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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.08966 |
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| _version_ | 1866916840175828992 |
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| author | Liu, Meng Leswing, Karl Chu, Simon K. S. Ramezanghorbani, Farhad Young, Griffin Marques, Gabriel Das, Prerna Panikar, Anjali Jamir, Esther Shamsudeen, Mohammed Sulaiman Watts, K. Shawn Sen, Ananya Devannagari, Hari Priya Miller, Edward B. Lihan, Muyun Hwang, Howook Paulsen, Janet Yu, Xin Gion, Kyle Rvachov, Timur Kucukbenli, Emine Paliwal, Saee Gopal |
| author_facet | Liu, Meng Leswing, Karl Chu, Simon K. S. Ramezanghorbani, Farhad Young, Griffin Marques, Gabriel Das, Prerna Panikar, Anjali Jamir, Esther Shamsudeen, Mohammed Sulaiman Watts, K. Shawn Sen, Ananya Devannagari, Hari Priya Miller, Edward B. Lihan, Muyun Hwang, Howook Paulsen, Janet Yu, Xin Gion, Kyle Rvachov, Timur Kucukbenli, Emine Paliwal, Saee Gopal |
| contents | Protein-ligand binding affinity prediction is essential for drug discovery and toxicity assessment. While machine learning (ML) promises fast and accurate predictions, its progress is constrained by the availability of reliable data. In contrast, physics-based methods such as absolute binding free energy perturbation (AB-FEP) deliver high accuracy but are computationally prohibitive for high-throughput applications. To bridge this gap, we introduce ToxBench, the first large-scale AB-FEP dataset designed for ML development and focused on a single pharmaceutically critical target, Human Estrogen Receptor Alpha (ER$α$). ToxBench contains 8,770 ER$α$-ligand complex structures with binding free energies computed via AB-FEP with a subset validated against experimental affinities at 1.75 kcal/mol RMSE, along with non-overlapping ligand splits to assess model generalizability. Using ToxBench, we further benchmark state-of-the-art ML methods, and notably, our proposed DualBind model, which employs a dual-loss framework to effectively learn the binding energy function. The benchmark results demonstrate the superior performance of DualBind and the potential of ML to approximate AB-FEP at a fraction of the computational cost. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08966 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ToxBench: A Binding Affinity Prediction Benchmark with AB-FEP-Calculated Labels for Human Estrogen Receptor Alpha Liu, Meng Leswing, Karl Chu, Simon K. S. Ramezanghorbani, Farhad Young, Griffin Marques, Gabriel Das, Prerna Panikar, Anjali Jamir, Esther Shamsudeen, Mohammed Sulaiman Watts, K. Shawn Sen, Ananya Devannagari, Hari Priya Miller, Edward B. Lihan, Muyun Hwang, Howook Paulsen, Janet Yu, Xin Gion, Kyle Rvachov, Timur Kucukbenli, Emine Paliwal, Saee Gopal Machine Learning Artificial Intelligence Chemical Physics Biomolecules Protein-ligand binding affinity prediction is essential for drug discovery and toxicity assessment. While machine learning (ML) promises fast and accurate predictions, its progress is constrained by the availability of reliable data. In contrast, physics-based methods such as absolute binding free energy perturbation (AB-FEP) deliver high accuracy but are computationally prohibitive for high-throughput applications. To bridge this gap, we introduce ToxBench, the first large-scale AB-FEP dataset designed for ML development and focused on a single pharmaceutically critical target, Human Estrogen Receptor Alpha (ER$α$). ToxBench contains 8,770 ER$α$-ligand complex structures with binding free energies computed via AB-FEP with a subset validated against experimental affinities at 1.75 kcal/mol RMSE, along with non-overlapping ligand splits to assess model generalizability. Using ToxBench, we further benchmark state-of-the-art ML methods, and notably, our proposed DualBind model, which employs a dual-loss framework to effectively learn the binding energy function. The benchmark results demonstrate the superior performance of DualBind and the potential of ML to approximate AB-FEP at a fraction of the computational cost. |
| title | ToxBench: A Binding Affinity Prediction Benchmark with AB-FEP-Calculated Labels for Human Estrogen Receptor Alpha |
| topic | Machine Learning Artificial Intelligence Chemical Physics Biomolecules |
| url | https://arxiv.org/abs/2507.08966 |