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Manen-Freixa L, Antolin AA. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery. Expert Opin Drug Discov 2024:1-27. [PMID: 39004919 DOI: 10.1080/17460441.2024.2376643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology. AREAS COVERED This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples. EXPERT OPINION Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.
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Affiliation(s)
- Leticia Manen-Freixa
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Albert A Antolin
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Center for Cancer Drug Discovery, The Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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Khan MK, Raza M, Shahbaz M, Hussain I, Khan MF, Xie Z, Shah SSA, Tareen AK, Bashir Z, Khan K. The recent advances in the approach of artificial intelligence (AI) towards drug discovery. Front Chem 2024; 12:1408740. [PMID: 38882215 PMCID: PMC11176507 DOI: 10.3389/fchem.2024.1408740] [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: 03/29/2024] [Accepted: 04/26/2024] [Indexed: 06/18/2024] Open
Abstract
Artificial intelligence (AI) has recently emerged as a unique developmental influence that is playing an important role in the development of medicine. The AI medium is showing the potential in unprecedented advancements in truth and efficiency. The intersection of AI has the potential to revolutionize drug discovery. However, AI also has limitations and experts should be aware of these data access and ethical issues. The use of AI techniques for drug discovery applications has increased considerably over the past few years, including combinatorial QSAR and QSPR, virtual screening, and denovo drug design. The purpose of this survey is to give a general overview of drug discovery based on artificial intelligence, and associated applications. We also highlighted the gaps present in the traditional method for drug designing. In addition, potential strategies and approaches to overcome current challenges are discussed to address the constraints of AI within this field. We hope that this survey plays a comprehensive role in understanding the potential of AI in drug discovery.
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Affiliation(s)
- Mahroza Kanwal Khan
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Mohsin Raza
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Muhammad Shahbaz
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
| | - Iftikhar Hussain
- Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong SAR, China
- A. J. Drexel Nanomaterials Institute and Department of Materials Science and Engineering, Drexel University, Philadelphia, PA, United States
| | - Muhammad Farooq Khan
- Department of Electrical Engineering, Sejong University, Seoul, Republic of Korea
| | - Zhongjian Xie
- Shenzhen Children's Hospital, Clinical Medical College of Southern University of Science and Technology, Shenzhen, China
| | - Syed Shoaib Ahmad Shah
- Department of Chemistry, School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Ayesha Khan Tareen
- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, China
| | - Zoobia Bashir
- College of Chemistry and Environmental Engineering, Shenzhen University, Shenzhen, China
| | - Karim Khan
- Additive Manufacturing Institute, Shenzhen University, Shenzhen, China
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Temizer AB, Uludoğan G, Özçelik R, Koulani T, Ozkirimli E, Ulgen KO, Karali N, Özgür A. Exploring data-driven chemical SMILES tokenization approaches to identify key protein-ligand binding moieties. Mol Inform 2024; 43:e202300249. [PMID: 38196065 DOI: 10.1002/minf.202300249] [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: 09/19/2023] [Revised: 11/13/2023] [Accepted: 01/06/2024] [Indexed: 01/11/2024]
Abstract
Machine learning models have found numerous successful applications in computational drug discovery. A large body of these models represents molecules as sequences since molecular sequences are easily available, simple, and informative. The sequence-based models often segment molecular sequences into pieces called chemical words, analogous to the words that make up sentences in human languages, and then apply advanced natural language processing techniques for tasks such as de novo drug design, property prediction, and binding affinity prediction. However, the chemical characteristics and significance of these building blocks, chemical words, remain unexplored. To address this gap, we employ data-driven SMILES tokenization techniques such as Byte Pair Encoding, WordPiece, and Unigram to identify chemical words and compare the resulting vocabularies. To understand the chemical significance of these words, we build a language-inspired pipeline that treats high affinity ligands of protein targets as documents and selects key chemical words making up those ligands based on tf-idf weighting. The experiments on multiple protein-ligand affinity datasets show that despite differences in words, lengths, and validity among the vocabularies generated by different subword tokenization algorithms, the identified key chemical words exhibit similarity. Further, we conduct case studies on a number of target to analyze the impact of key chemical words on binding. We find that these key chemical words are specific to protein targets and correspond to known pharmacophores and functional groups. Our approach elucidates chemical properties of the words identified by machine learning models and can be used in drug discovery studies to determine significant chemical moieties.
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Affiliation(s)
- Asu Busra Temizer
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, İstanbul University, İstanbul, Turkey
- Department of Pharmaceutical Chemistry, Institute of Health Sciences, İstanbul University, İstanbul, Turkey
| | - Gökçe Uludoğan
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Rıza Özçelik
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
| | - Taha Koulani
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, İstanbul University, İstanbul, Turkey
- Department of Pharmaceutical Chemistry, Institute of Health Sciences, İstanbul University, İstanbul, Turkey
| | - Elif Ozkirimli
- Science and Research Informatics, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Kutlu O Ulgen
- Department of Chemical Engineering, Boğaziçi University, İstanbul, Turkey
| | - Nilgun Karali
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, İstanbul University, İstanbul, Turkey
| | - Arzucan Özgür
- Department of Computer Engineering, Boğaziçi University, İstanbul, Turkey
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Ong WJG, Kirubakaran P, Karanicolas J. Poor Generalization by Current Deep Learning Models for Predicting Binding Affinities of Kinase Inhibitors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.04.556234. [PMID: 37732243 PMCID: PMC10508770 DOI: 10.1101/2023.09.04.556234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The extreme surge of interest over the past decade surrounding the use of neural networks has inspired many groups to deploy them for predicting binding affinities of drug-like molecules to their receptors. A model that can accurately make such predictions has the potential to screen large chemical libraries and help streamline the drug discovery process. However, despite reports of models that accurately predict quantitative inhibition using protein kinase sequences and inhibitors' SMILES strings, it is still unclear whether these models can generalize to previously unseen data. Here, we build a Convolutional Neural Network (CNN) analogous to those previously reported and evaluate the model over four datasets commonly used for inhibitor/kinase predictions. We find that the model performs comparably to those previously reported, provided that the individual data points are randomly split between the training set and the test set. However, model performance is dramatically deteriorated when all data for a given inhibitor is placed together in the same training/testing fold, implying that information leakage underlies the models' performance. Through comparison to simple models in which the SMILES strings are tokenized, or in which test set predictions are simply copied from the closest training set data points, we demonstrate that there is essentially no generalization whatsoever in this model. In other words, the model has not learned anything about molecular interactions, and does not provide any benefit over much simpler and more transparent models. These observations strongly point to the need for richer structure-based encodings, to obtain useful prospective predictions of not-yet-synthesized candidate inhibitors.
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Affiliation(s)
- Wern Juin Gabriel Ong
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA 19111
- Bowdoin College, Brunswick, ME 04011
| | - Palani Kirubakaran
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA 19111
| | - John Karanicolas
- Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA 19111
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Li X, Yang Q, Luo G, Xu L, Dong W, Wang W, Dong S, Wang K, Xuan P, Gao X. SAGDTI: self-attention and graph neural network with multiple information representations for the prediction of drug-target interactions. BIOINFORMATICS ADVANCES 2023; 3:vbad116. [PMID: 38282612 PMCID: PMC10818136 DOI: 10.1093/bioadv/vbad116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/31/2023] [Accepted: 08/24/2023] [Indexed: 01/30/2024]
Abstract
Motivation Accurate identification of target proteins that interact with drugs is a vital step in silico, which can significantly foster the development of drug repurposing and drug discovery. In recent years, numerous deep learning-based methods have been introduced to treat drug-target interaction (DTI) prediction as a classification task. The output of this task is binary identification suggesting the absence or presence of interactions. However, existing studies often (i) neglect the unique molecular attributes when embedding drugs and proteins, and (ii) determine the interaction of drug-target pairs without considering biological interaction information. Results In this study, we propose an end-to-end attention-derived method based on the self-attention mechanism and graph neural network, termed SAGDTI. The aim of this method is to overcome the aforementioned drawbacks in the identification of DTI. SAGDTI is the first method to sufficiently consider the unique molecular attribute representations for both drugs and targets in the input form of the SMILES sequences and three-dimensional structure graphs. In addition, our method aggregates the feature attributes of biological information between drugs and targets through multi-scale topologies and diverse connections. Experimental results illustrate that SAGDTI outperforms existing prediction models, which benefit from the unique molecular attributes embedded by atom-level attention and biological interaction information representation aggregated by node-level attention. Moreover, a case study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shows that our model is a powerful tool for identifying DTIs in real life. Availability and implementation The data and codes underlying this article are available in Github at https://github.com/lixiaokun2020/SAGDTI.
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Affiliation(s)
- Xiaokun Li
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Qiang Yang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Gongning Luo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Long Xu
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
| | - Weihe Dong
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Harbin 150090, China
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Wei Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Suyu Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Kuanquan Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
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