arXiv subjects

Xingcai Zhang

Publications and source records attributed to Xingcai Zhang.

4 recordsLinked to original sources

arXiv2026

Towards World Models in Biomedical Research

A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention. Although foundation models and large language models have accelerated biomedical data interpretation, most current systems remain focused on static pattern recognition rather than prospective simulation of biological futures. Here we propose biomedical world models as a paradigm for AI-driven discovery. These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics that allow future trajectories to be simulated before actions are taken. We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation. We outline the data infrastructure, evaluation benchmarks, safety constraints and governance frameworks required. Biomedical world models may provide a foundation for simulation-guided, closed-loop and experimentally actionable biomedical discovery.

cs.AI↗

arXiv2026

Designing the Haystack: Programmable Chemical Space for Generative Molecular Discovery

Chemical space exploration underlies drug discovery, yet most generative models treat chemical space as a fixed, implicitly learned distribution, focusing on sampling molecules rather than deliberately designing the space itself. We introduce SpaceGFN, a generative framework that elevates chemical space to a programmable computational object: a controllable degree of freedom enabling explicit construction and adaptive traversal of structured molecular universes. SpaceGFN decouples space definition from exploration. Users specify building blocks and reaction rules to construct chemically and synthetically coherent spaces, while a GFlowNet performs efficient, property-biased sampling within them. In Discovery mode, we demonstrate programmable space design through two strategies. A pseudo-natural product space assembles natural product-like architectures. An evolution-inspired (Evo) space recombines endogenous metabolite fragments via enzyme-consistent transformations, introducing an evolutionary prior into chemical generation. This bias yields favorable shifts in predicted metabolic and toxicological profiles while preserving pharmacological diversity, supported by broad docking enrichment across therapeutic targets. In Editing mode, SpaceGFN enables reaction-consistent lead optimization through a curated toolkit of executable synthetic transformations, allowing local, synthesis-aware modification of existing compounds instead of unrestricted graph mutation. Across 96 drug targets, SpaceGFN achieves strong optimization performance while maintaining structural diversity under synthetic constraints. By integrating programmable chemical universe construction with flow-based exploration and reaction-level editing, SpaceGFN establishes a general paradigm for deliberate navigation of therapeutic chemical space.

physics.chem-ph↗

arXiv2025

Discovery of novel antimicrobial peptides with notable antibacterial potency by a LLM-based foundation model

Large language models (LLMs) have shown remarkable advancements in chemistry and biomedical research, acting as versatile foundation models for various tasks. We introduce AMP-Designer, an LLM-based approach for swiftly designing novel antimicrobial peptides (AMPs) with desired properties. Within 11 days, AMP-Designer achieved the de novo design of 18 AMPs with broad-spectrum activity against Gram-negative bacteria. In vitro validation revealed a 94.4% success rate, with two candidates demonstrating exceptional antibacterial efficacy, minimal hemotoxicity, stability in human plasma, and low potential to induce resistance, as evidenced by significant bacterial load reduction in murine lung infection experiments. The entire process, from design to validation, concluded in 48 days. AMP-Designer excels in creating AMPs targeting specific strains despite limited data availability, with a top candidate displaying a minimum inhibitory concentration of 2.0 μg/ml against Propionibacterium acnes. Integrating advanced machine learning techniques, AMP-Designer demonstrates remarkable efficiency, paving the way for innovative solutions to antibiotic resistance.

q-bio.BM↗

arXiv2024

Nano-confinement induced nucleation of ice-VII at room temperature

The hydrogen bond (HB) network of water under confinement has been predicted to have distinct structures from that of bulk water. However, direct measurement of the structure has not been achieved. Here, we present experimental evidence of confinement-induced ice formation in water. We directly probe the HB network of a water nano-meniscus formed and confined between a mica substrate and a precisely-controlled-plasmonically active silver tip. By employing tip-enhanced Raman spectroscopy (TERS), we observe a novel double donor-double acceptor (DDAA) peak that emerges in the OH stretching band of water molecules at room temperature and at sub-nanometer confinement. This Raman peak indicates the presence of a solid phase of water, namely ice-VII with the body-centered cubic (bcc) unit. Interestingly, we observe a structural transition from bcc DDAA (ice-VII) to tetrahedral DDAA as the confinement is weakened. Moreover, by identifying the spatial distribution of the HB network, we find that the bcc DDAA network of ice-VII is predominantly present within the interior of the confined water, rather than at air/water or at solid/water interfaces. This suggest the possibility that the appearance of ice-VII in the strongly confined space could be a general characteristic of water under extreme confinement.

cond-mat.soft↗