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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.20254 |
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| _version_ | 1866917968412147712 |
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| author | He, Yihan Hong, Ming-Chun Ding, Qiming Lin, Chih-Sheng Lai, Chih-Ming Fang, Chao Gong, Xiao Hou, Tuo-Hung Liang, Gengchiau |
| author_facet | He, Yihan Hong, Ming-Chun Ding, Qiming Lin, Chih-Sheng Lai, Chih-Ming Fang, Chao Gong, Xiao Hou, Tuo-Hung Liang, Gengchiau |
| contents | Molecular docking is a critical computational strategy in drug design and discovery, but the complex diversity of biomolecular structures and flexible binding conformations create an enormous search space that challenges conventional computing methods. Although quantum computing holds promise for these challenges, it remains constrained by scalability, hardware limitations, and precision issues. Here, we report a prototype of a probabilistic computer (p-computer) that efficiently and accurately solves complex molecular docking for the first time, overcoming previously encountered challenges. At the core of the system is a p-computing chip based upon our artificial tunable probabilistic bits (p-bits), which are compatible with computing-in-memory schemes, based upon 180 nm CMOS technology and BEOL HfO2 RRAM. We successfully demonstrated the superior performance of the p-computer in practical ligand-protein docking scenarios. A 42-node molecular docking problem of lipoprotein with LolA-LolCDE complex-a key point in developing antibiotics against Gram-negative bacteria, was successfully solved. Our results align well with the Protein-Ligand Interaction Profiler tool. This work marks the first application of p-computing in molecular docking-based computational biology, which has great potential to overcome the limitations in success rate and efficiency of current technologies in addressing complex bioinformatics problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20254 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The First Hardware Demonstration of a Universal Programmable RRAM-based Probabilistic Computer for Molecular Docking He, Yihan Hong, Ming-Chun Ding, Qiming Lin, Chih-Sheng Lai, Chih-Ming Fang, Chao Gong, Xiao Hou, Tuo-Hung Liang, Gengchiau Computational Physics Applied Physics Molecular docking is a critical computational strategy in drug design and discovery, but the complex diversity of biomolecular structures and flexible binding conformations create an enormous search space that challenges conventional computing methods. Although quantum computing holds promise for these challenges, it remains constrained by scalability, hardware limitations, and precision issues. Here, we report a prototype of a probabilistic computer (p-computer) that efficiently and accurately solves complex molecular docking for the first time, overcoming previously encountered challenges. At the core of the system is a p-computing chip based upon our artificial tunable probabilistic bits (p-bits), which are compatible with computing-in-memory schemes, based upon 180 nm CMOS technology and BEOL HfO2 RRAM. We successfully demonstrated the superior performance of the p-computer in practical ligand-protein docking scenarios. A 42-node molecular docking problem of lipoprotein with LolA-LolCDE complex-a key point in developing antibiotics against Gram-negative bacteria, was successfully solved. Our results align well with the Protein-Ligand Interaction Profiler tool. This work marks the first application of p-computing in molecular docking-based computational biology, which has great potential to overcome the limitations in success rate and efficiency of current technologies in addressing complex bioinformatics problems. |
| title | The First Hardware Demonstration of a Universal Programmable RRAM-based Probabilistic Computer for Molecular Docking |
| topic | Computational Physics Applied Physics |
| url | https://arxiv.org/abs/2503.20254 |