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Jingjie Zhang

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Biomolecules cond-mat.mes-hall Machine Learning Methodology

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preprint2026arXiv

TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation

Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This is especially important for clinically relevant G protein-coupled receptors (GPCRs), where therapeutic efficacy depends on functional directionality. Structure-based design methods optimize binding to static conformations and cannot represent non-reversible, directional effects or systematically distinguish agonist from antagonist behavior. To address this gap, we introduce Transition-Directed Discrete Diffusion for Allosteric Binder Design (TD3B), a sequence-based generative framework that designs binders with specified agonist or antagonist behavior via a directional transition control objective. TD3B combines a target-aware Direction Oracle, a soft binding-affinity gate, and amortized fine-tuning of a pre-trained discrete diffusion model, enabling targeted agonist and antagonist generation decoupled from binding affinity and unattainable by equilibrium-based or inference-only guidance baselines. The code and checkpoints are available at https://huggingface.co/ChatterjeeLab/TD3B.

preprint2022arXiv

Locally anisotropic covariance functions on the sphere

Rapid developments in satellite remote-sensing technology have enabled the collection of geospatial data on a global scale, hence increasing the need for covariance functions that can capture spatial dependence on spherical domains. We propose a general method of constructing nonstationary, locally anisotropic covariance functions on the sphere based on covariance functions in R^3. We also provide theorems that specify the conditions under which the resulting correlation function is isotropic or axially symmetric. For large datasets on the sphere commonly seen in modern applications, the Vecchia approximation is used to achieve higher scalability on statistical inference. The importance of flexible covariance structures is demonstrated numerically using simulated data and a precipitation dataset.

preprint2015arXiv

Role of crystal structure and junction morphology on interface thermal conductance

We argue that the relative thermal conductance between interfaces with different morphologies is controlled by crystal structure through $M_{min}/M_c > 1$ , the ratio between the {\it minimum mode} count on either side $M_{min}$ , and the {\it conserving modes} $M_c$ that preserve phonon momentum transverse to the interface. Junctions with an added homogenous layer, "uniform", and "abrupt" junctions are limited to $M_c$ while junctions with interfacial disorder, "mixed", exploit the expansion of mode spectrum to $M_{min}$ . In our studies with cubic crystals, the largest enhancement of conductance from "abrupt" to "mixed" interfaces seems to be correlated with the emergence of voids in the conserving modes, where $M_c = 0$ . Such voids typically arise when the interlayer coupling is weakly dispersive, making the bands shift rigidly with momentum. Interfacial mixing also increases alloy scattering, which reduces conductance in opposition with the mode spectrum expansion. Thus the conductance across a "mixed' junction does not always increase relative to that at a "uniform" interface.

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