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Nada H, Kim S, Lee K. PT-Finder: A multi-modal neural network approach to target identification. Comput Biol Med 2024; 174:108444. [PMID: 38636325 DOI: 10.1016/j.compbiomed.2024.108444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/20/2024]
Abstract
Efficient target identification for bioactive compounds, including novel synthetic analogs, is crucial for accelerating the drug discovery pipeline. However, the process of target identification presents significant challenges and is often expensive, which in turn can hinder the drug discovery efforts. To address these challenges machine learning applications have arisen as a promising approach for predicting the targets for novel chemical compounds. These methods allow the exploration of ligand-target interactions, uncovering of biochemical mechanisms, and the investigation of drug repurposing. Typically, the current target identification tools rely on assessing ligand structural similarities. Herein, a multi-modal neural network model was built using a library of proteins, their respective sequences, and active inhibitors. Subsequent validations showed the model to possess accuracy of 82 % and MPRAUC of 0.80. Leveraging the trained model, we developed PT-Finder (Protein Target Finder), a user-friendly offline application that is capable of predicting the target proteins for hundreds of compounds within a few seconds. This combination of offline operation, speed, and accuracy positions PT-Finder as a powerful tool to accelerate drug discovery workflows. PT-Finder and its source codes have been made freely accessible for download at https://github.com/PT-Finder/PT-Finder.
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Affiliation(s)
- Hossam Nada
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea
| | - Sungdo Kim
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea
| | - Kyeong Lee
- BK21 FOUR Team and Integrated Research Institute for Drug Development, College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea.
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2
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Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, Wang S, Qiu Y, Chen Y, Zhu F. TTD: Therapeutic Target Database describing target druggability information. Nucleic Acids Res 2024; 52:D1465-D1477. [PMID: 37713619 PMCID: PMC10767903 DOI: 10.1093/nar/gkad751] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/31/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.
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Affiliation(s)
- Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Donghai Zhao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Pinzi L, Rastelli G. Trends and Applications in Computationally Driven Drug Repurposing. Int J Mol Sci 2023; 24:16511. [PMID: 38003701 PMCID: PMC10671888 DOI: 10.3390/ijms242216511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
Drug repurposing is a widely used approach originally developed to aid in the identification of new uses of already existing drugs outside the scope of the original medical indication [...].
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Affiliation(s)
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy;
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Khan M, Kandwal S, Fayne D. DataPype: A Fully Automated Unified Software Platform for Computer-Aided Drug Design. ACS OMEGA 2023; 8:39468-39480. [PMID: 37901539 PMCID: PMC10601415 DOI: 10.1021/acsomega.3c05207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 09/26/2023] [Indexed: 10/31/2023]
Abstract
With the advent of computer-aided drug design (CADD), traditional physical testing of thousands of molecules has now been replaced by target-focused drug discovery, where potentially bioactive molecules are predicted by computer software before their physical synthesis. However, despite being a significant breakthrough, CADD still faces various limitations and challenges. The increasing availability of data on small molecules has created a need to streamline the sourcing of data from different databases and automate the processing and cleaning of data into a form that can be used by multiple CADD software applications. Several standalone software packages are available to aid the drug designer, each with its own specific application, requiring specialized knowledge and expertise for optimal use. These applications require their own input and output files, making it a challenge for nonexpert users or multidisciplinary discovery teams. Here, we have developed a new software platform called DataPype, which wraps around these different software packages. It provides a unified automated workflow to search for hit compounds using specialist software. Additionally, multiple virtual screening packages can be used in the one workflow, and if different ways of looking at potential hit compounds all predict the same set of molecules, we have higher confidence that we should make or purchase and test the molecules. Importantly, DataPype can run on computer servers, speeding up the virtual screening for new compounds. Combining access to multiple CADD tools within one interface will enhance the early stage of drug discovery, increase usability, and enable the use of parallel computing.
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Affiliation(s)
- Mohemmed
Faraz Khan
- Molecular
Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
- Department
of Pharmaceutical Chemistry, Faculty of Pharmacy, Integral University, Lucknow U.P., 226026, India
| | - Shubhangi Kandwal
- Molecular
Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
| | - Darren Fayne
- Molecular
Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin 2, Ireland
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Bolz SN, Schroeder M. Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances. Expert Opin Drug Discov 2023; 18:973-985. [PMID: 37489516 DOI: 10.1080/17460441.2023.2239700] [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: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
INTRODUCTION Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.
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Affiliation(s)
- Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
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Bernal L, Pinzi L, Rastelli G. Identification of Promising Drug Candidates against Prostate Cancer through Computationally-Driven Drug Repurposing. Int J Mol Sci 2023; 24:ijms24043135. [PMID: 36834548 PMCID: PMC9964599 DOI: 10.3390/ijms24043135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/02/2023] [Accepted: 02/03/2023] [Indexed: 02/08/2023] Open
Abstract
Prostate cancer (PC) is one of the most common types of cancer in males. Although early stages of PC are generally associated with favorable outcomes, advanced phases of the disease present a significantly poorer prognosis. Moreover, currently available therapeutic options for the treatment of PC are still limited, being mainly focused on androgen deprivation therapies and being characterized by low efficacy in patients. As a consequence, there is a pressing need to identify alternative and more effective therapeutics. In this study, we performed large-scale 2D and 3D similarity analyses between compounds reported in the DrugBank database and ChEMBL molecules with reported anti-proliferative activity on various PC cell lines. The analyses included also the identification of biological targets of ligands with potent activity on PC cells, as well as investigations on the activity annotations and clinical data associated with the more relevant compounds emerging from the ligand-based similarity results. The results led to the prioritization of a set of drugs and/or clinically tested candidates potentially useful in drug repurposing against PC.
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Affiliation(s)
- Leonardo Bernal
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, 41125 Modena, Italy
| | - Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Giuseppe Campi 103, 41125 Modena, Italy
- Correspondence: ; Tel.: +39-059-2058564
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Moi D, Citarella A, Bonanni D, Pinzi L, Passarella D, Silvani A, Giannini C, Rastelli G. Synthesis of potent and selective HDAC6 inhibitors led to unexpected opening of a quinazoline ring. RSC Adv 2022; 12:11548-11556. [PMID: 35425078 PMCID: PMC9006239 DOI: 10.1039/d2ra01753a] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/06/2022] [Indexed: 12/14/2022] Open
Abstract
Histone deacetylase (HDAC) inhibitors are highly involved in the regulation of many pharmacological responses, which results in anti-inflammatory and anti-cancer effects. In the present work, chemoinformatic analyses were performed to obtain two potent and selective aminotriazoloquinazoline-based HDAC6 inhibitors. We unexpectedly obtained an aminotriazole from a water-driven ring opening of the triazoloquinazoline scaffold. Both compounds were evaluated as HDAC6 inhibitors, resulting in subnanomolar inhibitory activity and high selectivity with respect to class I HDAC1 and HDAC8. Importantly, the compounds were about 3- and 15-fold more potent compared to the reference compound trichostatin A. Additionally, the predicted binding modes were investigated with docking. Considering that the aminotriazole scaffold has never been embedded into the chemical structure of HDAC6 inhibitors, the present study suggests that both the aminotriazoloquinazoline and aminotriazole classes of compounds could be excellent starting points for further optimization of potential anticancer compounds, introducing such novel groups into a relevant and new area of investigation. Potent and selective inhibitors of HDAC6 have been designed, synthesized and tested. An unexpected opening of the quinazoline ring led to both aminotriazoloquinazoline and aminotriazole compounds with potent activity and isoform selectivity.![]()
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Affiliation(s)
- Davide Moi
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 103 Modena 41125 Italy .,Department of Chemistry, University of Milan Via Golgi 19 Milano 20133 Italy
| | - Andrea Citarella
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 103 Modena 41125 Italy .,Department of Chemistry, University of Milan Via Golgi 19 Milano 20133 Italy
| | - Davide Bonanni
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 103 Modena 41125 Italy
| | - Luca Pinzi
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 103 Modena 41125 Italy
| | - Daniele Passarella
- Department of Chemistry, University of Milan Via Golgi 19 Milano 20133 Italy
| | - Alessandra Silvani
- Department of Chemistry, University of Milan Via Golgi 19 Milano 20133 Italy
| | - Clelia Giannini
- Department of Chemistry, University of Milan Via Golgi 19 Milano 20133 Italy
| | - Giulio Rastelli
- Department of Life Sciences, University of Modena and Reggio Emilia Via Campi 103 Modena 41125 Italy
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Ciriaco F, Gambacorta N, Alberga D, Nicolotti O. Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach. J Chem Inf Model 2021; 61:4868-4876. [PMID: 34570498 DOI: 10.1021/acs.jcim.1c00498] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. Quantitative bioactivity predictions are made on the basis of the statistical distributions of multiple Tanimoto similarity θ values, calculated through 13 different molecular fingerprints, and of the variation of the measured biological activity, reported as ΔpIC50, for all of the ligands sharing a given protein drug target. The application data set comprises as much as 4241 protein drug targets as well as 418 485 ligands selected from ChEMBL (release 25) by employing a set of well-defined filtering rules. Several large internal and external validation studies were carried out to demonstrate the robustness and the predictive potential of the herein proposed method. Additional comparative studies, carried out on two freely available and well-known ligand-target prediction platforms, demonstrated the reliability of our proposed approach for accurate ligand-target matching. Moreover, two applicative cases were also discussed to practically describe how to use our predictive algorithm, which is freely available as a user-friendly web platform. The user can screen single or multiple queries at a time and retrieve the output as a terse html table or as a json file including all of the information concerning the explored similarities to obtain a deeper understanding of the results. High-throughput virtual reverse screening campaigns, allowing for a given query compound the quick detection of the potential drug target from a large collection of them, can be carried out in batch on demand.
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Affiliation(s)
- Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
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Recent Advances in In Silico Target Fishing. Molecules 2021; 26:molecules26175124. [PMID: 34500568 PMCID: PMC8433825 DOI: 10.3390/molecules26175124] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/14/2021] [Accepted: 08/18/2021] [Indexed: 12/24/2022] Open
Abstract
In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.
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