<i>(Invited)</i> P-Bits and Application of P-Bit-Based Ising Models to Protein Folding and Molecular Docking Problems

The computational complexity of biological phenomena, exemplified by protein folding and molecular docking, presents significant challenges due to their inherently non-deterministic polynomial-time (NP)-complete characteristics. Recent advancements in probabilistic computing, particularly probabilistic bits (p-bits), offer promising avenues for addressing these complex biological computations through efficient implementation of Ising models. This presentation discusses two pioneering studies demonstrating the efficacy of p-bit-based probabilistic computing systems in solving the 3-dimensional (3-D) protein folding problem and molecular docking, both crucial in biomedical research and pharmaceutical discovery. In addressing the protein folding challenge, a novel p-bit-based probabilistic computational approach has been developed. This approach leverages the well-established hydrophobic-polar (HP) lattice model, extended into 3-D space, to systematically encode amino acid sequences and their spatial constraints into an Ising framework. A sophisticated encoding scheme involving many-body interactions significantly streamlines the energy landscape, reducing complexity and enhancing computational efficiency. Simulation results indicate marked improvements in identifying correct folding configurations, notably demonstrating a substantial reduction—approximately half—in the total number of energy levels for shorter peptide sequences. Furthermore, this approach successfully predicts optimal configurations for peptide sequences containing up to 36 amino acids, reinforcing the robustness and scalability of p-bit probabilistic circuits (p-circuits) in solving biologically pertinent NP-complete problems. Complementing this work, we introduce the first application of p-bit-based probabilistic computing to molecular docking, a critical process in the elucidation of ligand-target interactions essential for drug discovery. Traditional docking methodologies frequently encounter significant obstacles due to the complex combinatorial nature of ligand-receptor interactions. Here, the molecular docking problem is recast as a Maximum Weighted Clique (MWC) optimization within graph theory, permitting its translation into an Ising model that p-circuits can efficiently resolve. Application of this innovative methodology to practical cases, including docking interactions involving the LolA-LolCDE lipoprotein complex and the AF9 YEATS domain with cyclopeptide inhibitors, demonstrates superior accuracy and computational efficiency compared to established quantum-based computational approaches, such as Gaussian Boson Sampling (GBS) and Quantum Approximate Optimization Algorithms (QAOA). Specifically, this p-bit-based method achieved an impressive success rate of approximately 84% in accurately predicting optimal docking conformations. Collectively, these studies underscore the transformative potential of probabilistic computing utilizing p-bits. The successful application of p-circuits to complex biological problems not only highlights their suitability and adaptability to large-scale biological computations but also establishes a foundation for future methodological innovations. By integrating probabilistic computing with biological research, this emerging computational paradigm holds substantial promise for significantly enhancing computational accuracy, efficiency, and capacity in biomedical science and pharmacological discovery.

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