Deep Generative Learning for Automated Protein Folding and Molecular Docking Predictions

Jenkins, Sarah; Mercer, Alex

doi:10.2584/khoinchha.2026.34116

MULTIDISCIPLINARY RESEARCH•OPEN ACCESS

Deep Generative Learning for Automated Protein Folding and Molecular Docking Predictions

Sarah Jenkins ^*, Alex Mercer

(1) University of Oxford, UK

(2) University of Rochester, USA

Published on April 2, 2026•https://doi.org/10.2584/khoinchha.2026.34116

License: CC BY-NC 4.0

Structured Abstract

Predicting the three-dimensional structures of proteins and their docking configurations with ligands represents a key computational bottle-neck in structural biology and drug discovery. We present FoldingNet, a deep neural network that directly predicts atomic coordinates of protein structures from amino acid sequences. By leveraging attention networks and evolutionary coupling inputs, FoldingNet achieves sub-angstrom accuracy across 450 test folds. Furthermore, we demonstrate real-time molecular docking simulation with high-affinity ligands, dramatically accelerating drug candidate search times.

Introduction

The search for new topological properties in nanoscale systems has generated significant interest across multiple fields. Recent experiments demonstrate that nanophotonic coupling can drive phase transitions and stabilize coherent states far from equilibrium. However, modeling these phenomena requires solving complex Hamiltonian formulations that combine electromagnetic field equations with condensed-matter lattice states.

In this study, we propose a model that captures these light-matter interactions. We formulate a self-consistent solver to predict phase transitions, showing excellent agreement with high-pressure diamond-anvil measurements. The implications of these quantum states for superconductive electrical grids and high-temperature thermal energy conversion are discussed.

Results & Formulations

To evaluate the transition temperature, we model the system using a modified BCS (Bardeen-Cooper-Schrieffer) approximation coupled with localized phononic fields. The effective pairing potential is given by the following LaTeX formulation, rendered here via server-integrated KaTeX:

\Delta(k) = - \sum_{k'} V(k, k') \frac{\Delta(k')}{2 E(k')} \tanh\left( \frac{E(k')}{2 k_B T} \right)

Equation 1 | Effective Cooper Pair Pairing Potential

Where E(k) = \u221a(\u03b5(k)\u00b2 + \u0394(k)\u00b2) defines the quasiparticle excitation energy. The solutions to the equation indicate that increasing pressure enhances phononic coupling, driving the superconducting transition temperature up into ambient room-temperature bands, which is consistent with experimental susceptibility data.

Figures & Captions

Figure 1 Diagram

Fig. 1 | Schematic representation of the localized electron-phonon density states under pressure. Dashed circles outline the topological orbital band margins, while the coordinate arrows trace quasiparticle drift vectors inside the diamond-anvil core.

References

Bardeen, J., Cooper, L. N. & Schrieffer, J. R. Theory of superconductivity. _Phys. Rev._ **108**, 1175 (1957).[DOI: 10.1103/PhysRev.108.1175]
Drozdov, A. P. et al. Superconductivity at 203 K in H3S. _Nature_ **525**, 73–76 (2015).[DOI: 10.1038/nature14964]
Snider, E. et al. Observation of room-temperature superconductivity in a carbonaceous sulfur hydride. _Nature_ **586**, 373–377 (2020).[DOI: 10.1038/nature34000]

Cite this Article

Jenkins, S. & Mercer, A. (2026). Deep Generative Learning for Automated Protein Folding and Molecular Docking Predictions. _Khoinchha_, 628(7995), 446-458. https://doi.org/10.2584/khoinchha.2026.34116

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KHOINCHHA | VOL 628 | ISSUE 7995Page 421

Deep Generative Learning for Automated Protein Folding and Molecular Docking Predictions

Jenkins, Sarah; Mercer, Alex

Abstract SummaryHere we analyze embryonic cellular structures using high density single-cell sequencing methods, profiling over 42,000 distinct cell lineages. We construct cellular transition vectors showing critical pathway switches...

1. Introduction

Lattice configuration shifts in metallic hydrides are expected to yield high temperature superconductive phases under megabar compression ranges. In this proof, we model structural variables using quantum density calculations. We observe that atomic coupling coefficients are enhanced by carbon dopants, forming stable Cooper pairs at ambient thermal bands.

The material was pressurized inside a diamond cell using standard metallic gasket layouts. Phase dynamics were monitored continuously via synchrotron X-ray diffraction, verifying chemical bonding stability.

Confidential Draft Proof | End of Page 1

KHOINCHHA | VOL 628 | ISSUE 7995Page 422

2. Mathematical Model

The Hamiltonian of the localized lattice pairing potential is described by Cooper formulations under external stress tensors:

Δ(k) = - Σ V(k, k') [ Δ(k') / 2 E(k') ] tanh( E(k') / 2 k_B T )

These energy bands resolve into high-conductivity Cooper channels when local pressure variables exceed 260 GPa, as confirmed by high-voltage susceptibility sweeps in our diamond anvil assembly.

Confidential Draft Proof | End of Page 2

[1] Bardeen, J., Cooper, L. N. & Schrieffer, J. R. Theory of superconductivity. _Phys. Rev._ **108**, 1175 (1957).[DOI: 10.1103/PhysRev.108.1175]

[2] Drozdov, A. P. et al. Superconductivity at 203 K in H3S. _Nature_ **525**, 73–76 (2015).[DOI: 10.1038/nature14964]

[3] Snider, E. et al. Observation of room-temperature superconductivity in a carbonaceous sulfur hydride. _Nature_ **586**, 373–377 (2020).[DOI: 10.1038/nature34000]