Diffusion models have emerged as a leading framework in generative modeling, poised to transform the traditionally slow and costly process of drug discovery. This review provides a systematic comparison of their application in designing two principal therapeutic modalities: small molecules and therapeutic peptides. We dissect how the unified framework of iterative denoising is adapted to the distinct molecular representations, chemical spaces, and design objectives of each modality. For small molecules, these models excel at structure-based design, generating novel, pocket-fitting ligands with desired physicochemical properties, yet face the critical hurdle of ensuring chemical synthesizability. Conversely, for therapeutic peptides, the focus shifts to generating functional sequences and designing de novo structures, where the primary challenges are achieving biological stability against proteolysis, ensuring proper folding, and minimizing immunogenicity. Despite these distinct challenges, both domains face shared hurdles: the scarcity of high-quality experimental data, the reliance on inaccurate scoring functions for validation, and the crucial need for experimental validation. We conclude that the full potential of diffusion models will be unlocked by bridging these modality-specific gaps and integrating them into automated, closed-loop Design-Build-Test-Learn (DBTL) platforms, thereby shifting the paradigm from mere chemical exploration to the on-demand engineering of novel therapeutics.

The recent study by Wang et al. introduces GLM7, a novel composite index integrating routine health indicators to enhance the diagnosis and prediction of multimorbidity. The study provides compelling evidence for the utility of cost‐effective biomarkers in large‐scale health screening. In this comment, we engage in a constructive discussion regarding the methodological interpretation of the index. Specifically, we explore the implications of incorporating diagnostic criteria (such as fasting blood glucose) into predictive models for diabetes, the mathematical nuance required when comparing Odds Ratios (ORs) between logarithmic indices and linear variables, and the structural contribution of age within the composite formula. We believe that addressing these aspects could further solidify the clinical applicability and interpretability of GLM7.

Flood disasters are characterized by high frequency, severe destructive power, and extensive impact. The prediction of flood disasters holds great significance. This paper proposes a flood disaster prediction model based on a multi-layer perceptron (MLP). Firstly, the model employs the Spearman correlation coefficient and random forest feature importance algorithm to identify the most influential feature indicators. Subsequently, an MLP neural network is established, trained, and optimized. Experimental findings demonstrate that the model accurately forecasts the likelihood of flood disasters through sample processing, achieving a coefficient of determination of approximately 85.27%.

Background: The role of APOE- ε 4 in hippocampal atrophy is contested. We aimed to determine whether it functions as a static risk factor or an amyloid- β (A β )-dependent modulator of neurodegeneration. Methods: We integrated a systematic meta-analysis of 18 studies ( N = 3, 781) with longitudinal validation using linear mixed-effects models in the NACC and ADNI cohorts ( N > 5, 000), employing biomarker stratification to test for gene-pathology interactions. Results: The meta-analysis confirmed significant atrophy in APOE- ε 4 carriers but with high heterogeneity. Longitudinal analysis resolved this by identifying a crucial interaction: in A β -negative individuals, carrier atrophy rates were indistinguishable from non-carriers. However, A β positivity triggered a dramatic, dose-dependent acceleration in atrophy among carriers, with homozygotes declining over three times faster. Conclusions: APOE- ε 4 acts as a potent, conditional accelerator of neurodegeneration, not an independent driver. Its deleterious effect is contingent on the presence of A β pathology. Clinical risk stratification should therefore integrate amyloid status with APOE genotype to accurately predict structural progression.

Flood disasters are characterized by high frequency, severe destructive power, and extensive impact. The prediction of flood disasters holds great significance. This paper proposes a flood disaster prediction model based on a multi-layer perceptron (MLP). Firstly, the model employs the Spearman correlation coefficient and random forest feature importance algorithm to identify the most influential feature indicators. Subsequently, an MLP neural network is established, trained, and optimized. Experimental findings demonstrate that the model accurately forecasts the likelihood of flood disasters through sample processing, achieving a coefficient of determination of approximately 85.27%.

This paper presents a multi-stage crop planting strategy optimization model utilizing the Particle Swarm Optimization (PSO) algorithm. The model aims to address key challenges in agriculture, such as fluctuating market demands, climate variability, and rising production costs. Over a seven-year planning horizon (2024-2030), the model dynamically adjusts crop planting strategies by integrating factors like crop yield, market prices, and crop rotation. The proposed framework optimizes both crop selection and area allocation to maximize economic profitability while maintaining sustainable practices. Unlike traditional static models, this approach accounts for uncertainties and adapts to real-time changes in market conditions. The model's ability to adjust planting strategies based on PSO ensures that farmers can optimize yields and reduce waste through flexible decision-making. Experimental results demonstrate the model's effectiveness in dynamically optimizing planting strategies while satisfying constraints such as land availability and crop rotation.