaceutical Chemistry, Priyadarshini J. L. College of Pharmacy, Nagpur, Maharashtra, India
| | - Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Ashish Shah
- Department of Pharmacy, Sumandeep Vidyapeeth, Vadodara, Gujarat, India
| | - Sumit Arora
- Department of Pharmacognosy, Gurunanak College of Pharmacy, Nagpur, Maharashtra, India
| | - Rahul Jawarkar
- Department of Medicinal Chemistry and Drug Discovery, Dr. Rajendra Gode Institute of Pharmacy, Amravati, India
| | - Mohammad Tauqeer
- Department of Pharmacognosy, Dr. Arun Motghare College of Pharmacy, Kosra-Kondha, Maharashtra, India
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15
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Berhe H, Kumar Cinthakunta Sridhar M, Zerihun M, Qvit N. The Potential Use of Peptides in the Fight against Chagas Disease and Leishmaniasis. Pharmaceutics 2024; 16:227. [PMID: 38399281 PMCID: PMC10892537 DOI: 10.3390/pharmaceutics16020227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/28/2023] [Accepted: 01/30/2024] [Indexed: 02/25/2024] Open
Abstract
Chagas disease and leishmaniasis are both neglected tropical diseases that affect millions of people around the world. Leishmaniasis is currently the second most widespread vector-borne parasitic disease after malaria. The World Health Organization records approximately 0.7-1 million newly diagnosed leishmaniasis cases each year, resulting in approximately 20,000-30,000 deaths. Also, 25 million people worldwide are at risk of Chagas disease and an estimated 6 million people are infected with Trypanosoma cruzi. Pentavalent antimonials, amphotericin B, miltefosine, paromomycin, and pentamidine are currently used to treat leishmaniasis. Also, nifurtimox and benznidazole are two drugs currently used to treat Chagas disease. These drugs are associated with toxicity problems such as nephrotoxicity and cardiotoxicity, in addition to resistance problems. As a result, the discovery of novel therapeutic agents has emerged as a top priority and a promising alternative. Overall, there is a need for new and effective treatments for Chagas disease and leishmaniasis, as the current drugs have significant limitations. Peptide-based drugs are attractive due to their high selectiveness, effectiveness, low toxicity, and ease of production. This paper reviews the potential use of peptides in the treatment of Chagas disease and leishmaniasis. Several studies have demonstrated that peptides are effective against Chagas disease and leishmaniasis, suggesting their use in drug therapy for these diseases. Overall, peptides have the potential to be effective therapeutic agents against Chagas disease and leishmaniasis, but more research is needed to fully investigate their potential.
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Affiliation(s)
| | | | | | - Nir Qvit
- The Azrieli Faculty of Medicine in the Galilee, Bar-Ilan University, Safed 1311502, Israel; (H.B.); (M.K.C.S.); (M.Z.)
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16
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Shastry RP, Abhinand CS. Targeting the Pseudomonas aeruginosa quorum sensing system to inhibit virulence factors and eradicate biofilm formation using AHL-analogue phytochemicals. J Biomol Struct Dyn 2024; 42:1956-1965. [PMID: 37097921 DOI: 10.1080/07391102.2023.2202270] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 04/09/2023] [Indexed: 04/26/2023]
Abstract
Quorum sensing plays a major role in the expression of virulence and development of biofilm in the human pathogen Pseudomonas aeruginosa. Natural compounds are well-known for their antibacterial characteristics by blocking various metabolic pathways. The goal of this study is to find natural compounds that mimic AHL (Acyl homoserine lactone) and suppress virulence in P. aeruginosa, which is triggered by quorum sensing-dependent pathways as an alternative drug development strategy. To support this rationale, functional network analysis and in silico investigations were carried out to find natural AHL analogues, followed by molecular docking studies. Out of the 16 top-hit AHL analogues derived from phytochemicals, seven ligands were found to bind to the quorum sensing activator proteins. Cassialactone, an AHL analogue, exhibited the highest binding affinity for RhlI, RhlR, and PqsE of P. aeruginosa, with a docking score of -9.4, -8.9, and -8.7 kcal/mol, respectively. 2(5H)-Furanone, a well-known inhibitor, was also docked to compare the docking score and intermolecular interactions between the ligand and the target protein. Furthermore, molecular dynamics simulations and binding free energy calculations were performed to determine the stability of the docked complexes. Additionally, the ADME properties of the analogues were also analyzed to evaluate the pharmacological parameters. Functional network analysis further showed that the interconnectedness of proteins such as RhlI, RhlR, LasI, and PqsE with the virulence and biofilm phenotype of the pathogen could offer potential as a therapeutic target.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rajesh P Shastry
- Division of Microbiology and Biotechnology, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, India
| | - Chandran S Abhinand
- Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, Karnataka, India
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Kutsal M, Ucar F, Kati N. Computational drug discovery on human immunodeficiency virus with a customized long short-term memory variational autoencoder deep-learning architecture. CPT Pharmacometrics Syst Pharmacol 2024; 13:308-316. [PMID: 38010989 PMCID: PMC10864928 DOI: 10.1002/psp4.13085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 11/01/2023] [Accepted: 11/07/2023] [Indexed: 11/29/2023] Open
Abstract
Despite attempts to control the spread of human immunodeficiency virus (HIV) through the use of anti-HIV medications, the absence of an effective vaccine continues to present a significant obstacle. In addition, the development of drug resistance by HIV underscores the necessity for computational drug discovery methods to identify novel therapies. This investigation specifically focused on employing a long short-term memory (LSTM) variational autoencoder deep-learning architecture for computational drug discovery in relation to HIV. Our data set comprised simplified molecular input line entry system (SMILES)-encoded compounds, which were used to train the LSTM autoencoder. Remarkably, our model achieved a training accuracy of 91%, with a data set containing 1377 compounds. Leveraging the generative model derived from the training phase, we generated potential new drugs for combating HIV and assessed their interaction with the virus using a previously developed artificial intelligence model. Lastly, we verified the drug likeliness of our computationally generated compounds in accordance with Lipinski's rule of five. Overall, our study presents a promising approach to computational drug discovery in the ongoing battle against HIV.
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Affiliation(s)
- Mucahit Kutsal
- Institute of Theoretical Physics and Astrophysics, Quantum Information TechnologyUniversity of GdańskGdańskPoland
| | - Ferhat Ucar
- Faculty of Technology, Software EngineeringFırat UniversityElazigTurkey
| | - Nida Kati
- Faculty of Technology, Materials and Metallurgical EngineeringFırat UniversityElazigTurkey
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18
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Alfalahi AO, Alrawi MS, Theer RM, Dawood KF, Charfi S, Almehemdi AF. Phytochemical analysis and antifungal potential of two Launaea mucronata (Forssk.) Muschl and Launaea nudicaulis (L.) Hook.fil. wildly growing in Anbar province, Iraq. JOURNAL OF ETHNOPHARMACOLOGY 2024; 318:116965. [PMID: 37506779 DOI: 10.1016/j.jep.2023.116965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/21/2023] [Accepted: 07/23/2023] [Indexed: 07/30/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Plant fungi are a serious problem in agriculture. Even though synthetic fungicides are an efficient control method, several negative side effects emerge from their extensive use, such as health problems, environmental pollution, and the emergence of resistant microorganisms. Thus, it is becoming more and more urgent to search for alternative control methods. AIM OF THE STUDY The aim of our study was to analyze phytochemical composition and antifungal potential of Launaea mucronata (Forssk.) Muschl. and Launaea nudicaulis (L.) Hook. fil. wildly growing in Anbar province, Iraq. In addition, molecular analysis was used to identify the plants species and molecular docking analysis was investigated between the major phytochemicals present in these plants and three selected fungal proteins, in order to assess the antifungal activity of the selected biochemicals against these proteins. MATERIALS AND METHODS Molecular analysis was performed using ITS sequencing protocol. The phytochemical analysis was done using GC-MS technique, while molecular docking analysis was performed by FRED application between selected compounds from each plant and three enzymes: 17β-hydroxysteroid dehydrogenase, endochitinase, and 14-α-demethylase. Finally, the antifungal activity was assessed by measuring inhibition percentage of Fusarium solani and Macrophomina phaseolina growth treated with ethanomethanolic extract of each plant. RESULTS Molecular analysis identified the selected plants as L. mucronata and L. nudicaulis, with an ITS region of 600 bp. Phytochemical analysis of Launaea spp. reported the presence of 35 compounds in each ethanomethanolic extract, belonging to different classes. L. mucronata was mainly formed of lupeol (9.33%), whereas L. nudicaulis extract was dominated by the heterocyclic compound 4-(3-methoxyphenoxy)-1,2,5-oxadiazol-3-amine (20.2%). Furthermore, molecular docking analysis showed that 4H-pyran-4-one,2,3-dihydro-3,5-dihydroxy-6-methyl from L. mucronata and gulonic acid Ƴ-lactone from L. nudicaulis possessed the highest affinity score to 17-β-hydroxysteroid dehydrogenase (-4.584 and -7.811 kcal/mol, respectively). Sucrose from L. mucronata and glutaric acid, di(3,4-difluorobenzyl) ester from L. nudicaulis gave the highest affinity to endochitinase (-7.979 and - 8.446 kcal/mol, respectively). In addition, sterol 14-α-demethylase was affinitive to sucrose from L. mucronata and glutaric acid, di(3,4-difluorobenzyl) ester from L. nudicaulis via energetic score of -10.002 and -9.582 kcal/mol, respectively. Both extracts exhibited antifungal activity against F. solani and M. phaseolina in a dose-dependent manner. CONCLUSIONS This study confirms the antifungal potential of both Launaea spp. explained by the presence of phytochemicals with antimicrobial properties. These compounds have potential to be used as new drugs to treat infectious diseases caused by pathogens. Consequently, plants from Launaea genus could be a raw material for many studies such as therapeutic, taxonomical, drug modelling, and antifungal agent.
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Affiliation(s)
- Ayoob Obaid Alfalahi
- Department of Plant Protection, College of Agriculture, University of Anbar, Iraq.
| | - Marwa Shakib Alrawi
- Department of Pharmacology &Toxicology, College of Pharmacy, University of Anbar, Iraq.
| | - Rashid Mushrif Theer
- Department of Plant Protection, College of Agriculture, University of Anbar, Iraq.
| | - Kutaiba Farhan Dawood
- Department of Scientific Affairs, University Headquarter, University of Anbar, Iraq.
| | - Saoulajan Charfi
- Laboratory of Biology and Health, Department of Biology, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan, 93000, Morocco.
| | - Ali F Almehemdi
- Department of Conservation Agriculture, Center of Desert Studies, University of Anbar, Iraq.
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19
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Geraci J, Bhargava R, Qorri B, Leonchyk P, Cook D, Cook M, Sie F, Pani L. Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS. Front Comput Neurosci 2024; 17:1199736. [PMID: 38260713 PMCID: PMC10801647 DOI: 10.3389/fncom.2023.1199736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 11/20/2023] [Indexed: 01/24/2024] Open
Abstract
Introduction Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states. Motivation In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets. Problem statement Using a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory. Methodology We use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input. Results We extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset. Conclusion In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.
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Affiliation(s)
- Joseph Geraci
- NetraMark Corp, Toronto, ON, Canada
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
- Centre for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, GA, United States
- Arthur C. Clarke Center for Human Imagination, School of Physical Sciences, University of California San Diego, San Diego, CA, United States
| | - Ravi Bhargava
- Department of Biomedical and Molecular Science, Queens University, Kingston, ON, Canada
- Science and Research, Roche Integrated Informatics, F. Hoffmann La-Roche, Toronto, ON, Canada
| | | | | | - Douglas Cook
- NetraMark Corp, Toronto, ON, Canada
- Department of Surgery, Queen's University, Kingston, ON, Canada
| | - Moses Cook
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Fanny Sie
- Science and Research, Roche Integrated Informatics, F. Hoffmann La-Roche, Toronto, ON, Canada
| | - Luca Pani
- NetraMark Corp, Toronto, ON, Canada
- Department of Psychiatry and Behavioral Sciences, Leonard M. Miller School of Medicine, University of Miami, Coral Gables, FL, United States
- Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
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20
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Bonde B. Edge, Fog, and Cloud Against Disease: The Potential of High-Performance Cloud Computing for Pharma Drug Discovery. Methods Mol Biol 2024; 2716:181-202. [PMID: 37702940 DOI: 10.1007/978-1-0716-3449-3_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
The high-performance computing (HPC) platform for large-scale drug discovery simulation demands significant investment in speciality hardware, maintenance, resource management, and running costs. The rapid growth in computing hardware has made it possible to provide cost-effective, robust, secure, and scalable alternatives to the on-premise (on-prem) HPC via Cloud, Fog, and Edge computing. It has enabled recent state-of-the-art machine learning (ML) and artificial intelligence (AI)-based tools for drug discovery, such as BERT, BARD, AlphaFold2, and GPT. This chapter attempts to overview types of software architectures for developing scientific software or application with deployment agnostic (on-prem to cloud and hybrid) use cases. Furthermore, the chapter aims to outline how the innovation is disrupting the orthodox mindset of monolithic software running on on-prem HPC and provide the paradigm shift landscape to microservices driven application programming (API) and message parsing interface (MPI)-based scientific computing across the distributed, high-available infrastructure. This is coupled with agile DevOps, and good coding practices, low code and no-code application development frameworks for cost-efficient, secure, automated, and robust scientific application life cycle management.
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Affiliation(s)
- Bhushan Bonde
- Evotec (UK) Ltd., Dorothy Crowfoot Hodgkin Campus, Abingdon, Oxfordshire, UK.
- Digital Futures Institute, University of Suffolk, Ipswich, United Kingdom.
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21
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Barman S, Bardhan I, Padhan J, Sudhamalla B. Integrated virtual screening and MD simulation approaches toward discovering potential inhibitors for targeting BRPF1 bromodomain in hepatocellular carcinoma. J Mol Graph Model 2024; 126:108642. [PMID: 37797430 DOI: 10.1016/j.jmgm.2023.108642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/21/2023] [Accepted: 09/26/2023] [Indexed: 10/07/2023]
Abstract
Hepatocellular carcinoma (HCC) is one of the most aggressive and life-threatening cancers. Although multiple treatment options are available, the prognosis of HCC patients is poor due to metastasis and drug resistance. Hence, discovering novel targets is essential for better therapeutic development for HCC. In this study, we used the cancer genome atlas (TCGA) dataset to analyze the expression of bromodomain-containing proteins in HCC, as bromodomains are emerging attractive therapeutic targets. Our analysis identified BRPF1 as the most highly upregulated gene in HCC among the 43 bromodomain-containing genes. Upregulation of BRPF1 was significantly associated with poorer patient survival. Therefore, targeting BRPF1 may be an approach for HCC treatment. Previously, several potential inhibitors of BRPF1 bromodomain have been discovered. However, due to the limited clinical success of the current inhibitors, we aim to search for new inhibitors with high affinity and specificity for the BRPF1 bromodomain. In this study, we utilized high-throughput virtual screening methods to screen synthetic and natural compound databases against the BRPF1 bromodomain. In addition, we used machine learning-based QSAR modeling to predict the IC50 values of the selected BRPF1 bromodomain inhibitors. Extensive MD simulations were used to calculate the binding free energies of BRPF1 bromodomain and inhibitor complexes. Using this approach, we identified four lead scaffolds with a similar or better binding affinity towards the BRPF1 bromodomain than the previously reported inhibitors. Overall, this study discovered some promising compounds that have the potential to act as potent BRPF1 bromodomain inhibitors.
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Affiliation(s)
- Soumen Barman
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, 741246, Nadia, West Bengal, India
| | - Ishita Bardhan
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, 741246, Nadia, West Bengal, India
| | - Jyotirmayee Padhan
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, 741246, Nadia, West Bengal, India
| | - Babu Sudhamalla
- Department of Biological Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, 741246, Nadia, West Bengal, India.
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Ayipo YO, Ahmad I, Chong CF, Zainurin NA, Najib SY, Patel H, Mordi MN. Carbazole derivatives as promising competitive and allosteric inhibitors of human serotonin transporter: computational pharmacology. J Biomol Struct Dyn 2024; 42:993-1014. [PMID: 37021485 DOI: 10.1080/07391102.2023.2198016] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 03/25/2023] [Indexed: 04/07/2023]
Abstract
The human serotonin transporters (hSERTs) are neurotransmitter sodium symporters of the aminergic G protein-coupled receptors, regulating the synaptic serotonin and neuropharmacological processes related to neuropsychiatric disorders, notably, depression. Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine and (S)-citalopram are competitive inhibitors of hSERTs and are commonly the first-line medications for major depressive disorder (MDD). However, treatment-resistance and unpleasant aftereffects constitute their clinical drawbacks. Interestingly, vilazodone emerged with polypharmacological (competitive and allosteric) inhibitions on hSERTs, amenable to improved efficacy. However, its application usually warrants adjuvant/combination therapy, another subject of critical adverse events. Thus, the discovery of alternatives with polypharmacological potentials (one-drug-multiple-target) and improved safety remains essential. In this study, carbazole analogues from chemical libraries were explored using docking and molecular dynamics (MD) simulation. Selectively, two IBScreen ligands, STOCK3S-30866 and STOCK1N-37454 predictively bound to the active pockets and expanded boundaries (extracellular vestibules) of the hSERTs more potently than vilazodone and (S)-citalopram. For instance, the two ligands showed docking scores of -9.52 and -9.59 kcal/mol and MM-GBSA scores of -92.96 and -65.66 kcal/mol respectively compared to vilazodone's respective scores of -7.828 and -59.27 against the central active site of the hSERT (PDB 7LWD). Similarly, the two ligands also docked to the allosteric pocket (PDB 5I73) with scores of -8.15 and -8.40 kcal/mol and MM-GBSA of -96.14 and -68.46 kcal/mol whereas (S)-citalopram has -6.90 and -69.39 kcal/mol respectively. The ligands also conferred conformational stability on the receptors during 100 ns MD simulations and displayed interesting ADMET profiles, representing promising hSERT modulators for MDD upon experimental validation.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Yusuf Oloruntoyin Ayipo
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia
- Department of Chemistry and Industrial Chemistry, Kwara State University, Ilorin, Nigeria
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof Ravindra Nikam College of Pharmacy, Dhule, Maharashtra, India
- Department of Pharmaceutical Chemistry, R C Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra, India
| | - Chien Fung Chong
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia
- Department of Allied Health Sciences, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia
| | - Nurul Amira Zainurin
- Faculty of Agro-Based Industry, Universiti Malaysia Kelantan, Jeli, Kelantan, Malaysia
| | - Sani Yahaya Najib
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia
- Department of Pharmaceutical and Medicinal Chemistry, Bayero University, Kano, Nigeria
| | - Harun Patel
- Department of Pharmaceutical Chemistry, R C Patel Institute of Pharmaceutical Education and Research, Shirpur, Maharashtra, India
| | - Mohd Nizam Mordi
- Centre for Drug Research, Universiti Sains Malaysia, USM, Pulau Pinang, Malaysia
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Yasmeen N, Ahmad Chaudhary A, K Niraj RR, Lakhawat SS, Sharma PK, Kumar V. Screening of phytochemicals from Clerodendrum inerme (L.) Gaertn as potential anti-breast cancer compounds targeting EGFR: an in-silico approach. J Biomol Struct Dyn 2023:1-43. [PMID: 38141177 DOI: 10.1080/07391102.2023.2294379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 12/04/2023] [Indexed: 12/25/2023]
Abstract
Breast cancer (BC) is the most prevalent malignancy among women around the world. The epidermal growth factor receptor (EGFR) is a tyrosine kinase receptor (RTK) of the ErbB/HER family. It is essential for triggering the cellular signaling cascades that control cell growth and survival. However, perturbations in EGFR signaling lead to cancer development and progression. Hence, EGFR is regarded as a prominent therapeutic target for breast cancer. Therefore, in the current investigation, EGFR was targeted with phytochemicals from Clerodendrum inerme (L.) Gaertn (C. inerme). A total of 121 phytochemicals identified by gas chromatography-mass spectrometry (GC-MS) analysis were screened against EGFR through molecular docking, ADMET analyses (Absorption, Distribution, Metabolism, Excretion, and Toxicity), PASS predictions, and molecular dynamics simulation, which revealed three potential hit compounds with CIDs 10586 [i.e. alpha-bisabolol (-6.4 kcal/mol)], 550281 [i.e. 2,(4,4-Trimethyl-3-hydroxymethyl-5a-(3-methyl-but-2-enyl)-cyclohexene) (-6.5 kcal/mol)], and 161271 [i.e. salvigenin (-7.4 kcal/mol)]. The FDA-approved drug gefitinib was used to compare the inhibitory effects of the phytochemicals. The top selected compounds exhibited good ADMET properties and obeyed Lipinski's rule of five (ROF). The molecular docking analysis showed that salvigenin was the best among the three compounds and formed bonds with the key residue Met 793. Furthermore, the molecular mechanics generalized born surface area (MMGBSA) calculations, molecular dynamics simulation, and normal mode analysis validated the binding affinity of the compounds and also revealed the strong stability and compactness of phytochemicals at the docked site. Additionally, DFT and DOS analyses were done to study the reactivity of the compounds and to further validate the selected phytochemicals. These results suggest that the identified phytochemicals possess high inhibitory potential against the target EGFR and can treat breast cancer. However, further in vitro and in vivo investigations are warranted towards the development of these constituents into novel anti-cancer drugs.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Nusrath Yasmeen
- Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur, India
| | - Anis Ahmad Chaudhary
- Department of Biology, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | | | | | | | - Vikram Kumar
- Amity Institute of Biotechnology, Amity University Rajasthan, Jaipur, India
- Amity Institute of Pharmacy, Amity University Rajasthan, Jaipur, India
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24
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Niazi SK, Mariam Z. Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis. Pharmaceuticals (Basel) 2023; 17:22. [PMID: 38256856 PMCID: PMC10819513 DOI: 10.3390/ph17010022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.
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Affiliation(s)
| | - Zamara Mariam
- Centre for Health and Life Sciences, Coventry University, Coventry City CV1 5FB, UK
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25
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Alrouji M, Alhumaydhi FA, Alsayari A, Sharaf SE, Shafi S, Anwar S, Shahwan M, Atiya A, Shamsi A. Targeting Sirtuin 1 for therapeutic potential: Drug repurposing approach integrating docking and molecular dynamics simulations. PLoS One 2023; 18:e0293185. [PMID: 38117829 PMCID: PMC10732437 DOI: 10.1371/journal.pone.0293185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/08/2023] [Indexed: 12/22/2023] Open
Abstract
Identifying novel therapeutic agents is a fundamental challenge in contemporary drug development, especially in the context of complex diseases like cancer, neurodegenerative disorders, and metabolic syndromes. Here, we present a comprehensive computational study to identify potential inhibitors of SIRT1 (Sirtuin 1), a critical protein involved in various cellular processes and disease pathways. Leveraging the concept of drug repurposing, we employed a multifaceted approach that integrates molecular docking and molecular dynamics (MD) simulations to predict the binding affinities and dynamic behavior of a diverse set of FDA-approved drugs from DrugBank against the SIRT1. Initially, compounds were shortlisted based on their binding affinities and interaction analyses to identify safe and promising binding partners for SIRT1. Among these candidates, Doxercalciferol and Timiperone emerged as potential candidates, displaying notable affinity, efficiency, and specificity towards the binding pocket of SIRT1. Extensive evaluation revealed that these identified compounds boast a range of favorable biological properties and prefer binding to the active site of SIRT1. To delve deeper into the interactions, all-atom MD simulations were conducted for 500 nanoseconds (ns). These simulations assessed the conformational dynamics, stability, and interaction mechanism of the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes. The MD simulations illustrated that the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes maintain stability over a 500 ns trajectory. These insightful outcomes propose that Doxercalciferol and Timiperone hold promise as viable scaffolds for developing potential SIRT1 inhibitors, with implications for tackling complex diseases such as cancer, neurodegenerative disorders, and metabolic syndromes.
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Affiliation(s)
- Mohammed Alrouji
- Department of Medical Laboratories, College of Applied Medical Sciences, Shaqra University, Shaqra, Saudi Arabia
| | - Fahad A. Alhumaydhi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia
| | - Abdulrhman Alsayari
- Department of Pharmacognosy, College of Pharmacy, King Khalid University (KKU), Abha, Saudi Arabia
- Complementary and Alternative Medicine Unit, King Khalid University (KKU), Abha, Saudi Arabia
| | - Sharaf E. Sharaf
- Pharmaceutical Chemistry Department, College of Pharmacy Umm Al-Qura University Makkah, Makkah, Saudi Arabia
| | - Sheeba Shafi
- Department of Nursing, College of Applied Medical Sciences, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Saleha Anwar
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, India
| | - Moyad Shahwan
- Center for Medical and Bio-Allied Health Sciences, Ajman University, Ajman, UAE
| | - Akhtar Atiya
- Department of Pharmacognosy, College of Pharmacy, King Khalid University (KKU), Abha, Saudi Arabia
| | - Anas Shamsi
- Center for Medical and Bio-Allied Health Sciences, Ajman University, Ajman, UAE
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26
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Akili AWR, Hardianto A, Latip J, Permana A, Herlina T. Virtual Screening and ADMET Prediction to Uncover the Potency of Flavonoids from Genus Erythrina as Antibacterial Agent through Inhibition of Bacterial ATPase DNA Gyrase B. Molecules 2023; 28:8010. [PMID: 38138500 PMCID: PMC10745610 DOI: 10.3390/molecules28248010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/02/2023] [Accepted: 10/02/2023] [Indexed: 12/24/2023] Open
Abstract
The emergence of antimicrobial resistance due to the widespread and inappropriate use of antibiotics has now become the global health challenge. Flavonoids have long been reported to be a potent antimicrobial agent against a wide range of pathogenic microorganisms in vitro. Therefore, new antibiotics development based on flavonoid structures could be a potential strategy to fight against antibiotic-resistant infections. This research aims to screen the potency of flavonoids of the genus Erythrina as an inhibitor of bacterial ATPase DNA gyrase B. From the 378 flavonoids being screened, 49 flavonoids show potential as an inhibitor of ATPase DNA gyrase B due to their lower binding affinity compared to the inhibitor and ATP. Further screening for their toxicity, we identified 6 flavonoids from these 49 flavonoids, which are predicted to have low toxicity. Among these flavonoids, erystagallin B (334) is predicted to have the best pharmacokinetic properties, and therefore, could be further developed as new antibacterial agent.
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Affiliation(s)
- Abd. Wahid Rizaldi Akili
- Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor 45363, Indonesia; (A.W.R.A.); (A.H.); (A.P.)
| | - Ari Hardianto
- Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor 45363, Indonesia; (A.W.R.A.); (A.H.); (A.P.)
| | - Jalifah Latip
- Department of Chemical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 46300, Malaysia;
| | - Afri Permana
- Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor 45363, Indonesia; (A.W.R.A.); (A.H.); (A.P.)
| | - Tati Herlina
- Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Padjadjaran, Jatinangor 45363, Indonesia; (A.W.R.A.); (A.H.); (A.P.)
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27
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Venianakis T, Primikyri A, Opatz T, Petry S, Papamokos G, Gerothanassis IP. NMR and Docking Calculations Reveal Novel Atomistic Selectivity of a Synthetic High-Affinity Free Fatty Acid vs. Free Fatty Acids in Sudlow's Drug Binding Sites in Human Serum Albumin. Molecules 2023; 28:7991. [PMID: 38138481 PMCID: PMC10745614 DOI: 10.3390/molecules28247991] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 12/24/2023] Open
Abstract
Saturation transfer difference (STD), inter-ligand NOEs (INPHARMA NMR), and docking calculations are reported for investigating specific binding sites of the high-affinity synthetic 7-nitrobenz-2-oxa-1,3-diazoyl-4-C12 fatty acid (NBD-C12 FA) with non-labeled human serum albumin (HSA) and in competition with the drugs warfarin and ibuprofen. A limited number of negative interligand NOEs between NBD-C12 FA and warfarin were interpreted in terms of a short-range allosteric competitive binding in the wide Sudlow's binding site II (FA7) of NBD-C12 FA with Ser-202, Lys-199, and Trp-214 and warfarin with Arg-218 and Arg-222. In contrast, the significant number of interligand NOEs between NBD-C12 FA and ibuprofen were interpreted in terms of a competitive binding mode in Sudlow's binding site I (FA3 and FA4) with Ser-342, Arg-348, Arg-485, Arg-410, and Tyr-411. NBD-C12 FA has the unique structural properties, compared to short-, medium-, and long-chain saturated and unsaturated natural free fatty acids, of interacting with well-defined structures with amino acids of both the internal and external polar anchor sites in Sudlow's binding site I and with amino acids in both FA3 and FA4 in Sudlow's binding site II. The NBD-C12 FA, therefore, interacts with novel structural characteristics in the drug binding sites I and II and can be regarded as a prototype molecule for drug development.
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Affiliation(s)
- Themistoklis Venianakis
- Section of Organic Chemistry and Biochemistry, Department of Chemistry, University of Ioannina, GR-45110 Ioannina, Greece; (T.V.); (A.P.)
| | - Alexandra Primikyri
- Section of Organic Chemistry and Biochemistry, Department of Chemistry, University of Ioannina, GR-45110 Ioannina, Greece; (T.V.); (A.P.)
| | - Till Opatz
- Department of Chemistry, Johannes Gutenberg-University, Duesbergweg, 10–14, 55128 Mainz, Germany;
| | - Stefan Petry
- Sanofi-Aventis Deutschland GmbH, Integrated Drug Discovery, Industriepark Höchst, 65926 Frankfurt am Main, Germany;
| | - Georgios Papamokos
- Section of Organic Chemistry and Biochemistry, Department of Chemistry, University of Ioannina, GR-45110 Ioannina, Greece; (T.V.); (A.P.)
- Department of Physics, Harvard University, 17 Oxford Street, Cambridge, MA 02138, USA
| | - Ioannis P. Gerothanassis
- Section of Organic Chemistry and Biochemistry, Department of Chemistry, University of Ioannina, GR-45110 Ioannina, Greece; (T.V.); (A.P.)
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28
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Murray JD, Lange JJ, Bennett-Lenane H, Holm R, Kuentz M, O'Dwyer PJ, Griffin BT. Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation. Eur J Pharm Sci 2023; 191:106562. [PMID: 37562550 DOI: 10.1016/j.ejps.2023.106562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/09/2023] [Accepted: 08/07/2023] [Indexed: 08/12/2023]
Abstract
Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.
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Affiliation(s)
- Jack D Murray
- School of Pharmacy, University College Cork, Cork, Ireland
| | - Justus J Lange
- School of Pharmacy, University College Cork, Cork, Ireland; Roche Pharmaceutical Research & Early Development, Pre-Clinical CMC, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, Basel, Switzerland
| | | | - René Holm
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, Odense 5230, Denmark
| | - Martin Kuentz
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, Muttenz CH 4132, Switzerland
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29
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Emanuele E, Minoretti P. Measuring the Impact of Data Sharing: From Author-Level Metrics to Quantification of Economic and Non-tangible Benefits. Cureus 2023; 15:e50308. [PMID: 38205488 PMCID: PMC10777335 DOI: 10.7759/cureus.50308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/10/2023] [Indexed: 01/12/2024] Open
Abstract
In early 2023, the National Institutes of Health (NIH) implemented its Data Management and Sharing (DMS) Policy, requiring researchers to share scientific data produced with NIH funding. The policy's objective is to amplify the benefits of public investment in research by promoting the dissemination and reusability of primary data. Given this backdrop, identifying a robust methodology to assess the impact of data sharing across diverse research domains is essential. In this review, we adopted two methodological paradigms, the bottom-up and top-down strategies, and employed content analysis to pinpoint established methodologies and innovative practices within this intricate field. Although numerous author-level metrics are available to gauge the impact of data sharing, their application is still limited. Non-traditional metrics, encompassing economic (e.g., cost savings) and intangible benefits, presently appear to hold more potential for evaluating the impact of primary data sharing. Finally, we address the primary obstacles encountered by open data policies and introduce an innovative "Shared model for shared data" framework to bolster data sharing practices and refine evaluation metrics.
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30
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McGibbon M, Shave S, Dong J, Gao Y, Houston DR, Xie J, Yang Y, Schwaller P, Blay V. From intuition to AI: evolution of small molecule representations in drug discovery. Brief Bioinform 2023; 25:bbad422. [PMID: 38033290 PMCID: PMC10689004 DOI: 10.1093/bib/bbad422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/13/2023] [Accepted: 11/01/2023] [Indexed: 12/02/2023] Open
Abstract
Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify molecular starting points that will develop into safe and efficacious drugs while reducing costs, time and failure rates. To achieve this goal, it is crucial to represent molecules in a digital format that makes them machine-readable and facilitates the accurate prediction of properties that drive decision-making. Over the years, molecular representations have evolved from intuitive and human-readable formats to bespoke numerical descriptors and fingerprints, and now to learned representations that capture patterns and salient features across vast chemical spaces. Among these, sequence-based and graph-based representations of small molecules have become highly popular. However, each approach has strengths and weaknesses across dimensions such as generality, computational cost, inversibility for generative applications and interpretability, which can be critical in informing practitioners' decisions. As the drug discovery landscape evolves, opportunities for innovation continue to emerge. These include the creation of molecular representations for high-value, low-data regimes, the distillation of broader biological and chemical knowledge into novel learned representations and the modeling of up-and-coming therapeutic modalities.
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Affiliation(s)
- Miles McGibbon
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Steven Shave
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410013, China
| | - Yumiao Gao
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Douglas R Houston
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
| | - Jiancong Xie
- Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Yuedong Yang
- Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-Sen University, Guangzhou, 510000, China
| | - Philippe Schwaller
- Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vincent Blay
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, Scotland EH9 3BF, United Kingdom
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31
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Khairullina V, Martynova Y. Quantitative Structure-Activity Relationship in the Series of 5-Ethyluridine, N2-Guanine, and 6-Oxopurine Derivatives with Pronounced Anti-Herpetic Activity. Molecules 2023; 28:7715. [PMID: 38067446 PMCID: PMC10708366 DOI: 10.3390/molecules28237715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
A quantitative analysis of the relationship between the structure and inhibitory activity against the herpes simplex virus thymidine kinase (HSV-TK) was performed for the series of 5-ethyluridine, N2-guanine, and 6-oxopurines derivatives with pronounced anti-herpetic activity (IC50 = 0.09 ÷ 160,000 μmol/L) using the GUSAR 2019 software. On the basis of the MNA and QNA descriptors and whole-molecule descriptors using the self-consistent regression, 12 statistically significant consensus models for predicting numerical pIC50 values were constructed. These models demonstrated high predictive accuracy for the training and test sets. Molecular fragments of HSV-1 and HSV-2 TK inhibitors that enhance or diminish the anti-herpetic activity are considered. Virtual screening of the ChEMBL database using the developed QSAR models revealed 42 new effective HSV-1 and HSV-2 TK inhibitors. These compounds are promising for further research. The obtained data open up new opportunities for developing novel effective inhibitors of TK.
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Affiliation(s)
- Veronika Khairullina
- Institute of Chemistry and Defence in Emergency Situations, Ufa University of Science and Technology, 50076 Ufa, Russia;
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32
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Yu T, Nantasenamat C, Anuwongcharoen N, Piacham T. Machine Learning Approaches to Investigate the Structure-Activity Relationship of Angiotensin-Converting Enzyme Inhibitors. ACS OMEGA 2023; 8:43500-43510. [PMID: 38027387 PMCID: PMC10666249 DOI: 10.1021/acsomega.3c03225] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/25/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023]
Abstract
Angiotensin-converting enzyme inhibitors (ACEIs) play a crucial role in treating conditions such as hypertension, heart failure, and kidney diseases. Nevertheless, the ACEIs currently available on the market are linked to a variety of adverse effects including renal insufficiency, which restricts their usage. There is thus an urgent need to optimize the currently available ACEIs. This study represents a structure-activity relationship investigation of ACEIs, employing machine learning to analyze data sets sourced from the ChEMBL database. Exploratory data analysis was performed to visualize the physicochemical properties of compounds by investigating the distributions, patterns, and statistical significance among the different bioactivity groups. Further scaffold analysis has identified 9 representative Murcko scaffolds with frequencies ≥10. Scaffold diversity has revealed that active ACEIs had more scaffold diversity than their intermediate and inactive counterparts, thereby indicating the significance of performing lead optimization on scaffolds of active ACEIs. Scaffolds 1, 3, 6, and 8 are unfavorable in comparison with scaffolds 2, 3, 5, 7, and 9. QSAR investigation of compiled data sets consisting of 549 compounds led to the selection of Mordred descriptor and Random Forest algorithm as the best model, which afforded robust model performance (accuracy: 0.981, 0.77, and 0.745; MCC: 0.972, 0.658, and 0.617 for the training set, 10-fold cross-validation set, and testing set, respectively). To enhance the model's robustness and predictability, we reduced the chemical diversity of the input compounds by using the 9 most prevalent Murcko scaffold-matched compounds (comprising a total of 168) followed by a subsequent QSAR model investigation using Mordred descriptor and extremely gradient boost algorithm (accuracy: 0.973, 0.849, and 0.823; MCC: 0.959, 0.786, and 0.742 for the training set, 10-fold cross-validation set, and testing set, respectively). Further illustration of the structure-activity relationship using SALI plots has enabled the identification of clusters of compounds that create activity cliffs. These findings, as presented in this study, contribute to the advancement of drug discovery and the optimization of ACEIs.
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Affiliation(s)
- Tianshi Yu
- Center
of Data Mining and Biomedical informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Chanin Nantasenamat
- Streamlit
Open Source, Snowflake Inc., San Mateo, California 94402, United States
| | - Nuttapat Anuwongcharoen
- Center
of Data Mining and Biomedical informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Theeraphon Piacham
- Department
of Clinical Microbiology and Applied Technology, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
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33
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Raut B, Upadhyaya SR, Bashyal J, Parajuli N. In Silico and In Vitro Analyses to Repurpose Quercetin as a Human Pancreatic α-Amylase Inhibitor. ACS OMEGA 2023; 8:43617-43631. [PMID: 38027372 PMCID: PMC10666247 DOI: 10.1021/acsomega.3c05082] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/20/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023]
Abstract
Human pancreatic α-amylase (HPA), situated at the apex of the starch digestion hierarchy, is an attractive therapeutic approach to precisely regulate blood glucose levels, thereby efficiently managing diabetes. Polyphenols offer a natural and multifaceted approach to moderate postprandial sugar spikes, with their slight modulation in carbohydrate digestion and potential secondary benefits, such as antioxidant and anti-inflammatory effects. Taking into consideration the unfavorable side effects of currently available commercial medications, we aimed to study a library of polyphenols attributed to their remarkable antidiabetic properties and screened the most potent HPA inhibitor via a comprehensive in silico study encompassing molecular docking, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) calculation, molecular dynamics (MD) simulation, density functional theory (DFT) study, and pharmacokinetic properties followed by an in vitro assay. Significant hydrogen bonding with the catalytic triad residues of HPA, prominent MM/GBSA binding energy of -27.03 kcal/mol, and the stable nature of the protein-ligand complex with regard to 100 ns MD simulation screened quercetin as the best HPA inhibitor. Additionally, quercetin showed strong reactivity in the substrate-binding pocket of HPA and exhibited favorable pharmacokinetic properties with a considerable inhibitory concentration (IC50) of 57.37 ± 0.9 μg/mL against α-amylase. This study holds prospects for HPA inhibition and suggests quercetin as an approach to therapy for diabetes; however, it is imperative to conduct further research.
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Affiliation(s)
- Bimal
K. Raut
- Central Department of Chemistry, Tribhuvan University, Kirtipur 44600, Kathmandu, Nepal
| | - Siddha Raj Upadhyaya
- Central Department of Chemistry, Tribhuvan University, Kirtipur 44600, Kathmandu, Nepal
| | - Jyoti Bashyal
- Central Department of Chemistry, Tribhuvan University, Kirtipur 44600, Kathmandu, Nepal
| | - Niranjan Parajuli
- Central Department of Chemistry, Tribhuvan University, Kirtipur 44600, Kathmandu, Nepal
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34
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Devaraji V, Sivaraman J. Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation. J Biomol Struct Dyn 2023:1-22. [PMID: 37938122 DOI: 10.1080/07391102.2023.2275176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 10/20/2023] [Indexed: 11/09/2023]
Abstract
Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into effective antidiabetic designs. Therefore, in this study, we used machine learning-based small molecule design. We used various chemoinformatic and binary fingerprint techniques on small molecules to construct multiple models for alpha-amylase inhibitors. Among these models, the top models were used for ensemble-based machine learning predictions on libraries of organic molecules supplemented with synthetic scaffolds that could be used as antidiabetic agents. Further, involved identifying 10 promising molecules from computational studies and determining their inhibitory effects on alpha-amylase. These molecules were synthesised and thoroughly analysed to assess their biological inhibitory properties. Then, thermodynamic simulations were conducted to determine the stability and affinity of experimentally active molecules. The research results showcased the top 10 ML models recorded impressive statistics with an average model score of 0.8216, Pearson-r value of 0.827 and external validation yielding a Q2 value of 0.835, proving their reliability and accuracy. Ten derivatives of benzothiophene dioxolane was prime research focus due to computational predictions. The biological inhibitory assay of synthesised molecules showed that small molecules with ID ALC5 and ALC6 exhibited inhibitory efficiencies (IC50) of 2.1 ± 0.14 µM and 5.71 ± 0.02 µM against alpha-amylase enzyme, whereas other molecules showed moderate inhibition. In conclusion, the positive results of the experiment indicate that researchers should explore machine learning-driven design.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Vinod Devaraji
- Computational Drug Design Lab, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Jayanthi Sivaraman
- Computational Drug Design Lab, Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Guidotti IL, Neis A, Martinez DP, Seixas FK, Machado K, Kremer FS. Bambu and its applications in the discovery of active molecules against melanoma. J Mol Graph Model 2023; 124:108564. [PMID: 37453311 DOI: 10.1016/j.jmgm.2023.108564] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/14/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE OR OBJECTIVE Melanoma is one of the most dangerous forms of skin cancer and the discovery of novel drugs is an ongoing effort. Quantitative Structure Activity Relationship (QSAR) is a computational method that allows the estimation of the properties of a molecule, including its biological activity. QSAR models have been widely employed in the search for potential drug candidates, but also for agrochemicals and other molecules with applications in different branches of the industry. Here we present Bambu, a simple command line tool to generate QSAR models from high-throughput screening bioassays datasets. METHODS The tool was developed using the Python programming language and relies mainly on RDKit for molecule data manipulation, FLAML for automated machine learning and the PubChem REST API for data retrieval. As a proof-of-concept we have employed the tool to generate QSAR models for melanoma cell growth inhibition based on HTS data and used them to screen libraries of FDA-approved drugs and natural compounds. Additionally, Bambu was compared to QSAR-Co, another automated tool for QSAR model generation. RESULTS based on the developed tool we were able to produce QSAR models and identify a wide variety of molecules with potential melanoma cell growth inhibitors, many of which with anti-tumoral activity already described. The QSAR models are available through the URL http://caramel.ufpel.edu.br, and all data and code used to generate its models are available at Zenodo (https://doi.org/10.5281/zenodo.7495214). Bambu source code is available at GitHub (https://github.com/omixlab/bambu-v2). In the benchmark, Bambu was able to produce models with higher accuracy, recall, F1 and ROC AUC when compared to QSAR-Co for the selected datasets. CONCLUSIONS Bambu is an free and open source tool which facilitates the creation of QSAR models and can be futurely applied in a wide variety of drug discovery projects.
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Affiliation(s)
- Isadora Leitzke Guidotti
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Alessandra Neis
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Daniela Peres Martinez
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Fabiana Kömmling Seixas
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Karina Machado
- Centro de Ciências Computacionais, Universidade Federal do Rio Grande, Rio Grande, Rio Grande do Sul, Brazil
| | - Frederico Schmitt Kremer
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil.
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Bhuia MS, Rokonuzzman M, Hossain MI, Ansari SA, Ansari IA, Islam T, Al Hasan MS, Mubarak MS, Islam MT. Anxiolytic- like Effects by trans-Ferulic Acid Possibly Occur through GABAergic Interaction Pathways. Pharmaceuticals (Basel) 2023; 16:1271. [PMID: 37765079 PMCID: PMC10535412 DOI: 10.3390/ph16091271] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
Numerous previous studies reported that ferulic acid exerts anxiolytic activity. However, the mechanisms have yet to be elucidated. The current study aimed to investigate the anxiolytic effect of trans-ferulic acid (TFA), a stereoisomer of ferulic acid, and evaluated its underlying mechanism using in vivo and computational studies. For this, different experimental doses of TFA (25, 50, and 75 mg/kg) were administered orally to Swiss albino mice, and various behavioral methods of open field, hole board, swing box, and light-dark tests were carried out. Diazepam (DZP), a positive allosteric modulator of the GABAA receptor, was employed as a positive control at a dose of 2 mg/kg, and distilled water served as a vehicle. Additionally, molecular docking was performed to estimate the binding affinities of the TFA and DZP toward the GABAA receptor subunits of α2 and α3, which are associated with the anxiolytic effect; visualizations of the ligand-receptor interaction were carried out using various computational tools. Our findings indicate that TFA dose-dependently reduces the locomotor activity of the animals in comparison with the controls, calming their behaviors. In addition, TFA exerted the highest binding affinity (-5.8 kcal/mol) to the α2 subunit of the GABAA receptor by forming several hydrogen and hydrophobic bonds. Taken together, our findings suggest that TFA exerts a similar effect to DZP, and the compound exerts moderate anxiolytic activity through the GABAergic interaction pathway. We suggest further clinical studies to develop TFA as a reliable anxiolytic agent.
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Affiliation(s)
- Md. Shimul Bhuia
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.S.B.); (M.R.); (M.I.H.); (T.I.); (M.S.A.H.)
| | - Md. Rokonuzzman
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.S.B.); (M.R.); (M.I.H.); (T.I.); (M.S.A.H.)
| | - Md. Imran Hossain
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.S.B.); (M.R.); (M.I.H.); (T.I.); (M.S.A.H.)
| | - Siddique Akber Ansari
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh 11451, Saudi Arabia;
| | - Irfan Aamer Ansari
- Department of Drug Science and Technology, University of Turin, 10124 Turin, Italy;
| | - Tawhida Islam
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.S.B.); (M.R.); (M.I.H.); (T.I.); (M.S.A.H.)
| | - Md. Sakib Al Hasan
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.S.B.); (M.R.); (M.I.H.); (T.I.); (M.S.A.H.)
| | | | - Muhammad Torequl Islam
- Department of Pharmacy, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh; (M.S.B.); (M.R.); (M.I.H.); (T.I.); (M.S.A.H.)
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Kleandrova VV, Ds Cordeiro MN, Speck-Planche A. Current in silico methods for multi-target drug discovery in early anticancer research: the rise of the perturbation-theory machine learning approach. Future Med Chem 2023; 15:1647-1650. [PMID: 37728008 DOI: 10.4155/fmc-2023-0241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023] Open
Affiliation(s)
- Valeria V Kleandrova
- LAQV@REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, Porto, 4169-007, Portugal
| | - Maria Natália Ds Cordeiro
- LAQV@REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, Porto, 4169-007, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry & Biochemistry, University of Porto, Porto, 4169-007, Portugal
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Bhowmik R, Kant R, Manaithiya A, Saluja D, Vyas B, Nath R, Qureshi KA, Parkkila S, Aspatwar A. Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study. Front Pharmacol 2023; 14:1265573. [PMID: 37705534 PMCID: PMC10495588 DOI: 10.3389/fphar.2023.1265573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 08/10/2023] [Indexed: 09/15/2023] Open
Abstract
Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.
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Affiliation(s)
- Ratul Bhowmik
- Medicinal Chemistry and Molecular Modelling Lab, Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
| | - Ravi Kant
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research, Delhi School of Public Health, IoE, University of Delhi, Delhi, India
| | - Ajay Manaithiya
- Medicinal Chemistry and Molecular Modelling Lab, Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
| | - Daman Saluja
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research, Delhi School of Public Health, IoE, University of Delhi, Delhi, India
| | - Bharti Vyas
- Department of Bioinformatics, School of Interdisciplinary Studies, Jamia Hamdard, New Delhi, India
| | - Ranajit Nath
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India
| | - Kamal A. Qureshi
- Department of Pharmaceutics, Unaizah College of Pharmacy, Qassim University, Unaizah, Al-Qassim, Saudi Arabia
| | - Seppo Parkkila
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Fimlab Ltd., Tampere University Hospital, Tampere, Finland
| | - Ashok Aspatwar
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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Sokouti B, Hamzeh-Mivehroud M. 6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling. BMC Chem 2023; 17:63. [PMID: 37349775 DOI: 10.1186/s13065-023-00970-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/30/2023] [Indexed: 06/24/2023] Open
Abstract
The application of QSAR analysis dates back a half-century ago and is currently continuously employed in any rational drug design. The multi-dimensional QSAR modeling can be a promising tool for researchers to develop reliable predictive QSAR models for designing novel compounds. In the present work, we studied inhibitors of human aldose reductase (AR) to generate multi-dimensional QSAR models using 3D- and 6D-QSAR methods. For this purpose, Pentacle and Quasar's programs were used to produce the QSAR models using corresponding dissociation constant (Kd) values. By inspecting the performance metrics of the generated models, we achieved similar results with comparable internal validation statistics. However, considering the externally validated values, 6D-QSAR models provide significantly better prediction of endpoint values. The obtained results suggest that the higher the dimension of the QSAR model, the higher the performance of the generated model. However, more studies are required to verify these outcomes.
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Affiliation(s)
- Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Maryam Hamzeh-Mivehroud
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
- School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran.
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40
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Azad I, Khan T, Ahmad N, Khan AR, Akhter Y. Updates on drug designing approach through computational strategies: a review. Future Sci OA 2023; 9:FSO862. [PMID: 37180609 PMCID: PMC10167725 DOI: 10.2144/fsoa-2022-0085] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.
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Affiliation(s)
- Iqbal Azad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Tahmeena Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Naseem Ahmad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Abdul Rahman Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Yusuf Akhter
- Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, UP, 2260025, India
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41
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Fan YJ, Allen JE, McLoughlin KS, Shi D, Bennion BJ, Zhang X, Lightstone FC. Evaluating point-prediction uncertainties in neural networks for protein-ligand binding prediction. ARTIFICIAL INTELLIGENCE CHEMISTRY 2023; 1:100004. [PMID: 37583465 PMCID: PMC10426331 DOI: 10.1016/j.aichem.2023.100004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
Neural Network (NN) models provide potential to speed up the drug discovery process and reduce its failure rates. The success of NN models requires uncertainty quantification (UQ) as drug discovery explores chemical space beyond the training data distribution. Standard NN models do not provide uncertainty information. Some methods require changing the NN architecture or training procedure, limiting the selection of NN models. Moreover, predictive uncertainty can come from different sources. It is important to have the ability to separately model different types of predictive uncertainty, as the model can take assorted actions depending on the source of uncertainty. In this paper, we examine UQ methods that estimate different sources of predictive uncertainty for NN models aiming at protein-ligand binding prediction. We use our prior knowledge on chemical compounds to design the experiments. By utilizing a visualization method we create non-overlapping and chemically diverse partitions from a collection of chemical compounds. These partitions are used as training and test set splits to explore NN model uncertainty. We demonstrate how the uncertainties estimated by the selected methods describe different sources of uncertainty under different partitions and featurization schemes and the relationship to prediction error.
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Affiliation(s)
- Ya Ju Fan
- Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA, USA
| | - Jonathan E. Allen
- Biological Science and Security Center, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Kevin S. McLoughlin
- Biological Science and Security Center, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Da Shi
- Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Brian J. Bennion
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Xiaohua Zhang
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Felice C. Lightstone
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA
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42
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Taylor MG, Burrill DJ, Janssen J, Batista ER, Perez D, Yang P. Architector for high-throughput cross-periodic table 3D complex building. Nat Commun 2023; 14:2786. [PMID: 37188661 PMCID: PMC10185541 DOI: 10.1038/s41467-023-38169-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
Rare-earth and actinide complexes are critical for a wealth of clean-energy applications. Three-dimensional (3D) structural generation and prediction for these organometallic systems remains a challenge, limiting opportunities for computational chemical discovery. Here, we introduce Architector, a high-throughput in-silico synthesis code for s-, p-, d-, and f-block mononuclear organometallic complexes capable of capturing nearly the full diversity of the known experimental chemical space. Beyond known chemical space, Architector performs in-silico design of new complexes including any chemically accessible metal-ligand combinations. Architector leverages metal-center symmetry, interatomic force fields, and tight binding methods to build many possible 3D conformers from minimal 2D inputs including metal oxidation and spin state. Over a set of more than 6,000 x-ray diffraction (XRD)-determined complexes spanning the periodic table, we demonstrate quantitative agreement between Architector-predicted and experimentally observed structures. Further, we demonstrate out-of-the box conformer generation and energetic rankings of non-minimum energy conformers produced from Architector, which are critical for exploring potential energy surfaces and training force fields. Overall, Architector represents a transformative step towards cross-periodic table computational design of metal complex chemistry.
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Affiliation(s)
- Michael G Taylor
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Daniel J Burrill
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Jan Janssen
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Enrique R Batista
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
| | - Danny Perez
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
| | - Ping Yang
- Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
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Current trends in natural products for the treatment and management of dementia: Computational to clinical studies. Neurosci Biobehav Rev 2023; 147:105106. [PMID: 36828163 DOI: 10.1016/j.neubiorev.2023.105106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 02/17/2023] [Accepted: 02/18/2023] [Indexed: 02/24/2023]
Abstract
The number of preclinical and clinical studies evaluating natural products-based management of dementia has gradually increased, with an exponential rise in 2020 and 2021. Keeping this in mind, we examined current trends from 2016 to 2021 in order to assess the growth potential of natural products in the treatment of dementia. Publicly available literature was collected from various databases like PubMed and Google Scholar. Oxidative stress-related targets, NF-κB pathway, anti-tau aggregation, anti-AChE, and A-β aggregation were found to be common targets and pathways. A retrospective analysis of 33 antidementia natural compounds identified 125 sustainable resources distributed among 65 families, 39 orders, and 7 classes. We found that families such as Berberidaceae, Zingiberaceae, and Fabaceae, as well as orders such as Lamiales, Sapindales, and Myrtales, appear to be important and should be researched further for antidementia compounds. Moreover, some natural products, such as quercetin, curcumin, icariside II, berberine, and resveratrol, have a wide range of applications. Clinical studies and patents support the importance of dietary supplements and natural products, which we will also discuss. Finally, we conclude with the broad scope, future challenges, and opportunities for field researchers.
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Multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis of the synergistic effects between natural compounds baicalein and cubebin for the inhibition of the main protease of SARS-CoV-2. J Mol Liq 2023; 374:121253. [PMID: 36694691 PMCID: PMC9854241 DOI: 10.1016/j.molliq.2023.121253] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 12/16/2022] [Accepted: 01/10/2023] [Indexed: 01/18/2023]
Abstract
Combination drugs have been used for several diseases for many years since they produce better therapeutic effects. However, it is still a challenge to discover candidates to form a combination drug. This study aimed to investigate whether using a comprehensive in silico approach to identify novel combination drugs from a Chinese herbal formula is an appropriate and creative strategy. We, therefore, used Toujie Quwen Granules for the main protease (Mpro) of SARS-CoV-2 as an example. We first used molecular docking to identify molecular components of the formula which may inhibit Mpro. Baicalein (HQA004) is the most favorable inhibitory ligand. We also identified a ligand from the other component, cubebin (CHA008), which may act to support the proposed HQA004 inhibitor. Molecular dynamics simulations were then performed to further elucidate the possible mechanism of inhibition by HQA004 and synergistic bioactivity conferred by CHA008. HQA004 bound strongly at the active site and that CHA008 enhanced the contacts between HQA004 and Mpro. However, CHA008 also dynamically interacted at multiple sites, and continued to enhance the stability of HQA004 despite diffusion to a distant site. We proposed that HQA004 acted as a possible inhibitor, and CHA008 served to enhance its effects via allosteric effects at two sites. Additionally, our novel wavelet analysis showed that as a result of CHA008 binding, the dynamics and structure of Mpro were observed to have more subtle changes, demonstrating that the inter-residue contacts within Mpro were disrupted by the synergistic ligand. This work highlighted the molecular mechanism of synergistic effects between different herbs as a result of allosteric crosstalk between two ligands at a protein target, as well as revealed that using the multi-ligand molecular docking, simulation, free energy calculations and wavelet analysis to discover novel combination drugs from a Chinese herbal remedy is an innovative pathway.
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Key Words
- ADME/T, absorption, distribution, metabolism, excretion and toxicity
- COVID-19
- COVID-19, Coronavirus disease 2019
- Combination drug therapy
- Computer simulation
- Computers molecular
- H-bonds, hydrogen bonds
- LD50, median lethal dose
- MD, molecular dynamics
- MM-PBSA, molecular mechanics Poisson Boltzmann surface area
- Mpro, main protease
- Natural products
- PAINS, Pan-assay interference compounds
- RCO, inter-residue contact order
- RMSF, root-mean-square-fluctuation
- SARS-CoV-2, severe acute respiratory syndrome coronavirus 2
- SMILES, Simplified Molecular-input Line-entry System
- TCMSP, traditional Chinese medicine systems pharmacology database and analysis platform
- TQG, Toujie Quwen Granule
- Virus diseases
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Yu T, Nantasenamat C, Kachenton S, Anuwongcharoen N, Piacham T. Cheminformatic Analysis and Machine Learning Modeling to Investigate Androgen Receptor Antagonists to Combat Prostate Cancer. ACS OMEGA 2023; 8:6729-6742. [PMID: 36844574 PMCID: PMC9948163 DOI: 10.1021/acsomega.2c07346] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 02/01/2023] [Indexed: 06/18/2023]
Abstract
Prostate cancer (PCa) is a major leading cause of mortality of cancer among males. There have been numerous studies to develop antagonists against androgen receptor (AR), a crucial therapeutic target for PCa. This study is a systematic cheminformatic analysis and machine learning modeling to study the chemical space, scaffolds, structure-activity relationship, and landscape of human AR antagonists. There are 1678 molecules as final data sets. Chemical space visualization by physicochemical property visualization has demonstrated that molecules from the potent/active class generally have a mildly smaller molecular weight (MW), octanol-water partition coefficient (log P), number of hydrogen-bond acceptors (nHA), number of rotatable bonds (nRot), and topological polar surface area (TPSA) than molecules from intermediate/inactive class. The chemical space visualization in the principal component analysis (PCA) plot shows significant overlapping distributions between potent/active class molecules and intermediate/inactive class molecules; potent/active class molecules are intensively distributed, while intermediate/inactive class molecules are widely and sparsely distributed. Murcko scaffold analysis has shown low scaffold diversity in general, and scaffold diversity of potent/active class molecules is even lower than intermediate/inactive class molecules, indicating the necessity for developing molecules with novel scaffolds. Furthermore, scaffold visualization has identified 16 representative Murcko scaffolds. Among them, scaffolds 1, 2, 3, 4, 7, 8, 10, 11, 15, and 16 are highly favorable scaffolds due to their high scaffold enrichment factor values. Based on scaffold analysis, their local structure-activity relationships (SARs) were investigated and summarized. In addition, the global SAR landscape was explored by quantitative structure-activity relationship (QSAR) modelings and structure-activity landscape visualization. A QSAR classification model incorporating all of the 1678 molecules stands out as the best model from a total of 12 candidate models for AR antagonists (built on PubChem fingerprint, extra trees algorithm, accuracy for training set: 0.935, 10-fold cross-validation set: 0.735 and test set: 0.756). Deeper insights into the structure-activity landscape highlighted a total of seven significant activity cliff (AC) generators (ChEMBL molecule IDs: 160257, 418198, 4082265, 348918, 390728, 4080698, and 6530), which provide valuable SAR information for medicinal chemistry. The findings in this study provide new insights and guidelines for hit identification and lead optimization for the development of novel AR antagonists.
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Affiliation(s)
- Tianshi Yu
- Center
of Data Mining and Biomedical informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Chanin Nantasenamat
- Streamlit
Open Source, Snowflake Inc., San Mateo, California 94402, United States
| | - Supicha Kachenton
- Department
of Clinical Microbiology and Applied Technology, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
| | - Nuttapat Anuwongcharoen
- Center
of Data Mining and Biomedical informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Theeraphon Piacham
- Department
of Clinical Microbiology and Applied Technology, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
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Exploring the Chemical Space of CYP17A1 Inhibitors Using Cheminformatics and Machine Learning. Molecules 2023; 28:molecules28041679. [PMID: 36838665 PMCID: PMC9966999 DOI: 10.3390/molecules28041679] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/01/2023] [Accepted: 01/12/2023] [Indexed: 02/12/2023] Open
Abstract
Cytochrome P450 17A1 (CYP17A1) is one of the key enzymes in steroidogenesis that produces dehydroepiandrosterone (DHEA) from cholesterol. Abnormal DHEA production may lead to the progression of severe diseases, such as prostatic and breast cancers. Thus, CYP17A1 is a druggable target for anti-cancer molecule development. In this study, cheminformatic analyses and quantitative structure-activity relationship (QSAR) modeling were applied on a set of 962 CYP17A1 inhibitors (i.e., consisting of 279 steroidal and 683 nonsteroidal inhibitors) compiled from the ChEMBL database. For steroidal inhibitors, a QSAR classification model built using the PubChem fingerprint along with the extra trees algorithm achieved the best performance, reflected by the accuracy values of 0.933, 0.818, and 0.833 for the training, cross-validation, and test sets, respectively. For nonsteroidal inhibitors, a systematic cheminformatic analysis was applied for exploring the chemical space, Murcko scaffolds, and structure-activity relationships (SARs) for visualizing distributions, patterns, and representative scaffolds for drug discoveries. Furthermore, seven total QSAR classification models were established based on the nonsteroidal scaffolds, and two activity cliff (AC) generators were identified. The best performing model out of these seven was model VIII, which is built upon the PubChem fingerprint along with the random forest algorithm. It achieved a robust accuracy across the training set, the cross-validation set, and the test set, i.e., 0.96, 0.92, and 0.913, respectively. It is anticipated that the results presented herein would be instrumental for further CYP17A1 inhibitor drug discovery efforts.
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Insight into potent TLR2 inhibitors for the treatment of disease caused by Mycoplasma pneumoniae based on machine learning approaches. Mol Divers 2023; 27:371-387. [PMID: 35488091 DOI: 10.1007/s11030-022-10433-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 04/01/2022] [Indexed: 02/08/2023]
Abstract
Mycoplasma pneumoniae (MP) is one of the most common pathogens that causes acute respiratory tract infections. Children experiencing MP infection often suffer severe complications, lung injury, and even death. Previous studies have demonstrated that Toll-like receptor 2 (TLR2) is a potential therapeutic target for treating the MP-induced inflammatory response. However, the screening of natural compounds has received more attention for the treatment of bacterial infections to reduce the likelihood of bacterial resistance. Herein, we screened compounds by combining molecular docking and machine learning approaches to find potential lead compounds for treating MP infection. First, all compounds were docked with the TLR2 receptor protein to screen for potential candidates. To predict drug bioactivity, a machine learning model (random forest) was trained for TLR2 inhibitors to obtain the predictive model. The model achieved significant squared correlation coefficient (R2) values for the training set (0.85) and validation set (0.84) of compounds. The developed machine learning model was then used to predict the pIC50 values of the top 50 candidates from the Traditional Chinese compounds and Discovery Diversity sets of compounds. As a result, these compounds are capable of inhibiting the inflammatory response induced by MP. However, prior to bringing these compounds to market, it is necessary to verify these results with additional biological testing, including preclinical and clinical studies. Moreover, the present study provides a theoretical basis for the use of natural compounds as potential candidates to treat pneumonia caused by MP.
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Torab-Miandoab A, Samad-Soltani T, Jodati A, Rezaei-Hachesu P. Interoperability of heterogeneous health information systems: a systematic literature review. BMC Med Inform Decis Mak 2023; 23:18. [PMID: 36694161 PMCID: PMC9875417 DOI: 10.1186/s12911-023-02115-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 01/18/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms among the various health information systems. The aim of this review was to investigate the interoperability requirements for heterogeneous health information systems and to summarize and present them. METHODS In accordance with the PRISMA guideline, a broad electronic search of all literature was conducted on the topic through six databases, including PubMed, Web of science, Scopus, MEDLINE, Cochrane Library and Embase to 25 July 2022. The inclusion criteria were to select English-written articles available in full text with the closest objectives. 36 articles were selected for further analysis. RESULTS Interoperability has been raised in the field of health information systems from 2003 and now it is one of the topics of interest to researchers. The projects done in this field are mostly in the national scope and to achieve the electronic health record. HL7 FHIR, CDA, HIPAA and SNOMED-CT, SOA, RIM, XML, API, JAVA and SQL are among the most important requirements for implementing interoperability. In order to guarantee the concept of data exchange, semantic interaction is the best choice because the systems can recognize and process semantically similar information homogeneously. CONCLUSIONS The health industry has become more complex and has new needs. Interoperability meets this needs by communicating between the output and input of processor systems and making easier to access the data in the required formats.
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Affiliation(s)
- Amir Torab-Miandoab
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Taha Samad-Soltani
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
| | - Ahmadreza Jodati
- grid.412888.f0000 0001 2174 8913Cardiovascular Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Peyman Rezaei-Hachesu
- grid.412888.f0000 0001 2174 8913Department of Health Information Technology, Faculty of Management and Medical Informatics, Tabriz University of Medical Sciences, Golghast St., Tabriz, 5166614711 Iran
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Belfield SJ, Cronin MTD, Enoch SJ, Firman JW. Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs). PLoS One 2023; 18:e0282924. [PMID: 37163504 PMCID: PMC10171609 DOI: 10.1371/journal.pone.0282924] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/26/2023] [Indexed: 05/12/2023] Open
Abstract
Recent years have seen a substantial growth in the adoption of machine learning approaches for the purposes of quantitative structure-activity relationship (QSAR) development. Such a trend has coincided with desire to see a shifting in the focus of methodology employed within chemical safety assessment: away from traditional reliance upon animal-intensive in vivo protocols, and towards increased application of in silico (or computational) predictive toxicology. With QSAR central amongst techniques applied in this area, the emergence of algorithms trained through machine learning with the objective of toxicity estimation has, quite naturally, arisen. On account of the pattern-recognition capabilities of the underlying methods, the statistical power of the ensuing models is potentially considerable-appropriate for the handling even of vast, heterogeneous datasets. However, such potency comes at a price: this manifesting as the general practical deficits observed with respect to the reproducibility, interpretability and generalisability of the resulting tools. Unsurprisingly, these elements have served to hinder broader uptake (most notably within a regulatory setting). Areas of uncertainty liable to accompany (and hence detract from applicability of) toxicological QSAR have previously been highlighted, accompanied by the forwarding of suggestions for "best practice" aimed at mitigation of their influence. However, the scope of such exercises has remained limited to "classical" QSAR-that conducted through use of linear regression and related techniques, with the adoption of comparatively few features or descriptors. Accordingly, the intention of this study has been to extend the remit of best practice guidance, so as to address concerns specific to employment of machine learning within the field. In doing so, the impact of strategies aimed at enhancing the transparency (feature importance, feature reduction), generalisability (cross-validation) and predictive power (hyperparameter optimisation) of algorithms, trained upon real toxicity data through six common learning approaches, is evaluated.
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Affiliation(s)
- Samuel J Belfield
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
| | - Steven J Enoch
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
| | - James W Firman
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
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50
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Kar RK. Benefits of hybrid QM/MM over traditional classical mechanics in pharmaceutical systems. Drug Discov Today 2023; 28:103374. [PMID: 36174967 DOI: 10.1016/j.drudis.2022.103374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 06/27/2022] [Accepted: 09/22/2022] [Indexed: 02/02/2023]
Abstract
Hybrid quantum mechanics/molecular mechanics (QM/MM) is one of the most reliable approaches for accurately modeling and studying the complex pharmaceutical discovery system. Classical mechanics has significantly accelerated the drug discovery process in the past decade. However, the current challenge is the large pool of false positives, which require extensive validation. Hybrid QM/MM is an effective solution for accurately studying ligand binding, structural mechanisms, free energy evaluation, and spectroscopic characterization. This article highlights the methodological details relevant to cost-effective hybrid QM/MM methods. This approach, combined with traditional pharmacoinformatics methods, could be a reliable strategy to balance the cost and accuracy of the calculations.
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Affiliation(s)
- Rajiv K Kar
- Jyoti and Bhupat Mehta School of Health Sciences and Technology, Indian Institute of Technology Guwahati, Guwahati, Assam 781039, India.
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