Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914078194139136 |
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| author | Liu, Xuefeng Jiang, Songhao Duan, Xiaotian Vasan, Archit Huang, Qinan Liu, Chong Li, Michelle M. Ma, Heng Brettin, Thomas Ramanathan, Arvind Xia, Fangfang Wang, Mengdi Pandey, Abhishek Zitnik, Marinka Foster, Ian T. Xu, Jinbo Stevens, Rick L. |
| author_facet | Liu, Xuefeng Jiang, Songhao Duan, Xiaotian Vasan, Archit Huang, Qinan Liu, Chong Li, Michelle M. Ma, Heng Brettin, Thomas Ramanathan, Arvind Xia, Fangfang Wang, Mengdi Pandey, Abhishek Zitnik, Marinka Foster, Ian T. Xu, Jinbo Stevens, Rick L. |
| contents | Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein. Binding affinity, which characterizes the strength of biomolecular interactions, is essential for tackling diverse challenges in life sciences, including therapeutic design, protein engineering, enzyme optimization, and elucidating biological mechanisms. Much work has been devoted to predicting binding affinity over the past decades. Here, we review recent significant works, with a focus on methods, evaluation strategies, and benchmark datasets. We note growing use of both traditional machine learning and deep learning models for predicting binding affinity, accompanied by an increasing amount of data on proteins and small drug-like molecules. With improved predictive performance and the FDA's phasing out of animal testing, AI-driven in silico models, such as AI virtual cells (AIVCs), are poised to advance binding affinity prediction; reciprocally, progress in building binding affinity predictors can refine AIVCs. Future efforts in binding affinity prediction and AI-driven in silico models can enhance the simulation of temporal dynamics, cell-type specificity, and multi-omics integration to support more accurate and personalized outcomes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_00709 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches Liu, Xuefeng Jiang, Songhao Duan, Xiaotian Vasan, Archit Huang, Qinan Liu, Chong Li, Michelle M. Ma, Heng Brettin, Thomas Ramanathan, Arvind Xia, Fangfang Wang, Mengdi Pandey, Abhishek Zitnik, Marinka Foster, Ian T. Xu, Jinbo Stevens, Rick L. Quantitative Methods Artificial Intelligence Machine Learning Protein-ligand binding is the process by which a small molecule (drug or inhibitor) attaches to a target protein. Binding affinity, which characterizes the strength of biomolecular interactions, is essential for tackling diverse challenges in life sciences, including therapeutic design, protein engineering, enzyme optimization, and elucidating biological mechanisms. Much work has been devoted to predicting binding affinity over the past decades. Here, we review recent significant works, with a focus on methods, evaluation strategies, and benchmark datasets. We note growing use of both traditional machine learning and deep learning models for predicting binding affinity, accompanied by an increasing amount of data on proteins and small drug-like molecules. With improved predictive performance and the FDA's phasing out of animal testing, AI-driven in silico models, such as AI virtual cells (AIVCs), are poised to advance binding affinity prediction; reciprocally, progress in building binding affinity predictors can refine AIVCs. Future efforts in binding affinity prediction and AI-driven in silico models can enhance the simulation of temporal dynamics, cell-type specificity, and multi-omics integration to support more accurate and personalized outcomes. |
| title | Binding Affinity Prediction: From Conventional to Machine Learning-Based Approaches |
| topic | Quantitative Methods Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2410.00709 |