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Carpenter KA, Altman RB. Databases of ligand-binding pockets and protein-ligand interactions. Comput Struct Biotechnol J 2024; 23:1320-1338. [PMID: 38585646 PMCID: PMC10997877 DOI: 10.1016/j.csbj.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 04/09/2024] Open
Abstract
Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.
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Affiliation(s)
- Kristy A. Carpenter
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Russ B. Altman
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
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2
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Moreira-Filho JT, Ranganath D, Conway M, Schmitt C, Kleinstreuer N, Mansouri K. Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow. J Cheminform 2024; 16:101. [PMID: 39152469 PMCID: PMC11330086 DOI: 10.1186/s13321-024-00894-1] [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: 03/28/2024] [Accepted: 08/06/2024] [Indexed: 08/19/2024] Open
Abstract
With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of processes such as molecular descriptor calculation, feature selection, dimensionality reduction, hyperparameter search, and supervised and unsupervised machine learning methods, enabling effective chemical grouping and visualization of results. Furthermore, we implemented tools for interpretation, identifying key molecular descriptors for the chemical groups, and using natural language summaries to clarify the rationale behind these groupings. The workflow was designed to run seamlessly in both the KNIME local desktop version and KNIME Server WebPortal as a web application. It incorporates interactive interfaces and guides to assist users in a step-by-step manner. We demonstrate the utility of this workflow through a case study using an eye irritation and corrosion dataset.Scientific contributionsThis work presents a novel, comprehensive chemical grouping workflow in KNIME, enhancing accessibility by integrating a user-friendly graphical interface that eliminates the need for extensive programming skills. This workflow uniquely combines several features such as automated molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning algorithms (both supervised and unsupervised), with hyperparameter optimization to refine chemical grouping accuracy. Moreover, we have introduced an innovative interpretative step and natural language summaries to elucidate the underlying reasons for chemical groupings, significantly advancing the usability of the tool and interpretability of the results.
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Affiliation(s)
- José T Moreira-Filho
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
| | - Dhruv Ranganath
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mike Conway
- National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Charles Schmitt
- Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Kamel Mansouri
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
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3
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Rezić I, Somogyi Škoc M. Computational Methodologies in Synthesis, Preparation and Application of Antimicrobial Polymers, Biomolecules, and Nanocomposites. Polymers (Basel) 2024; 16:2320. [PMID: 39204538 PMCID: PMC11359845 DOI: 10.3390/polym16162320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 08/05/2024] [Accepted: 08/14/2024] [Indexed: 09/04/2024] Open
Abstract
The design and optimization of antimicrobial materials (polymers, biomolecules, or nanocomposites) can be significantly advanced by computational methodologies like molecular dynamics (MD), which provide insights into the interactions and stability of the antimicrobial agents within the polymer matrix, and machine learning (ML) or design of experiment (DOE), which predicts and optimizes antimicrobial efficacy and material properties. These innovations not only enhance the efficiency of developing antimicrobial polymers but also enable the creation of materials with tailored properties to meet specific application needs, ensuring safety and longevity in their usage. Therefore, this paper will present the computational methodologies employed in the synthesis and application of antimicrobial polymers, biomolecules, and nanocomposites. By leveraging advanced computational techniques such as MD, ML, or DOE, significant advancements in the design and optimization of antimicrobial materials are achieved. A comprehensive review on recent progress, together with highlights of the most relevant methodologies' contributions to state-of-the-art materials science will be discussed, as well as future directions in the field will be foreseen. Finally, future possibilities and opportunities will be derived from the current state-of-the-art methodologies, providing perspectives on the potential evolution of polymer science and engineering of novel materials.
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Affiliation(s)
- Iva Rezić
- Department of Applied Chemistry, Faculty of Textile Technology, University of Zagreb, 10000 Zagreb, Croatia
| | - Maja Somogyi Škoc
- Department of Materials Testing, Faculty of Textile Technology, University of Zagreb, 10000 Zagreb, Croatia;
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Kong MM, Wei T, Liu B, Xi ZX, Ding JT, Liu X, Li K, Qin TL, Qian ZY, Wu WC, Wu JZ, Li WL. Discovery of novel ULK1 inhibitors through machine learning-guided virtual screening and biological evaluation. Future Med Chem 2024:1-17. [PMID: 39145469 DOI: 10.1080/17568919.2024.2385288] [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: 11/02/2023] [Accepted: 07/16/2024] [Indexed: 08/16/2024] Open
Abstract
Aim: Build a virtual screening model for ULK1 inhibitors based on artificial intelligence. Materials & methods: Build machine learning and deep learning classification models and combine molecular docking and biological evaluation to screen ULK1 inhibitors from 13 million compounds. And molecular dynamics was used to explore the binding mechanism of active compounds. Results & conclusion: Possibly due to less available training data, machine learning models significantly outperform deep learning models. Among them, the Naive Bayes model has the best performance. Through virtual screening, we obtained three inhibitors with IC50 of μM level and they all bind well to ULK1. This study provides an efficient virtual screening model and three promising compounds for the study of ULK1 inhibitors.
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Affiliation(s)
- Miao-Miao Kong
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision & Brain Health), Wenzhou, Zhejiang, 325000, China
| | - Tao Wei
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Bo Liu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, SAR, China
| | - Zi-Xuan Xi
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Jun-Tao Ding
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Xin Liu
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Ke Li
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Tian-Li Qin
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Zhen-Yong Qian
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Wen-Can Wu
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Jian-Zhang Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision & Brain Health), Wenzhou, Zhejiang, 325000, China
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Wu-Lan Li
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
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5
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Li Y, Yu ND, Ye XL, Jiang MC, Chen XQ. Construction of lung cancer serum markers based on ReliefF feature selection. Comput Methods Biomech Biomed Engin 2024; 27:1215-1223. [PMID: 37489703 DOI: 10.1080/10255842.2023.2235045] [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: 05/05/2023] [Accepted: 07/03/2023] [Indexed: 07/26/2023]
Abstract
Serum miRNAs are available clinical samples for cancer screening. Identifying early serum markers in lung cancer (LC) is essential for patients' early diagnosis and clinical treatment. Expression data of serum miRNAs of lung adenocarcinoma (LUAD) patients and healthy individuals were downloaded from the Gene Expression Omnibus (GEO). These data were normalized and subjected to differential expression analysis to obtain differentially expressed miRNAs (DEmiRNAs). The DEmiRNAs were subsequently subjected to ReliefF feature selection, and subsets closely related to cancer were screened as candidate feature miRNAs. Thereafter, a Gaussian Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifier were constructed based on these candidate feature miRNAs. Then the best diagnostic signature was constructed through NB combined with incremental feature selection (IFS). Thereafter, these samples were subjected to principal component analysis (PCA) based on miRNAs with optimal predictive performance. Finally, the peripheral serum miRNAs of 64 LUAD patients and 59 normal individuals were extracted for qRT-PCR analysis to validate the performance of the diagnostic model in respect of clinical detection. Finally, according to area under the curve (AUC) and accuracy values, the NB classifier composed of miR-5100 and miR-663a manifested the most outstanding diagnostic performance. The PCA results also revealed that the 2-miRNA diagnostic signature could effectively distinguish cancer patients from healthy individuals. Finally, qRT-PCR results of clinical serum samples revealed that miR-5100 and miR-663a expression in tumor samples was remarkably higher than that in normal samples. The AUC of the 2-miRNA diagnostic signature was 0.968. In summary, we identified markers (miR-5100 and miR-663a) in serum for early LUAD screening, providing ideas for developing early LUAD diagnostic models.
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Affiliation(s)
- Yong Li
- Department of Respiration Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Nan-Ding Yu
- Department of Respiration Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Xiang-Li Ye
- Department of Respiration Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Mei-Chen Jiang
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
| | - Xiang-Qi Chen
- Department of Respiration Medicine, Fujian Medical University Union Hospital, Fuzhou, Fujian, China
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6
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Abadir P, Oh E, Chellappa R, Choudhry N, Demiris G, Ganesan D, Karlawish J, Marlin B, Li RM, Dehak N, Arbaje A, Unberath M, Cudjoe T, Chute C, Moore JH, Phan P, Samus Q, Schoenborn NL, Battle A, Walston JD. Artificial Intelligence and Technology Collaboratories: Innovating aging research and Alzheimer's care. Alzheimers Dement 2024; 20:3074-3079. [PMID: 38324244 PMCID: PMC11032553 DOI: 10.1002/alz.13710] [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: 08/24/2023] [Revised: 12/11/2023] [Accepted: 12/28/2023] [Indexed: 02/08/2024]
Abstract
This perspective outlines the Artificial Intelligence and Technology Collaboratories (AITC) at Johns Hopkins University, University of Pennsylvania, and University of Massachusetts, highlighting their roles in developing AI-based technologies for older adult care, particularly targeting Alzheimer's disease (AD). These National Institute on Aging (NIA) centers foster collaboration among clinicians, gerontologists, ethicists, business professionals, and engineers to create AI solutions. Key activities include identifying technology needs, stakeholder engagement, training, mentoring, data integration, and navigating ethical challenges. The objective is to apply these innovations effectively in real-world scenarios, including in rural settings. In addition, the AITC focuses on developing best practices for AI application in the care of older adults, facilitating pilot studies, and addressing ethical concerns related to technology development for older adults with cognitive impairment, with the ultimate aim of improving the lives of older adults and their caregivers. HIGHLIGHTS: Addressing the complex needs of older adults with Alzheimer's disease (AD) requires a comprehensive approach, integrating medical and social support. Current gaps in training, techniques, tools, and expertise hinder uniform access across communities and health care settings. Artificial intelligence (AI) and digital technologies hold promise in transforming care for this demographic. Yet, transitioning these innovations from concept to marketable products presents significant challenges, often stalling promising advancements in the developmental phase. The Artificial Intelligence and Technology Collaboratories (AITC) program, funded by the National Institute on Aging (NIA), presents a viable model. These Collaboratories foster the development and implementation of AI methods and technologies through projects aimed at improving care for older Americans, particularly those with AD, and promote the sharing of best practices in AI and technology integration. Why Does This Matter? The National Institute on Aging (NIA) Artificial Intelligence and Technology Collaboratories (AITC) program's mission is to accelerate the adoption of artificial intelligence (AI) and new technologies for the betterment of older adults, especially those with dementia. By bridging scientific and technological expertise, fostering clinical and industry partnerships, and enhancing the sharing of best practices, this program can significantly improve the health and quality of life for older adults with Alzheimer's disease (AD).
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Affiliation(s)
- Peter Abadir
- Johns Hopkins MedicineJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Esther Oh
- Johns Hopkins MedicineJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Rama Chellappa
- Whiting School of EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Niteesh Choudhry
- Harvard Medical SchoolHarvard University, and Department of Medicine Brigham and Women's HospitalBostonMassachusettsUSA
| | - George Demiris
- School of NursingUniversity of Pennsylvania, and Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Deepak Ganesan
- Manning College of Information and Computer SciencesUniversity of Massachusetts AmherstAmherstMassachusettsUSA
| | - Jason Karlawish
- Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Benjamin Marlin
- Manning College of Information and Computer SciencesUniversity of Massachusetts AmherstAmherstMassachusettsUSA
| | - Rose M. Li
- Rose Li and Associates, Inc.Chevy ChaseMarylandUSA
| | - Najim Dehak
- Whiting School of EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Alicia Arbaje
- Johns Hopkins MedicineJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Mathias Unberath
- Whiting School of EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Thomas Cudjoe
- Johns Hopkins MedicineJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Christopher Chute
- Johns Hopkins MedicineJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Jason H. Moore
- Department of Computational BiomedicineCedars‐Sinai Medical CenterLos AngelesCaliforniaUSA
| | - Phillip Phan
- Johns Hopkins Carey Business SchoolJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Quincy Samus
- Johns Hopkins MedicineJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Nancy L. Schoenborn
- Johns Hopkins MedicineJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
| | - Alexis Battle
- Whiting School of EngineeringJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jeremy D. Walston
- Johns Hopkins MedicineJohns Hopkins University, School of MedicineBaltimoreMarylandUSA
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7
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Napolitano G, Has C, Schwerk A, Yuan JH, Ullrich C. Potential of Artificial Intelligence to Accelerate Drug Development for Rare Diseases. Pharmaceut Med 2024; 38:79-86. [PMID: 38315404 DOI: 10.1007/s40290-023-00504-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2023] [Indexed: 02/07/2024]
Abstract
The growth in breadth and depth of artificial intelligence (AI) applications has been fast, running hand in hand with the increasing amount of digital data available. Here, we comment on the application of AI in the field of drug development, with a strong focus on the specific achievements and challenges posed by rare diseases. Data paucity and high costs make drug development for rare diseases especially hard. AI can enable otherwise inaccessible approaches based on the large-scale integration of heterogeneous datasets and knowledge bases, guided by expert biological understanding. Obstacles still exist for the routine use of AI in the usually conservative pharmaceutical domain, which can easily become disillusioned. It is crucial to acknowledge that AI is a powerful, supportive tool that can assist but not replace human expertise in the various phases and aspects of drug discovery and development.
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Affiliation(s)
| | - Canan Has
- Centogene GmbH, Alboinstraße 36-42, 12103, Berlin, Germany
| | - Anne Schwerk
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jui-Hung Yuan
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carsten Ullrich
- Beriln Institute of Health Center for Regenerative Therapies (BCRT), Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
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8
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Visan AI, Negut I. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life (Basel) 2024; 14:233. [PMID: 38398742 PMCID: PMC10890405 DOI: 10.3390/life14020233] [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: 01/09/2024] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.
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Affiliation(s)
| | - Irina Negut
- National Institute for Lasers, Plasma and Radiation Physics, 409 Atomistilor Street, 077125 Magurele, Ilfov, Romania;
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9
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Shafiq M, Sherwani ZA, Mushtaq M, Nur-E-Alam M, Ahmad A, Ul-Haq Z. A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors. Mol Divers 2024:10.1007/s11030-023-10799-0. [PMID: 38305819 DOI: 10.1007/s11030-023-10799-0] [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: 10/25/2023] [Accepted: 12/22/2023] [Indexed: 02/03/2024]
Abstract
Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for further research, the significant structural conservation among PI3Ks makes it extremely difficult to develop new isoform-selective PI3Kα inhibitors. Embracing this challenge, we herein designed a hybrid protocol by integrating machine learning (ML) with in silico drug-designing strategies. A deep learning classification model was developed and trained on the physicochemical descriptors data of known PI3Kα inhibitors and used as a screening filter for a database of small molecules. This approach led us to the prediction of 662 compounds showcasing appropriate features to be considered as PI3Kα inhibitors. Subsequently, a multiphase molecular docking was applied to further characterize the predicted hits in terms of their binding affinities and binding modes in the targeted cavity of the PI3Kα. As a result, a total of 12 compounds were identified whereas the best poses highlighted the efficiency of these ligands in maintaining interactions with the crucial residues of the protein to be targeted for the inhibition of associated activity. Notably, potential activity of compound 12 in counteracting PI3Kα function was found in a previous in vitro study. Following the drug-likeness and pharmacokinetic characterizations, six compounds (compounds 1, 2, 3, 6, 7, and 11) with suitable ADME-T profiles and promising bioavailability were selected. The mechanistic studies in dynamic mode further endorsed the potential of identified hits in blocking the ATP-binding site of the receptor with higher binding affinities than the native inhibitor, alpelisib (BYL-719), particularly the compounds 1, 2, and 11. These outcomes support the reliability of the developed classification model and the devised computational strategy for identifying new isoform-selective drug candidates for PI3Kα inhibition.
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Affiliation(s)
- Muhammad Shafiq
- H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Zaid Anis Sherwani
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Mamona Mushtaq
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan
| | - Mohammad Nur-E-Alam
- Department of Pharmacognosy, College of Pharmacy, King Saud University, P.O. Box. 2457, Riyadh, 11451, Kingdom of Saudi Arabia
| | - Aftab Ahmad
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA, 92618, USA
| | - Zaheer Ul-Haq
- Dr. Panjwani Center for Molecular Medicine and Drug Research, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, 75270, Pakistan.
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10
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Higgins WT, Vibhute S, Bennett C, Lindert S. Discovery of Nanomolar Inhibitors for Human Dihydroorotate Dehydrogenase Using Structure-Based Drug Discovery Methods. J Chem Inf Model 2024; 64:435-448. [PMID: 38175956 DOI: 10.1021/acs.jcim.3c01358] [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: 01/06/2024]
Abstract
We used a structure-based drug discovery approach to identify novel inhibitors of human dihydroorotate dehydrogenase (DHODH), which is a therapeutic target for treating cancer and autoimmune and inflammatory diseases. In the case of acute myeloid leukemia, no previously discovered DHODH inhibitors have yet succeeded in this clinical application. Thus, there remains a strong need for new inhibitors that could be used as alternatives to the current standard-of-care. Our goal was to identify novel inhibitors of DHODH. We implemented prefiltering steps to omit PAINS and Lipinski violators at the earliest stages of this project. This enriched compounds in the data set that had a higher potential of favorable oral druggability. Guided by Glide SP docking scores, we found 20 structurally unique compounds from the ChemBridge EXPRESS-pick library that inhibited DHODH with IC50, DHODH values between 91 nM and 2.7 μM. Ten of these compounds reduced MOLM-13 cell viability with IC50, MOLM-13 values between 2.3 and 50.6 μM. Compound 16 (IC50, DHODH = 91 nM) inhibited DHODH more potently than the known DHODH inhibitor, teriflunomide (IC50, DHODH = 130 nM), during biochemical characterizations and presented a promising scaffold for future hit-to-lead optimization efforts. Compound 17 (IC50, MOLM-13 = 2.3 μM) was most successful at reducing survival in MOLM-13 cell lines compared with our other hits. The discovered compounds represent excellent starting points for the development and optimization of novel DHODH inhibitors.
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Affiliation(s)
- William T Higgins
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
| | - Sandip Vibhute
- Medicinal Chemistry Shared Resource, Comprehensive Cancer Center, Ohio State University, Columbus, Ohio 43210, United States
| | - Chad Bennett
- Medicinal Chemistry Shared Resource, Comprehensive Cancer Center, Ohio State University, Columbus, Ohio 43210, United States
- Drug Development Institute, Ohio State University, Columbus, Ohio 43210, United States
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, United States
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11
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Thirunavukkarasu MK, Veerappapillai S, Karuppasamy R. Sequential virtual screening collaborated with machine-learning strategies for the discovery of precise medicine against non-small cell lung cancer. J Biomol Struct Dyn 2024; 42:615-628. [PMID: 36995235 DOI: 10.1080/07391102.2023.2194994] [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: 12/16/2022] [Accepted: 03/17/2023] [Indexed: 03/31/2023]
Abstract
Dysregulation of MAPK pathway receptors are crucial in causing uncontrolled cell proliferation in many cancer types including non-small cell lung cancer. Due to the complications in targeting the upstream components, MEK is an appealing target to diminish this pathway activity. Hence, we have aimed to discover potent MEK inhibitors by integrating virtual screening and machine learning-based strategies. Preliminary screening was conducted on 11,808 compounds using the cavity-based pharmacophore model AADDRRR. Further, seven ML models were accessed to predict the MEK active compounds using six molecular representations. The LGB model with morgan2 fingerprints surpasses other models ensuing 0.92 accuracy and 0.83 MCC value versus test set and 0.85 accuracy and 0.70 MCC value with external set. Further, the binding ability of screened hits were examined using glide XP docking and prime-MM/GBSA calculations. Note that we have utilized three ML-based scoring functions to predict the various biological properties of the compounds. The two hit compounds such as DB06920 and DB08010 resulted excellent binding mechanism with acceptable toxicity properties against MEK. Further, 200 ns of MD simulation combined with MM-GBSA/PBSA calculations confirms that DB06920 may have stable binding conformations with MEK thus step forwarded to the experimental studies in the near future.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Muthu Kumar Thirunavukkarasu
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Shanthi Veerappapillai
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Ramanathan Karuppasamy
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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12
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Doherty T, Yao Z, Khleifat AAL, Tantiangco H, Tamburin S, Albertyn C, Thakur L, Llewellyn DJ, Oxtoby NP, Lourida I, Ranson JM, Duce JA. Artificial intelligence for dementia drug discovery and trials optimization. Alzheimers Dement 2023; 19:5922-5933. [PMID: 37587767 DOI: 10.1002/alz.13428] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/26/2023] [Accepted: 07/05/2023] [Indexed: 08/18/2023]
Abstract
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.
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Affiliation(s)
- Thomas Doherty
- Eisai Europe Ltd, Hatfield, UK
- University of Westminster, London, UK
| | | | - Ahmad A L Khleifat
- Institute of Psychiatry, Psychology & Neuroscience, Department of Basic and Clinical Neuroscience, King's College London, London, UK
| | | | - Stefano Tamburin
- University of Verona, Department of Neurosciences, Biomedicine & Movement Sciences, Verona, Italy
| | - Chris Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | | | - James A Duce
- The ALBORADA Drug Discovery Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
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13
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Guo W, Lv C, Guo M, Zhao Q, Yin X, Zhang L. Innovative applications of artificial intelligence in zoonotic disease management. SCIENCE IN ONE HEALTH 2023; 2:100045. [PMID: 39077042 PMCID: PMC11262289 DOI: 10.1016/j.soh.2023.100045] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/22/2023] [Indexed: 07/31/2024]
Abstract
Zoonotic diseases, transmitted between humans and animals, pose a substantial threat to global public health. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the fight against diseases. This comprehensive review discusses the innovative applications of AI in the management of zoonotic diseases, including disease prediction, early diagnosis, drug development, and future prospects. AI-driven predictive models leverage extensive datasets to predict disease outbreaks and transmission patterns, thereby facilitating proactive public health responses. Early diagnosis benefits from AI-powered diagnostic tools that expedite pathogen identification and containment. Furthermore, AI technologies have accelerated drug discovery by identifying potential drug targets and optimizing candidate drugs. This review addresses these advancements, while also examining the promising future of AI in zoonotic disease control. We emphasize the pivotal role of AI in revolutionizing our approach to managing zoonotic diseases and highlight its potential to safeguard the health of both humans and animals on a global scale.
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Affiliation(s)
- Wenqiang Guo
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chenrui Lv
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Meng Guo
- College of Veterinary Medicine, Henan Agricultural University, Zhengzhou 450046, China
| | - Qiwei Zhao
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xinyi Yin
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Li Zhang
- Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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14
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Ahmed F, Yang YJ, Samantasinghar A, Kim YW, Ko JB, Choi KH. Network-based drug repurposing for HPV-associated cervical cancer. Comput Struct Biotechnol J 2023; 21:5186-5200. [PMID: 37920815 PMCID: PMC10618120 DOI: 10.1016/j.csbj.2023.10.038] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/04/2023] Open
Abstract
In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, South Korea
| | - Young Jin Yang
- Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, South Korea
| | | | - Young Woo Kim
- Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, South Korea
| | - Jeong Beom Ko
- Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, South Korea
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, South Korea
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15
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Durai P, Lee SJ, Lee JW, Pan CH, Park K. Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors. J Cheminform 2023; 15:86. [PMID: 37742003 PMCID: PMC10517535 DOI: 10.1186/s13321-023-00760-6] [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: 05/23/2023] [Accepted: 09/12/2023] [Indexed: 09/25/2023] Open
Abstract
Machine learning-based chemical screening has made substantial progress in recent years. However, these predictions often have low accuracy and high uncertainty when identifying new active chemical scaffolds. Hence, a high proportion of retrieved compounds are not structurally novel. In this study, we proposed a strategy to address this issue by iteratively optimizing an evolutionary chemical binding similarity (ECBS) model using experimental validation data. Various data update and model retraining schemes were tested to efficiently incorporate new experimental data into ECBS models, resulting in a fine-tuned ECBS model with improved accuracy and coverage. To demonstrate the effectiveness of our approach, we identified the novel hit molecules for the mitogen-activated protein kinase kinase 1 (MEK1). These molecules showed sub-micromolar affinity (Kd 0.1-5.3 μM) to MEKs and were distinct from previously-known MEK1 inhibitors. We also determined the binding specificity of different MEK isoforms and proposed potential docking models. Furthermore, using de novo drug design tools, we utilized one of the new MEK inhibitors to generate additional drug-like molecules with improved binding scores. This resulted in the identification of several potential MEK1 inhibitors with better binding affinity scores. Our results demonstrated the potential of this approach for identifying novel hit molecules and optimizing their binding affinities.
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Affiliation(s)
- Prasannavenkatesh Durai
- Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Sue Jung Lee
- Natural Product Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Jae Wook Lee
- Natural Product Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Cheol-Ho Pan
- Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea
| | - Keunwan Park
- Natural Product Informatics Research Center, Korea Institute of Science and Technology, Gangneung, 25451, Republic of Korea.
- Department of YM-KIST Bio-Health Convergence, Yonsei University, Wonju, 26493, Republic of Korea.
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16
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Alnammi M, Liu S, Ericksen SS, Ananiev GE, Voter AF, Guo S, Keck JL, Hoffmann FM, Wildman SA, Gitter A. Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries. J Chem Inf Model 2023; 63:5513-5528. [PMID: 37625010 PMCID: PMC10538940 DOI: 10.1021/acs.jcim.3c00912] [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: 06/16/2023] [Indexed: 08/27/2023]
Abstract
Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries. However, there has been limited experimental validation of these methods in practical applications on large commercially available or synthesize-on-demand chemical libraries. Through a prospective evaluation with the bacterial protein-protein interaction PriA-SSB, we demonstrate that ligand-based virtual screening can identify many active compounds in large commercial libraries. We use cross-validation to compare different types of supervised learning models and select a random forest (RF) classifier as the best model for this target. When predicting the activity of more than 8 million compounds from Aldrich Market Select, the RF substantially outperforms a naïve baseline based on chemical structure similarity. 48% of the RF's 701 selected compounds are active. The RF model easily scales to score one billion compounds from the synthesize-on-demand Enamine REAL database. We tested 68 chemically diverse top predictions from Enamine REAL and observed 31 hits (46%), including one with an IC50 value of 1.3 μM.
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Affiliation(s)
- Moayad Alnammi
- Department
of Computer Sciences, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
- Department
of Information and Computer Science, King
Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
| | - Shengchao Liu
- Department
of Computer Sciences, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
| | - Spencer S. Ericksen
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - Gene E. Ananiev
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - Andrew F. Voter
- Department
of Biomolecular Chemistry, University of
Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - Song Guo
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - James L. Keck
- Department
of Biomolecular Chemistry, University of
Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - F. Michael Hoffmann
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
- McArdle Laboratory
for Cancer Research, University of Wisconsin−Madison, Madison, Wisconsin 53705, United States
| | - Scott A. Wildman
- Small
Molecule Screening Facility, University
of Wisconsin−Madison, Madison, Wisconsin 53792, United States
| | - Anthony Gitter
- Department
of Computer Sciences, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
- Morgridge
Institute for Research, Madison, Wisconsin 53715, United States
- Department
of Biostatistics and Medical Informatics, University of Wisconsin−Madison, Madison, Wisconsin 53792, United States
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17
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Han R, Yoon H, Kim G, Lee H, Lee Y. Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery. Pharmaceuticals (Basel) 2023; 16:1259. [PMID: 37765069 PMCID: PMC10537003 DOI: 10.3390/ph16091259] [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: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical industry and research, where it has been utilized to efficiently identify new chemical entities with desirable properties. The application of AI algorithms to drug discovery presents both remarkable opportunities and challenges. This review article focuses on the transformative role of AI in medicinal chemistry. We delve into the applications of machine learning and deep learning techniques in drug screening and design, discussing their potential to expedite the early drug discovery process. In particular, we provide a comprehensive overview of the use of AI algorithms in predicting protein structures, drug-target interactions, and molecular properties such as drug toxicity. While AI has accelerated the drug discovery process, data quality issues and technological constraints remain challenges. Nonetheless, new relationships and methods have been unveiled, demonstrating AI's expanding potential in predicting and understanding drug interactions and properties. For its full potential to be realized, interdisciplinary collaboration is essential. This review underscores AI's growing influence on the future trajectory of medicinal chemistry and stresses the importance of ongoing synergies between computational and domain experts.
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Affiliation(s)
| | | | | | | | - Yoonji Lee
- College of Pharmacy, Chung-Ang University, Seoul 06974, Republic of Korea
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18
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Kırboğa KK, Abbasi S, Küçüksille EU. Explainability and white box in drug discovery. Chem Biol Drug Des 2023; 102:217-233. [PMID: 37105727 DOI: 10.1111/cbdd.14262] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023]
Abstract
Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed.
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Affiliation(s)
- Kevser Kübra Kırboğa
- Bioengineering Department, Bilecik Seyh Edebali University, Bilecik, Turkey
- Informatics Institute, Istanbul Technical University, Maslak, Turkey
| | - Sumra Abbasi
- Department of Biological Sciences, National of Medical Sciences, Rawalpindi, Pakistan
| | - Ecir Uğur Küçüksille
- Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey
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19
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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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20
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Park D, Son SI, Kim MS, Kim TY, Choi JH, Lee SE, Hong D, Kim MC. Machine learning predictive model for aspiration screening in hospitalized patients with acute stroke. Sci Rep 2023; 13:7835. [PMID: 37188793 DOI: 10.1038/s41598-023-34999-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 05/11/2023] [Indexed: 05/17/2023] Open
Abstract
Dysphagia is a fatal condition after acute stroke. We established machine learning (ML) models for screening aspiration in patients with acute stroke. This retrospective study enrolled patients with acute stroke admitted to a cerebrovascular specialty hospital between January 2016 and June 2022. A videofluoroscopic swallowing study (VFSS) confirmed aspiration. We evaluated the Gugging Swallowing Screen (GUSS), an early assessment tool for dysphagia, in all patients and compared its predictive value with ML models. Following ML algorithms were applied: regularized logistic regressions (ridge, lasso, and elastic net), random forest, extreme gradient boosting, support vector machines, k-nearest neighbors, and naïve Bayes. We finally analyzed data from 3408 patients, and 448 of them had aspiration on VFSS. The GUSS showed an area under the receiver operating characteristics curve (AUROC) of 0.79 (0.77-0.81). The ridge regression model was the best model among all ML models, with an AUROC of 0.81 (0.76-0.86), an F1 measure of 0.45. Regularized logistic regression models exhibited higher sensitivity (0.66-0.72) than the GUSS (0.64). Feature importance analyses revealed that the modified Rankin scale was the most important feature of ML performance. The proposed ML prediction models are valid and practical for screening aspiration in patients with acute stroke.
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Affiliation(s)
- Dougho Park
- Department of Medical Science and Engineering, School of Convergence Science and Technology, Pohang University of Science and Technology, Pohang, Republic of Korea.
- Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea.
| | - Seok Il Son
- Occupational Therapy Department of Rehabilitation Center, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Min Sol Kim
- Occupational Therapy Department of Rehabilitation Center, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Tae Yeon Kim
- Speech-Language Therapy Department of Rehabilitation Center, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Jun Hwa Choi
- Department of Quality Improvement, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Sang-Eok Lee
- Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Daeyoung Hong
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
| | - Mun-Chul Kim
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, Republic of Korea
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21
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Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M, Thaunat O, Buchler M, Crespo M, Linhares K, Orandi BJ, Akalin E, Pujol GS, Silva HT, Gupta G, Segev DL, Jouven X, Bentall AJ, Stegall MD, Lefaucheur C, Aubert O, Loupy A. Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction. Kidney Int 2023; 103:936-948. [PMID: 36572246 DOI: 10.1016/j.kint.2022.12.011] [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: 06/14/2022] [Revised: 11/04/2022] [Accepted: 12/15/2022] [Indexed: 12/24/2022]
Abstract
Machine learning (ML) models have recently shown potential for predicting kidney allograft outcomes. However, their ability to outperform traditional approaches remains poorly investigated. Therefore, using large cohorts of kidney transplant recipients from 14 centers worldwide, we developed ML-based prediction models for kidney allograft survival and compared their prediction performances to those achieved by a validated Cox-Based Prognostication System (CBPS). In a French derivation cohort of 4000 patients, candidate determinants of allograft failure including donor, recipient and transplant-related parameters were used as predictors to develop tree-based models (RSF, RSF-ERT, CIF), Support Vector Machine models (LK-SVM, AK-SVM) and a gradient boosting model (XGBoost). Models were externally validated with cohorts of 2214 patients from Europe, 1537 from North America, and 671 from South America. Among these 8422 kidney transplant recipients, 1081 (12.84%) lost their grafts after a median post-transplant follow-up time of 6.25 years (Inter Quartile Range 4.33-8.73). At seven years post-risk evaluation, the ML models achieved a C-index of 0.788 (95% bootstrap percentile confidence interval 0.736-0.833), 0.779 (0.724-0.825), 0.786 (0.735-0.832), 0.527 (0.456-0.602), 0.704 (0.648-0.759) and 0.767 (0.711-0.815) for RSF, RSF-ERT, CIF, LK-SVM, AK-SVM and XGBoost respectively, compared with 0.808 (0.792-0.829) for the CBPS. In validation cohorts, ML models' discrimination performances were in a similar range of those of the CBPS. Calibrations of the ML models were similar or less accurate than those of the CBPS. Thus, when using a transparent methodological pipeline in validated international cohorts, ML models, despite overall good performances, do not outperform a traditional CBPS in predicting kidney allograft failure. Hence, our current study supports the continued use of traditional statistical approaches for kidney graft prognostication.
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Affiliation(s)
- Agathe Truchot
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France
| | - Marc Raynaud
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France
| | - Nassim Kamar
- Université Paul Sabatier, INSERM, Department of Nephrology and Organ Transplantation, CHU Rangueil and Purpan, Toulouse, France
| | - Maarten Naesens
- Department of Microbiology, Immunology and Transplantation, KU Leuven, Leuven, Belgium
| | - Christophe Legendre
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Michel Delahousse
- Department of Transplantation, Nephrology and Clinical Immunology, Foch Hospital, Suresnes, France
| | - Olivier Thaunat
- Department of Transplantation, Nephrology and Clinical Immunology, Hospices Civils de Lyon, Lyon, France
| | - Matthias Buchler
- Nephrology and Immunology Department, Bretonneau Hospital, Tours, France
| | - Marta Crespo
- Department of Nephrology, Hospital del Mar Barcelona, Barcelona, Spain
| | - Kamilla Linhares
- Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Babak J Orandi
- University of Alabama at Birmingham Heersink School of Medicine, Birmingham, Alabama, USA
| | - Enver Akalin
- Renal Division, Montefiore Medical Centre, Kidney Transplantation Program, Albert Einstein College of Medicine, New York, New York, USA
| | - Gervacio Soler Pujol
- Unidad de Trasplante Renopancreas, Centro de Educacion Medica e Investigaciones Clinicas Buenos Aires, Buenos Aires, Argentina
| | - Helio Tedesco Silva
- Hospital do Rim, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Gaurav Gupta
- Division of Nephrology, Department of Internal Medicine, Virginia Commonwealth University School of Medicine, Richmond, Virginia, USA
| | - Dorry L Segev
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Xavier Jouven
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Cardiology Department, European Georges Pompidou Hospital, Paris, France
| | - Andrew J Bentall
- William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark D Stegall
- William J von Liebig Centre for Transplantation and Clinical Regeneration, Mayo Clinic, Rochester, Minnesota, USA
| | - Carmen Lefaucheur
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Saint-Louis Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Olivier Aubert
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Alexandre Loupy
- Université de Paris, INSERM, PARCC, Paris Translational Research Centre for Organ Transplantation, Paris, France; Kidney Transplant Department, Necker Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France.
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22
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Trisciuzzi D, Siragusa L, Baroni M, Cruciani G, Nicolotti O. An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening. J Chem Inf Model 2022; 62:6812-6824. [PMID: 36320100 DOI: 10.1021/acs.jcim.2c00583] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.
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Affiliation(s)
- Daniela Trisciuzzi
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Lydia Siragusa
- Molecular Horizon s.r.l., Via Montelino, 30, 06084Bettona (PG), Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Massimo Baroni
- Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, Università degli Studi di Perugia, via Elce di Sotto, 8, 06123Perugia (PG), Italy
| | - Orazio Nicolotti
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy
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Hajjo R, Sabbah DA, Abusara OH, Al Bawab AQ. A Review of the Recent Advances in Alzheimer's Disease Research and the Utilization of Network Biology Approaches for Prioritizing Diagnostics and Therapeutics. Diagnostics (Basel) 2022; 12:diagnostics12122975. [PMID: 36552984 PMCID: PMC9777434 DOI: 10.3390/diagnostics12122975] [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: 10/16/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/29/2022] Open
Abstract
Alzheimer's disease (AD) is a polygenic multifactorial neurodegenerative disease that, after decades of research and development, is still without a cure. There are some symptomatic treatments to manage the psychological symptoms but none of these drugs can halt disease progression. Additionally, over the last few years, many anti-AD drugs failed in late stages of clinical trials and many hypotheses surfaced to explain these failures, including the lack of clear understanding of disease pathways and processes. Recently, different epigenetic factors have been implicated in AD pathogenesis; thus, they could serve as promising AD diagnostic biomarkers. Additionally, network biology approaches have been suggested as effective tools to study AD on the systems level and discover multi-target-directed ligands as novel treatments for AD. Herein, we provide a comprehensive review on Alzheimer's disease pathophysiology to provide a better understanding of disease pathogenesis hypotheses and decipher the role of genetic and epigenetic factors in disease development and progression. We also provide an overview of disease biomarkers and drug targets and suggest network biology approaches as new tools for identifying novel biomarkers and drugs. We also posit that the application of machine learning and artificial intelligence to mining Alzheimer's disease multi-omics data will facilitate drug and biomarker discovery efforts and lead to effective individualized anti-Alzheimer treatments.
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Affiliation(s)
- Rima Hajjo
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, The University of North Carlina at Chapel Hill, Chapel Hill, NC 27599, USA
- National Center for Epidemics and Communicable Disease Control, Amman 11118, Jordan
- Correspondence:
| | - Dima A. Sabbah
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Osama H. Abusara
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
| | - Abdel Qader Al Bawab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, P.O. Box 130, Amman 11733, Jordan
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24
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Hernández-Silva D, Alcaraz-Pérez F, Pérez-Sánchez H, Cayuela ML. Virtual screening and zebrafish models in tandem, for drug discovery and development. Expert Opin Drug Discov 2022:1-13. [DOI: 10.1080/17460441.2022.2147503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- David Hernández-Silva
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Structural Bioinformatics and High-Performance Computing Research Group (BIOHPC), Computer Engineering Department, Universidad Católica de Murcia (UCAM), Guadalupe, 30107 Murcia, Spain
| | - Francisca Alcaraz-Pérez
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Horacio Pérez-Sánchez
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Maria Luisa Cayuela
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
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25
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Zhang Y, Luo M, Wu P, Wu S, Lee TY, Bai C. Application of Computational Biology and Artificial Intelligence in Drug Design. Int J Mol Sci 2022; 23:13568. [PMID: 36362355 PMCID: PMC9658956 DOI: 10.3390/ijms232113568] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/29/2022] [Accepted: 11/03/2022] [Indexed: 08/24/2023] Open
Abstract
Traditional drug design requires a great amount of research time and developmental expense. Booming computational approaches, including computational biology, computer-aided drug design, and artificial intelligence, have the potential to expedite the efficiency of drug discovery by minimizing the time and financial cost. In recent years, computational approaches are being widely used to improve the efficacy and effectiveness of drug discovery and pipeline, leading to the approval of plenty of new drugs for marketing. The present review emphasizes on the applications of these indispensable computational approaches in aiding target identification, lead discovery, and lead optimization. Some challenges of using these approaches for drug design are also discussed. Moreover, we propose a methodology for integrating various computational techniques into new drug discovery and design.
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Affiliation(s)
- Yue Zhang
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Mengqi Luo
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Peng Wu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518055, China
| | - Song Wu
- South China Hospital, Health Science Center, Shenzhen University, Shenzhen 518116, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
| | - Chen Bai
- School of Life and Health Sciences, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China
- Warshel Institute for Computational Biology, Shenzhen 518172, China
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26
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Khan MI, Park T, Imran MA, Gowda Saralamma VV, Lee DC, Choi J, Baig MH, Dong JJ. Development of machine learning models for the screening of potential HSP90 inhibitors. Front Mol Biosci 2022; 9:967510. [PMID: 36339714 PMCID: PMC9626531 DOI: 10.3389/fmolb.2022.967510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/15/2022] [Indexed: 11/18/2022] Open
Abstract
Heat shock protein 90 (Hsp90) is a molecular chaperone playing a significant role in the folding of client proteins. This cellular protein is linked to the progression of several cancer types, including breast cancer, lung cancer, and gastrointestinal stromal tumors. Several oncogenic kinases are Hsp90 clients and their activity depends on this molecular chaperone. This makes HSP90 a prominent therapeutic target for cancer treatment. Studies have confirmed the inhibition of HSP90 as a striking therapeutic treatment for cancer management. In this study, we have utilized machine learning and different in silico approaches to screen the KCB database to identify the potential HSP90 inhibitors. Further evaluation of these inhibitors on various cancer cell lines showed favorable inhibitory activity. These inhibitors could serve as a basis for future development of effective HSP90 inhibitors.
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Affiliation(s)
- Mohd Imran Khan
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Taehwan Park
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Mohammad Azhar Imran
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | | | - Duk Chul Lee
- Department of Family Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Jaehyuk Choi
- BNJBiopharma, Yonsei University International Campus, Incheon, South Korea
| | - Mohammad Hassan Baig
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- *Correspondence: Jae-June Dong, ; Mohammad Hassan Baig,
| | - Jae-June Dong
- Department of Family Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
- *Correspondence: Jae-June Dong, ; Mohammad Hassan Baig,
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27
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Artificial Intelligence in Biological Sciences. Life (Basel) 2022; 12:life12091430. [PMID: 36143468 PMCID: PMC9505413 DOI: 10.3390/life12091430] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 08/25/2022] [Accepted: 09/10/2022] [Indexed: 12/03/2022] Open
Abstract
Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.
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28
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Mégret L, Mendoza C, Arrieta Lobo M, Brouillet E, Nguyen TTY, Bouaziz O, Chambaz A, Néri C. Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases. Front Mol Neurosci 2022; 15:914830. [PMID: 36157078 PMCID: PMC9500540 DOI: 10.3389/fnmol.2022.914830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 08/19/2022] [Indexed: 11/13/2022] Open
Abstract
Micro-RNAs (miRNAs) are short (∼21 nt) non-coding RNAs that regulate gene expression through the degradation or translational repression of mRNAs. Accumulating evidence points to a role of miRNA regulation in the pathogenesis of a wide range of neurodegenerative (ND) diseases such as, for example, Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and Huntington disease (HD). Several systems level studies aimed to explore the role of miRNA regulation in NDs, but these studies remain challenging. Part of the problem may be related to the lack of sufficiently rich or homogeneous data, such as time series or cell-type-specific data obtained in model systems or human biosamples, to account for context dependency. Part of the problem may also be related to the methodological challenges associated with the accurate system-level modeling of miRNA and mRNA data. Here, we critically review the main families of machine learning methods used to analyze expression data, highlighting the added value of using shape-analysis concepts as a solution for precisely modeling highly dimensional miRNA and mRNA data such as the ones obtained in the study of the HD process, and elaborating on the potential of these concepts and methods for modeling complex omics data.
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Affiliation(s)
- Lucile Mégret
- Sorbonne Université, Centre National de la Recherche Scientifique UMR 8256, Paris, France
- *Correspondence: Lucile Mégret,
| | - Cloé Mendoza
- Sorbonne Université, Centre National de la Recherche Scientifique UMR 8256, Paris, France
| | - Maialen Arrieta Lobo
- Sorbonne Université, Centre National de la Recherche Scientifique UMR 8256, Paris, France
| | - Emmanuel Brouillet
- Sorbonne Université, Centre National de la Recherche Scientifique UMR 8256, Paris, France
| | - Thi-Thanh-Yen Nguyen
- Université Paris Cité, MAP5 (Centre National de la Recherche Scientifique UMR 8145), Paris, France
| | - Olivier Bouaziz
- Université Paris Cité, MAP5 (Centre National de la Recherche Scientifique UMR 8145), Paris, France
| | - Antoine Chambaz
- Université Paris Cité, MAP5 (Centre National de la Recherche Scientifique UMR 8145), Paris, France
| | - Christian Néri
- Sorbonne Université, Centre National de la Recherche Scientifique UMR 8256, Paris, France
- Christian Néri,
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29
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Wang R, Xu J, Yan R, Liu H, Zhao J, Xie Y, Deng W, Liao W, Nie Y. Virtual screening and activity evaluation of multitargeting inhibitors for idiopathic pulmonary fibrosis. Front Pharmacol 2022; 13:998245. [PMID: 36160399 PMCID: PMC9493029 DOI: 10.3389/fphar.2022.998245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
Abstract
Transforming growth factor β receptor (TGF-β1R) and receptor tyrosine kinases (RTKs), such as VEGFRs, PDGFRs and FGFRs are considered important therapeutic targets in blocking myofibroblast migration and activation of idiopathic pulmonary fibrosis (IPF). To screen and design innovative prodrug to simultaneously target these four classes of receptors, we proposed an approach based on network pharmacology combining virtual screening and machine learning activity prediction, followed by efficient in vitro and in vivo models to evaluate drug activity. We first constructed Collagen1A2-A549 cells with type I collagen as the main biomarker and evaluated the activity of compounds to inhibit collagen expression at the cellular level. The data from the first round of Collagen1A2-A549 cell screening were substituted into the machine learning model, and the model was optimized accordingly. As a result, the false positive rate of the model was reduced from 85.0% to 66.7%, and two prospective compounds, Z103080500 and Z104578368, were finally selected. Collagen levels were reduced effectively by both Z103080500 (67.88% reduction) and Z104578368 (69.54% reduction). Moreover, these two compounds showed low cellular cytotoxicity. Subsequently, the effect of Z103080500 and Z104578368 was evaluated in a bleomycin-induced C57BL/6 mouse IPF model. These results showed that 50 mg/kg Z103080500 and Z104578368 could effectively reduce the number of inflammatory cells and the expression level of α-SMA. Meanwhile, Z103080500 and Z104578368 reduced the expression of major markers and inflammatory factors of IPF, such as collagen, IFN-γ, IL-17 and HYP, indicating that these screened Z103080500 and Z104578368 effectively delayed lung tissue inflammation and had a potential therapeutic effect on IPF. Our findings demonstrate that a screening and evaluation model for prodrug against IPF has been successfully established. It is of great significance to further modify these compounds to enhance their potency and activity.
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Affiliation(s)
- Rui Wang
- Clinical Research Institute, The First People’s Hospital of Foshan, Foshan, China
| | - Jian Xu
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Rong Yan
- Clinical Research Institute, The First People’s Hospital of Foshan, Foshan, China
| | - Huanbin Liu
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Jingxin Zhao
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Yuan Xie
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Wenbin Deng
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
| | - Weiping Liao
- Foshan Fourth People’s Hospital, Foshan, China
- *Correspondence: Weiping Liao, ; Yichu Nie,
| | - Yichu Nie
- Clinical Research Institute, The First People’s Hospital of Foshan, Foshan, China
- School of Pharmaceutical Sciences (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
- *Correspondence: Weiping Liao, ; Yichu Nie,
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30
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Halder D, Das S, R A, R S J. Molecular docking and dynamics based approach for the identification of kinase inhibitors targeting PI3Kα against non-small cell lung cancer: a computational study. RSC Adv 2022; 12:21452-21467. [PMID: 35975074 PMCID: PMC9346375 DOI: 10.1039/d2ra03451d] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 06/23/2022] [Indexed: 12/17/2022] Open
Abstract
Non-small cell lung cancer (NSCLC) is an obscure disease whose incidence is increasing worldwide day by day, and PI3Kα is one of the major targets for cell proliferation due to the mutation. Since PI3K is a class of kinase enzyme, and no in silico research has been performed on the inhibition of PI3Kα mutation by small molecules, we have selected the protein kinase inhibitor database and performed the energy minimization process by ligand preparation. The key objective of this research is to identify the potential hits from the protein kinase inhibitor library and further to perform lead optimization by a molecular docking and dynamics approach. And so, the protein was selected (PDB ID: 4JPS), having a unique inhibitor and a specific binding pocket with amino acid residue for the inhibition of kinase activity. After the docking protocol validation, structure-based virtual screening by molecular docking and MMGBSA binding affinity calculations were performed and a total of ten hits were reported. Detailed analysis of the best scoring molecules was performed with ADMET analysis, induced fit docking (IFD) and molecular dynamics (MD) simulation. Two molecules - 6943 and 34100 - were considered lead molecules and showed better results than the PI3K inhibitor Copanlisib in the docking assessment, ADMET analysis, and molecular dynamics simulation. Furthermore, the synthetic accessibility of the two compounds - 6943 and 34100 - was investigated using SwissADME, and the two lead molecules are easier to synthesize than the PI3K inhibitor Copanlisib. Computational drug discovery tools were used for identification of kinase inhibitors as anti-cancer agents for NSCLC in the present research.
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Affiliation(s)
- Debojyoti Halder
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education Manipal Karnataka-576104 India +919742351531
| | - Subham Das
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education Manipal Karnataka-576104 India +919742351531
| | - Aiswarya R
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education Manipal Karnataka-576104 India +919742351531
| | - Jeyaprakash R S
- Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education Manipal Karnataka-576104 India +919742351531
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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32
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Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12125859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Current medical methods still confront numerous limitations and barriers to detect and fight against illnesses and disorders. The introduction of emerging technologies in the healthcare industry is anticipated to enable novel medical techniques for an efficient and effective smart healthcare system. Internet of Things (IoT), Wireless Sensor Networks (WSN), Big Data Analytics (BDA), and Cloud Computing (CC) can play a vital role in the instant detection of illnesses, diseases, viruses, or disorders. Complicated techniques such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) could provide acceleration in drug and antibiotics discovery. Moreover, the integration of visualization techniques such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) with Tactile Internet (TI), can be applied from the medical staff to provide the most accurate diagnosis and treatment for the patients. A novel system architecture, which combines several future technologies, is proposed in this paper. The objective is to describe the integration of a mixture of emerging technologies in assistance with advanced networks to provide a smart healthcare system that may be established in hospitals or medical centers. Such a system will be able to deliver immediate and accurate data to the medical stuff in order to aim them in order to provide precise patient diagnosis and treatment.
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33
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Trans-channel fluorescence learning improves high-content screening for Alzheimer’s disease therapeutics. NAT MACH INTELL 2022; 4:583-595. [DOI: 10.1038/s42256-022-00490-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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34
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Machine Learning for the Prediction of Antiviral Compounds Targeting Avian Influenza A/H9N2 Viral Proteins. Symmetry (Basel) 2022. [DOI: 10.3390/sym14061114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Avian influenza subtype A/H9N2—which infects chickens, reducing egg production by up to 80%—may be transmissible to humans. In humans, this virus is very harmful since it attacks the respiratory system and reproductive tract, replicating in both. Previous attempts to find antiviral candidates capable of inhibiting influenza A/H9N2 transmission were unsuccessful. This study aims to better characterize A/H9N2 to facilitate the discovery of antiviral compounds capable of inhibiting its transmission. The Symmetry of this study is to apply several machine learning methods to perform virtual screening to identify H9N2 antivirus candidates. The parameters used to measure the machine learning model’s quality included accuracy, sensitivity, specificity, balanced accuracy, and receiver operating characteristic score. We found that the extreme gradient boosting method yielded better results in classifying compounds predicted to be suitable antiviral compounds than six other machine learning methods, including logistic regression, k-nearest neighbor analysis, support vector machine, multilayer perceptron, random forest, and gradient boosting. Using this algorithm, we identified 10 candidate synthetic compounds with the highest scores. These high scores predicted that the molecular fingerprint may involve strong bonding characteristics. Thus, we were able to find significant candidates for synthetic H9N2 antivirus compounds and identify the best machine learning method to perform virtual screenings.
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35
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Drug Design by Pharmacophore and Virtual Screening Approach. Pharmaceuticals (Basel) 2022; 15:ph15050646. [PMID: 35631472 PMCID: PMC9145410 DOI: 10.3390/ph15050646] [Citation(s) in RCA: 63] [Impact Index Per Article: 31.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 05/18/2022] [Accepted: 05/21/2022] [Indexed: 12/20/2022] Open
Abstract
Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.
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36
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Identification of Potential WSB1 Inhibitors by AlphaFold Modeling, Virtual Screening, and Molecular Dynamics Simulation Studies. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:4629392. [PMID: 35600960 PMCID: PMC9122669 DOI: 10.1155/2022/4629392] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/27/2022] [Indexed: 12/03/2022]
Abstract
WD40 repeat and SOCS box containing 1 (WSB1) consists of seven WD40 repeat structural domains at the N-terminal end and one SOCS box structural domain at the C-terminal end. WSB1 promotes cancer progression by affecting the Von Hippel–Lindau tumor suppressor protein (pVHL) and upregulating hypoxia inducible factor-1α (HIF-1α) target gene expression. However, the crystal structure of WSB1 has not been reported, which is not beneficial to the research on WSB1 inhibitors. Therefore, we focused on specific small molecule inhibitors of WSB1. This study applied virtual screening and molecular dynamics simulations; finally, 20 compounds were obtained. Among them, compound G490-0341 showed the best stable structure and was a promising composite for further development of WSB1 inhibitors.
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Shang Q, Zhang Q, Liu X, Zhu L. Prediction of Early Alzheimer Disease by Hippocampal Volume Changes under Machine Learning Algorithm. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3144035. [PMID: 35572832 PMCID: PMC9106502 DOI: 10.1155/2022/3144035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 04/06/2022] [Accepted: 04/15/2022] [Indexed: 11/17/2022]
Abstract
This research was aimed at discussing the application value of different machine learning algorithms in the prediction of early Alzheimer's disease (AD), which was based on hippocampal volume changes in magnetic resonance imaging (MRI). In the research, the 84 cases in American Alzheimer's disease neuroimaging initiative (ADNI) database were selected as the research data. Based on the scoring results of cognitive function, all cases were divided into three groups, including cognitive function normal (normal group), early mild cognitive impairment (e-MCI group), and later mild cognitive impairment (l-MCI group) groups. Each group included 28 cases. The features of hippocampal volume changes in MRI images of the patients in different groups were extracted. The samples of training set and test set were established. Besides, the established support vector machine (SVM), decision tree (DT), and random forest (RF) prediction models were used to predict e-MCI. Metalinear regression was utilized to analyze MRI feature data, and the predictive accuracy, sensitivity, and specificity of different models were calculated. The result showed that the volumes of hippocampal left CA1, left CA2-3, left CA4-DG, left presubiculum, left tail, right CA2-3, right CA4-DG, right presubiculum, and right tail in e-MCI group were all smaller than those in normal group (P < 0.01). The corresponding volume of hippocampal subregions in l-MCI group was remarkably reduced compared with that in normal group (P < 0.001). The volumes of regions left CA1, left CA2-3, left CA4-DG, right CA2-3, right CA4-DG, and right presubiculum were all positively correlated with logical memory test-delay recall (LMT-DR) score (R 2 = 0.1702, 0.3779, 0.1607, 0.1620, 0.0426, and 0.1309; P < 0.001). The predictive accuracy of training set sample by DT, SVM, and RF was 86.67%, 93.33%, and 98.33%, respectively. Based on the changes in the volumes of left CA4-DG, right CA2-3, and right CA4-DG, the predictive accuracy of e-MCI and l-MCI by RF model was both higher than those by DT model (P < 0.01). Besides, the predictive accuracy, sensitivity, and specificity of e-MCI by RF model was all notably higher than those by DT model (P < 0.01). The above results demonstrated that the effective early AD prediction models were established by the volume changes in hippocampal subregions, which was based on RF in the research. The establishment of early AD prediction models offered certain reference basis to the diagnosis and treatment of AD patients.
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Affiliation(s)
- Qun Shang
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Qi Zhang
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Xiao Liu
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
| | - Lingchen Zhu
- Department of Radiology, Zibo Central Hospital, Zibo, 255000 Shandong, China
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Moshawih S, Goh HP, Kifli N, Idris AC, Yassin H, Kotra V, Goh KW, Liew KB, Ming LC. Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives. Chem Biol Drug Des 2022; 100:185-217. [PMID: 35490393 DOI: 10.1111/cbdd.14062] [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: 01/18/2022] [Revised: 04/15/2022] [Accepted: 04/23/2022] [Indexed: 11/28/2022]
Abstract
Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.
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Affiliation(s)
- Said Moshawih
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hui Poh Goh
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Nurolaini Kifli
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Azam Che Idris
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Hayati Yassin
- Faculty of Integrated Technologies, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
| | - Vijay Kotra
- Faculty of Pharmacy, Quest International University, Perak, Malaysia
| | - Khang Wen Goh
- Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
| | - Kai Bin Liew
- Faculty of Pharmacy, University of Cyberjaya, Cyberjaya, Malaysia
| | - Long Chiau Ming
- PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam
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Alqahtani A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2022; 2022:6201067. [PMID: 35509623 PMCID: PMC9060979 DOI: 10.1155/2022/6201067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/17/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022]
Abstract
Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.
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Affiliation(s)
- Amal Alqahtani
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, 31541, Saudi Arabia
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
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Panahi Y, Dadkhah M, Talei S, Gharari Z, Asghariazar V, Abdolmaleki A, Matin S, Molaei S. Can anti-parasitic drugs help control COVID-19? Future Virol 2022. [PMID: 35359702 PMCID: PMC8940209 DOI: 10.2217/fvl-2021-0160] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 02/28/2022] [Indexed: 01/18/2023]
Abstract
Novel COVID-19 is a public health emergency that poses a serious threat to people worldwide. Given the virus spreading so quickly, novel antiviral medications are desperately needed. Repurposing existing drugs is the first strategy. Anti-parasitic drugs were among the first to be considered as a potential treatment option for this disease. Even though many papers have discussed the efficacy of various anti-parasitic drugs in treating COVID-19 separately, so far, no single study comprehensively discussed these drugs. This study reviews some anti-parasitic recommended drugs to treat COVID-19, in terms of function and in vitro as well as clinical results. Finally, we briefly review the advanced techniques, such as artificial intelligence, that have been used to find effective drugs for the treatment of COVID-19.
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Affiliation(s)
- Yasin Panahi
- Department of Pharmacology & Toxicology, School of Pharmacy, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Masoomeh Dadkhah
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Sahand Talei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Zahra Gharari
- Department of Biotechnology, Faculty of Biological Sciences, Al-Zahra University, Tehran, Iran
| | - Vahid Asghariazar
- Deputy of Research & Technology, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Arash Abdolmaleki
- Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran.,Bio Science & Biotechnology Research center (BBRC), Sabalan University of Advanced Technologies (SUAT), Namin, Iran
| | - Somayeh Matin
- Department of Internal Medicine, Imam Khomeini Hospital, Ardabil University of Medical Sciences, Ardabil, Iran
| | - Soheila Molaei
- Pharmaceutical Sciences Research Center, Ardabil University of Medical Sciences, Ardabil, Iran.,Zoonoses Research Center, Ardabil University of Medical Sciences, Ardabil, Iran
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Baltrukevich H, Podlewska S. From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output. Front Pharmacol 2022; 13:844293. [PMID: 35359865 PMCID: PMC8960308 DOI: 10.3389/fphar.2022.844293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 01/26/2022] [Indexed: 12/02/2022] Open
Abstract
An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.
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Affiliation(s)
- Hanna Baltrukevich
- Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
- Faculty of Pharmacy, Chair of Technology and Biotechnology of Medical Remedies, Jagiellonian University Medical College in Krakow, Kraków, Poland
| | - Sabina Podlewska
- Maj Institute of Pharmacology, Polish Academy of Sciences, Kraków, Poland
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Ager Meldgaard S, Köhler J, Lund Mortensen H, Christiansen MPV, Noé F, Hammer B. Generating stable molecules using imitation and reinforcement learning. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2022. [DOI: 10.1088/2632-2153/ac3eb4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Abstract
Chemical space is routinely explored by machine learning methods to discover interesting molecules, before time-consuming experimental synthesizing is attempted. However, these methods often rely on a graph representation, ignoring 3D information necessary for determining the stability of the molecules. We propose a reinforcement learning (RL) approach for generating molecules in Cartesian coordinates allowing for quantum chemical prediction of the stability. To improve sample-efficiency we learn basic chemical rules from imitation learning (IL) on the GDB-11 database to create an initial model applicable for all stoichiometries. We then deploy multiple copies of the model conditioned on a specific stoichiometry in a RL setting. The models correctly identify low energy molecules in the database and produce novel isomers not found in the training set. Finally, we apply the model to larger molecules to show how RL further refines the IL model in domains far from the training data.
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Sarathi P, Padhi S. Insight of the various in silico screening techniques developed for assortment of cocrystal formers and their thermodynamic characterization. Drug Dev Ind Pharm 2022; 47:1523-1534. [PMID: 35164621 DOI: 10.1080/03639045.2022.2042554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Most of the widely used drugs have problems associated with their oral bioavailability either due to their poor aqueous solubility or due to their poor permeability. Co-crystallization is an efficient and economically feasible approach that offers a great opportunity for improvement in physicochemical properties such as solubility, stability, and bioavailability of such type of therapeutic agent. Selection of the best co-former plays a major role in co-crystallization. Various approaches have been developed for the selection of suitable co-formers with API. In recent years in silico screening, a computational tool paying more attention for screening of co-formers has been developed. Numerous approaches can be used for in silico screening such as the Autodocking tool, COSMORS, COSMOTHERM, etc. Autodocking can predict several numbers of co-former effectively screened in silico method to identify a suitable co-former with an API. Prediction of solubility and dissolution is also important for the development of co-crystal. In this review, we discuss in silico screening of coformer and thermodynamic approaches to determine the dissolution and solubility of co-crystal specially with reference to the drugs belonging to BCS class II group.
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Affiliation(s)
- Parth Sarathi
- Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida, India
| | - Swarupanjali Padhi
- Noida Institute of Engineering and Technology (Pharmacy Institute), Greater Noida, India
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44
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Huang T, Liu S, Huang J, Li J, Liu G, Zhang W, Wang X. Prediction and associated factors of hypothyroidism in systemic lupus erythematosus: a cross-sectional study based on multiple machine learning algorithms. Curr Med Res Opin 2022; 38:229-235. [PMID: 34873978 DOI: 10.1080/03007995.2021.2015156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
OBJECTIVES The prevalence of hypothyroidism in systemic lupus erythematosus (SLE) is significantly higher than that in the common public. While SLE itself can affect multiple organs, abnormal thyroid function may exacerbate organ damage in patients with SLE. We aimed to predict abnormal thyroid function and to examine the associated factors with multiple machine learning approaches. METHODS In a cross-sectional study, 255 patients diagnosed with SLE at the rheumatology department in Xiangya Hospital between June 2012 and December 2016 were investigated. Feature engineering was used for filtering out principle clinical parameters, and five different machine learning methods were used to build prediction models for SLE with hypothyroidism. RESULTS Feature engineering selected 11 variables with which to build machine learning models. Among them, random forest modelling obtained the best prediction performance, with an accuracy rate of 88.37 and an area under the receiver operating characteristic curve of 0.772. The weights of anti-SSB antibody and anti-dsDNA antibody were 1.421 and 1.011, respectively, indicating a strong association with hypothyroidism in SLE. CONCLUSIONS Random Forest model performed best and is recommended for selecting vital indices and assessing clinical complications of SLE, it indicated that anti-SSB and anti-dsDNA antibodies may play principal roles in the development of hypothyroidism in SLE patients. It's feasible to build an accurate machine learning model for early diagnosis or risk factors assessment in SLE using clinical parameters, which would provide a reference for the research work of SLE in China.
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Affiliation(s)
- Ting Huang
- Department of Rheumatology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Siyang Liu
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
| | - Jian Huang
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
| | - Jiarong Li
- Department of Rheumatology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Guixiong Liu
- School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
| | - Weiru Zhang
- Department of General Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xuan Wang
- Department of General Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
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Exploiting activity cliffs for building pharmacophore models and comparison with other pharmacophore generation methods: sphingosine kinase 1 as case study. J Comput Aided Mol Des 2022; 36:39-62. [PMID: 35059939 DOI: 10.1007/s10822-021-00435-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 11/24/2021] [Indexed: 12/20/2022]
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46
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Yang Q, Jian X, Syed AAS, Fahira A, Zheng C, Zhu Z, Wang K, Zhang J, Wen Y, Li Z, Pan D, Lu T, Wang Z, Shi Y. Structural Comparison and Drug Screening of Spike Proteins of Ten SARS-CoV-2 Variants. RESEARCH (WASHINGTON, D.C.) 2022; 2022:9781758. [PMID: 35198984 PMCID: PMC8829538 DOI: 10.34133/2022/9781758] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 12/30/2021] [Indexed: 12/14/2022]
Abstract
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has evolved many variants with stronger infectivity and immune evasion than the original strain, including Alpha, Beta, Gamma, Delta, Epsilon, Kappa, Iota, Lambda, and 21H strains. Amino acid mutations are enriched in the spike protein of SARS-CoV-2, which plays a crucial role in cell infection. However, the impact of these mutations on protein structure and function is unclear. Understanding the pathophysiology and pandemic features of these SARS-CoV-2 variants requires knowledge of the spike protein structures. Here, we obtained the spike protein structures of 10 main globally endemic SARS-CoV-2 strains using AlphaFold2. The clustering analysis based on structural similarity revealed the unique features of the mainly pandemic SARS-CoV-2 Delta variants, indicating that structural clusters can reflect the current characteristics of the epidemic more accurately than those based on the protein sequence. The analysis of the binding affinities of ACE2-RBD, antibody-NTD, and antibody-RBD complexes in the different variants revealed that the recognition of antibodies against S1 NTD and RBD was decreased in the variants, especially the Delta variant compared with the original strain, which may induce the immune evasion of SARS-CoV-2 variants. Furthermore, by virtual screening the ZINC database against a high-accuracy predicted structure of Delta spike protein and experimental validation, we identified multiple compounds that target S1 NTD and RBD, which might contribute towards the development of clinical anti-SARS-CoV-2 medicines. Our findings provided a basic foundation for future in vitro and in vivo investigations that might speed up the development of potential therapies for the SARS-CoV-2 variants.
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Affiliation(s)
- Qiangzhen Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Xuemin Jian
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Ali Alamdar Shah Syed
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Aamir Fahira
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Chenxiang Zheng
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Zijia Zhu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Ke Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Jinmai Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Yanqin Wen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Zhiqiang Li
- Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao 266003, China
| | - Dun Pan
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Tingting Lu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Zhuo Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao 266003, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
- Department of Psychiatry, First Teaching Hospital of Xinjiang Medical University, Urumqi 830046, China
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Zhou Y, Gao J. Why not try to predict autism spectrum disorder with crucial biomarkers in cuproptosis signaling pathway? Front Psychiatry 2022; 13:1037503. [PMID: 36405901 PMCID: PMC9667021 DOI: 10.3389/fpsyt.2022.1037503] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 10/17/2022] [Indexed: 01/24/2023] Open
Abstract
The exact pathogenesis of autism spectrum disorder (ASD) is still unclear, yet some potential mechanisms may not have been evaluated before. Cuproptosis is a novel form of regulated cell death reported this year, and no study has reported the relationship between ASD and cuproptosis. This study aimed to identify ASD in suspected patients early using machine learning models based on biomarkers of the cuproptosis pathway. We collected gene expression profiles from brain samples from ASD model mice and blood samples from humans with ASD, selected crucial genes in the cuproptosis signaling pathway, and then analysed these genes with different machine learning models. The accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves of the machine learning models were estimated in the training, internal validation, and external validation cohorts. Differences between models were determined with Bonferroni's test. The results of screening with the Boruta algorithm showed that FDX1, DLAT, LIAS, and ATP7B were crucial genes in the cuproptosis signaling pathway for ASD. All selected genes and corresponding proteins were also expressed in the human brain. The k-nearest neighbor, support vector machine and random forest models could identify approximately 72% of patients with ASD. The artificial neural network (ANN) model was the most suitable for the present data because the accuracy, sensitivity, and specificity were 0.90, 1.00, and 0.80, respectively, in the external validation cohort. Thus, we first report the prediction of ASD in suspected patients with machine learning methods based on crucial biomarkers in the cuproptosis signaling pathway, and these findings may contribute to investigations of the potential pathogenesis and early identification of ASD.
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Affiliation(s)
- Yu Zhou
- Department of Child Rehabilitation Division, Huai'an Maternal and Child Health Care Center, Huai'an, China.,Affiliated Hospital of Yang Zhou University Medical College, Huai'an Maternal and Child Health Care Center, Huai'an, China
| | - Jing Gao
- Department of Child Rehabilitation Division, Huai'an Maternal and Child Health Care Center, Huai'an, China.,Affiliated Hospital of Yang Zhou University Medical College, Huai'an Maternal and Child Health Care Center, Huai'an, China
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Mahendran N, Vincent PMDR, Srinivasan K, Chang CY. Improving the Classification of Alzheimer's Disease Using Hybrid Gene Selection Pipeline and Deep Learning. Front Genet 2021; 12:784814. [PMID: 34868275 PMCID: PMC8632950 DOI: 10.3389/fgene.2021.784814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.
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Affiliation(s)
- Nivedhitha Mahendran
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - P M Durai Raj Vincent
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
| | - Chuan-Yu Chang
- Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
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Kessler A, Kouznetsova VL, Tsigelny IF. Targeting Epigenetic Regulators Using Machine Learning: Potential Sirtuin 2 Inhibitors. JOURNAL OF COMPUTATIONAL BIOPHYSICS AND CHEMISTRY 2021. [DOI: 10.1142/s2737416521500526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Sirtuin 2 (SIRT2) is a nicotinamide adenine dinucleotide (NAD+)-dependent deacetylase that has been identified as a target for many diseases, including Parkinson’s disease (PD) and leukemia. Using 234 SIRT2 inhibitors from the ZINC15 database, we generated molecular descriptors with PaDEL and constructed a machine-learning (ML) model for the binary classification of SIRT2 inhibitors. To predict compounds with novel inhibitory mechanisms, we then applied the model on the ZINC15/FDA subset, yielding 107 potential SIRT2 inhibitors. For validation of these substances, we employed the binding analysis software AutoDock Vina to perform virtual screening, with which 43 compounds were considered best inhibitors at the [Formula: see text][Formula: see text]kcal/mol binding affinity threshold. Our results demonstrate the potential of ligand-based (LB) ML techniques in conjunction with receptor-based virtual screening (RBVS) to facilitate the drug discovery or repurposing.
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Affiliation(s)
- Andrew Kessler
- REHS program, San Diego Supercomputer Center, UC San Diego, California, USA
| | | | - Igor F. Tsigelny
- San Diego Supercomputer Center, UC San Diego, California, USA
- BiAna, San Diego, California, USA
- Department of Neurosciences, UC San Diego, California, USA
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