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Cichońska A, Ravikumar B, Rahman R. AI for targeted polypharmacology: The next frontier in drug discovery. Curr Opin Struct Biol 2024; 84:102771. [PMID: 38215530 DOI: 10.1016/j.sbi.2023.102771] [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: 09/15/2023] [Revised: 11/30/2023] [Accepted: 12/20/2023] [Indexed: 01/14/2024]
Abstract
In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such drugs were discovered serendipitously, limiting predictability and efficacy, but currently artificial intelligence (AI) offers a transformative solution. Machine learning and deep learning techniques enable modeling protein structures, generating novel compounds, and decoding their polypharmacological effects, opening an avenue for more systematic and predictive multi-target drug design. This review explores the use of AI in identifying synergistic co-targets and delineating them from anti-targets that lead to adverse effects, and then discusses advances in AI-enabled docking, generative chemistry, and proteochemometric modeling of proteome-wide compound interactions, in the context of polypharmacology. We also provide insights into challenges ahead.
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Veleiro U, de la Fuente J, Serrano G, Pizurica M, Casals M, Pineda-Lucena A, Vicent S, Ochoa I, Gevaert O, Hernaez M. GeNNius: an ultrafast drug-target interaction inference method based on graph neural networks. Bioinformatics 2024; 40:btad774. [PMID: 38134424 PMCID: PMC10766589 DOI: 10.1093/bioinformatics/btad774] [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: 06/19/2023] [Revised: 11/20/2023] [Accepted: 12/21/2023] [Indexed: 12/24/2023] Open
Abstract
MOTIVATION Drug-target interaction (DTI) prediction is a relevant but challenging task in the drug repurposing field. In-silico approaches have drawn particular attention as they can reduce associated costs and time commitment of traditional methodologies. Yet, current state-of-the-art methods present several limitations: existing DTI prediction approaches are computationally expensive, thereby hindering the ability to use large networks and exploit available datasets and, the generalization to unseen datasets of DTI prediction methods remains unexplored, which could potentially improve the development processes of DTI inferring approaches in terms of accuracy and robustness. RESULTS In this work, we introduce GeNNius (Graph Embedding Neural Network Interaction Uncovering System), a Graph Neural Network (GNN)-based method that outperforms state-of-the-art models in terms of both accuracy and time efficiency across a variety of datasets. We also demonstrated its prediction power to uncover new interactions by evaluating not previously known DTIs for each dataset. We further assessed the generalization capability of GeNNius by training and testing it on different datasets, showing that this framework can potentially improve the DTI prediction task by training on large datasets and testing on smaller ones. Finally, we investigated qualitatively the embeddings generated by GeNNius, revealing that the GNN encoder maintains biological information after the graph convolutions while diffusing this information through nodes, eventually distinguishing protein families in the node embedding space. AVAILABILITY AND IMPLEMENTATION GeNNius code is available at https://github.com/ubioinformat/GeNNius.
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Affiliation(s)
- Uxía Veleiro
- CIMA University of Navarra, IdiSNA, 31008 Pamplona, Spain
| | - Jesús de la Fuente
- TECNUN, University of Navarra, 20016 San Sebastian, Spain
- Center for Data Science, New York University, New York, NY 10012, United States
| | - Guillermo Serrano
- CIMA University of Navarra, IdiSNA, 31008 Pamplona, Spain
- TECNUN, University of Navarra, 20016 San Sebastian, Spain
| | - Marija Pizurica
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Department Biomedical Data Science, Stanford University, Stanford, CA 94305, United States
- Internet Technology and Data Science LAB (IDLab), Ghent University, Gent 9052, Belgium
| | - Mikel Casals
- TECNUN, University of Navarra, 20016 San Sebastian, Spain
| | | | - Silve Vicent
- CIMA University of Navarra, IdiSNA, 31008 Pamplona, Spain
| | - Idoia Ochoa
- TECNUN, University of Navarra, 20016 San Sebastian, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, 31008 Pamplona, Spain
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Department Biomedical Data Science, Stanford University, Stanford, CA 94305, United States
| | - Mikel Hernaez
- CIMA University of Navarra, IdiSNA, 31008 Pamplona, Spain
- Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, 31008 Pamplona, Spain
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