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Abavisani M, Khoshrou A, Sahebkar A. Artificial intelligence in drug discovery: A new frontier in the fight against Mycobacterium tuberculosis. Drug Discov Today 2024; 29:103909. [PMID: 38307380 DOI: 10.1016/j.drudis.2024.103909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 02/04/2024]
Affiliation(s)
- Mohammad Abavisani
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Khoshrou
- Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amirhossein Sahebkar
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
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Cirinciani M, Da Pozzo E, Trincavelli ML, Milazzo P, Martini C. Drug Mechanism: A bioinformatic update. Biochem Pharmacol 2024:116078. [PMID: 38402909 DOI: 10.1016/j.bcp.2024.116078] [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: 12/13/2023] [Revised: 02/01/2024] [Accepted: 02/22/2024] [Indexed: 02/27/2024]
Abstract
A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.
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Affiliation(s)
- Martina Cirinciani
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy
| | - Eleonora Da Pozzo
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy
| | - Maria Letizia Trincavelli
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy
| | - Paolo Milazzo
- Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy; Department of Computer Science, University of Pisa, Largo Pontecorvo, 3, 56127 Pisa, Italy
| | - Claudia Martini
- Department of Pharmacy, University of Pisa, via Bonanno 6, 56126 Pisa, Italy; Center for Instrument Sharing University of Pisa (CISUP), Lungarno Pacinotti, 43/44, 56126 Pisa, Italy.
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Ramli AH, Mohd Faudzi SM. Diarylpentanoids, the privileged scaffolds in antimalarial and anti-infectives drug discovery: A review. Arch Pharm (Weinheim) 2023; 356:e2300391. [PMID: 37806761 DOI: 10.1002/ardp.202300391] [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: 07/18/2023] [Revised: 09/17/2023] [Accepted: 09/18/2023] [Indexed: 10/10/2023]
Abstract
Asia is a hotspot for infectious diseases, including malaria, dengue fever, tuberculosis, and the pandemic COVID-19. Emerging infectious diseases have taken a heavy toll on public health and the economy and have been recognized as a major cause of morbidity and mortality, particularly in Southeast Asia. Infectious disease control is a major challenge, but many surveillance systems and control strategies have been developed and implemented. These include vector control, combination therapies, vaccine development, and the development of new anti-infectives. Numerous newly discovered agents with pharmacological anti-infective potential are being actively and extensively studied for their bioactivity, toxicity, selectivity, and mode of action, but many molecules lose their efficacy over time due to resistance developments. These facts justify the great importance of the search for new, effective, and safe anti-infectives. Diarylpentanoids, a curcumin derivative, have been developed as an alternative with better bioavailability and metabolism as a therapeutic agent. In this review, the mechanisms of action and potential targets of antimalarial drugs as well as the classes of antimalarial drugs are presented. The bioactivity of diarylpentanoids as a potential scaffold for a new class of anti-infectives and their structure-activity relationships are also discussed in detail.
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Affiliation(s)
- Amirah H Ramli
- Natural Medicines and Products Research Laboratory, Institute of Bioscience, Universiti Putra Malaysia, Serdang, Malaysia
| | - Siti M Mohd Faudzi
- Natural Medicines and Products Research Laboratory, Institute of Bioscience, Universiti Putra Malaysia, Serdang, Malaysia
- Department of Chemistry, Faculty of Science, Universiti Putra Malaysia, Serdang, Malaysia
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Arnold A, Alexander J, Liu G, Stokes JM. Applications of machine learning in microbial natural product drug discovery. Expert Opin Drug Discov 2023; 18:1259-1272. [PMID: 37651150 DOI: 10.1080/17460441.2023.2251400] [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: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 09/01/2023]
Abstract
INTRODUCTION Natural products (NPs) are a desirable source of new therapeutics due to their structural diversity and evolutionarily optimized bioactivities. NPs and their derivatives account for roughly 70% of approved pharmaceuticals. However, the rate at which novel NPs are discovered has decreased. To accelerate the microbial NP discovery process, machine learning (ML) is being applied to numerous areas of NP discovery and development. AREAS COVERED This review explores the utility of ML at various phases of the microbial NP drug discovery pipeline, discussing concrete examples throughout each major phase: genome mining, dereplication, and biological target prediction. Moreover, the authors discuss how ML approaches can be applied to semi-synthetic approaches to drug discovery. EXPERT OPINION Despite the important role that microbial NPs play in the development of novel drugs, their discovery has declined due to challenges associated with the conventional discovery process. ML is positioned to overcome these limitations given its ability to model complex datasets and generalize to novel chemical and sequence space. Unsurprisingly, ML comes with its own limitations that must be considered for its successful implementation. The authors stress the importance of continuing to build high quality and open access NP datasets to further increase the utility of ML in NP discovery.
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Affiliation(s)
- Autumn Arnold
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
| | - Jeremie Alexander
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
| | - Gary Liu
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
| | - Jonathan M Stokes
- Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, OntarioCanada
- Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton,Ontario, Canada
- David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario Canada
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Liu C, Sun Y, Davis R, Cardona ST, Hu P. ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction. J Cheminform 2023; 15:29. [PMID: 36843022 PMCID: PMC9968697 DOI: 10.1186/s13321-023-00698-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 02/10/2023] [Indexed: 02/27/2023] Open
Abstract
Graph convolutional neural networks (GCNs) have been repeatedly shown to have robust capacities for modeling graph data such as small molecules. Message-passing neural networks (MPNNs), a group of GCN variants that can learn and aggregate local information of molecules through iterative message-passing iterations, have exhibited advancements in molecular modeling and property prediction. Moreover, given the merits of Transformers in multiple artificial intelligence domains, it is desirable to combine the self-attention mechanism with MPNNs for better molecular representation. We propose an atom-bond transformer-based message-passing neural network (ABT-MPNN), to improve the molecular representation embedding process for molecular property predictions. By designing corresponding attention mechanisms in the message-passing and readout phases of the MPNN, our method provides a novel architecture that integrates molecular representations at the bond, atom and molecule levels in an end-to-end way. The experimental results across nine datasets show that the proposed ABT-MPNN outperforms or is comparable to the state-of-the-art baseline models in quantitative structure-property relationship tasks. We provide case examples of Mycobacterium tuberculosis growth inhibitors and demonstrate that our model's visualization modality of attention at the atomic level could be an insightful way to investigate molecular atoms or functional groups associated with desired biological properties. The new model provides an innovative way to investigate the effect of self-attention on chemical substructures and functional groups in molecular representation learning, which increases the interpretability of the traditional MPNN and can serve as a valuable way to investigate the mechanism of action of drugs.
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Affiliation(s)
- Chengyou Liu
- grid.21613.370000 0004 1936 9609Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB Canada
| | - Yan Sun
- grid.21613.370000 0004 1936 9609Department of Computer Science, University of Manitoba, Winnipeg, MB Canada
| | - Rebecca Davis
- grid.21613.370000 0004 1936 9609Department of Chemistry, University of Manitoba, Winnipeg, MB Canada
| | - Silvia T. Cardona
- grid.21613.370000 0004 1936 9609Department of Microbiology, University of Manitoba, Winnipeg, MB Canada ,grid.21613.370000 0004 1936 9609Department of Medical Microbiology & Infectious Disease, University of Manitoba, Winnipeg, MB Canada
| | - Pingzhao Hu
- Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada. .,Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada. .,Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, MB, Canada. .,Department of Biochemistry, Western University, Building Rm. 362, London, ON, N6A 5C1, Canada.
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