<i>(Invited)</i> P-Bits and Application of P-Bit-Based Ising Models to Protein Folding and Molecular Docking Problems
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".