The First Hardware Demonstration of a Universal Programmable RRAM-based Probabilistic Computer for Molecular Docking
arXiv:2503.20254 · doi:10.1038/s41467-025-67309-z
Abstract
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.
The main text comprises 24 pages with 5 figures. The supplementary information includes 9 pages, containing Supplementary Figures S1 to S3 and Supplementary Tables S1 to S9
References in corpus (23)
- Ising formulations of many NP problems
- Gaussian Boson Sampling
- Strawberry Fields: A Software Platform for Photonic Quantum Computing
- Massively Parallel Probabilistic Computing with Sparse Ising Machines
- Dynamic Local Search for the Maximum Clique Problem
- Molecular Docking with Gaussian Boson Sampling
- Using Gaussian Boson Sampling to Find Dense Subgraphs
- A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithms
- Compilation of Fault-Tolerant Quantum Heuristics for Combinatorial Optimization
- Weighted p-bits for FPGA implementation of probabilistic circuits
- Autonomous Probabilistic Coprocessing with Petaflips per Second
- Solving Graph Problems Using Gaussian Boson Sampling
- Biasing the quantum vacuum to control macroscopic probability distributions
- A universal programmable Gaussian Boson Sampler for drug discovery
- Experimental demonstration of an integrated on-chip p-bit core utilizing stochastic Magnetic Tunnel Junctions and 2D-MoS2 FETs
- Spintronics-compatible approach to solving maximum satisfiability problems with probabilistic computing, invertible logic and parallel tempering
- Efficient Probabilistic Computing with Stochastic Perovskite Nickelates
- All-to-all reconfigurability with sparse and higher-order Ising machines
- CMOS-compatible Ising and Potts Annealing Using Single Photon Avalanche Diodes
- Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source
- Parallel Tempering Simulation of the three-dimensional Edwards-Anderson Model with Compact Asynchronous Multispin Coding on GPU
- Probabilistic-Bits based on Ferroelectric Field-Effect Transistors for Stochastic Computing
- GPU-accelerated simulated annealing based on p-bits with real-world device-variability modeling