Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2511.05904

The Role and Mechanism of Deep Statistical Machine Learning In Biological Target Screening and Immune Microenvironment Regulation of Asthma

Pengwei Zhu
View original source ↗Read source document ↗Export metadata ↓

Abstract

As an important source of small molecule drugs, natural products show remarkable biological activities with their rich types and unique structures. However, due to the limited number of samples and structural complexity, the rapid discovery of lead compounds is limited. Therefore, in this study, natural inhibitors of phosphodiesterase 4 (PDE4) and Phosphodiesterase 7 (PDE7) were screened by combining computer aided drug design (CADD) technology and deep learning method, and their activities were verified by enzyme activity experiment and enzymo-linked immunoassay. These two enzymes have important application potential in the treatment of inflammatory diseases such as chronic obstructive pulmonary disease and asthma, but PDE4 inhibitors may cause adverse reactions, so it is particularly important to develop both effective and safe dual-target inhibitors. In addition, as a potential target of hyperuricemia, the development of natural inhibitors of xanthine oxidase (X0) is also of great value. We used pharmacophore technology for virtual screening, combined with molecular docking technology to improve accuracy, and finally selected 16 potential natural inhibitors of PDE4/7, and verified their binding stability through molecular dynamics simulation. The results of this study laid a foundation for establishing an efficient dual-target inhibitor screening system and exploring the lead compounds of novel X0 inhibitors.

Explore related subjects

cs.SIphysics.chem-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pengwei Zhu. 2025-11-08. The Role and Mechanism of Deep Statistical Machine Learning In Biological Target Screening and Immune Microenvironment Regulation of Asthma. https://arxiv.org/abs/2511.05904

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Link prediction in complex networks via fusing node centrality and local similarity indices

Local similarity indices are widely used in link prediction on complex networks owing to their low computational cost; however, in sparse networks they assign a zero score to every node pair lacking common neighbors, which severely limits their predictive power. A natural remedy is to fuse node centrality indices with local similarity indices: the former provide global importance for the node pair, while the latter capture fine-grained local topology, and the two can be combined into complementary scores within a unified framework. This paper uses PageRank and DomiRank as two representative centrality measures and constructs a centrality--local-similarity fusion framework. The PageRank-based fusion proposed by Charikhi is first generalized to seven classical local similarity indices, and the universality of its improvement is systematically verified on nine real-world network datasets. Furthermore, the DomiRank centrality is introduced to build the DR-MD series of fused indices under a unified weighting coefficient, which overcomes the drawback that the PageRank-based fusion requires index-by-index weight tuning. Results of five-fold cross-validation together with Wilcoxon signed-rank tests show that, under the unified experimental protocol, all DR-MD indices consistently outperform the corresponding local baselines and their PR-MD counterparts on all nine datasets ($p=0.002$), and that the improvements remain robust against perturbations of $\sigma$ and the weighting coefficients within the near-critical parameter plateau; in particular, DR-RA achieves an average AUC of 0.7084, surpassing global methods such as Katz and RWR as well as several advanced similarity indices. The framework is inherently extensible, and its fusion paradigm can be straightforwardly generalized to couple other node centrality indices with local similarity indices.

cs.SI↗
arXiv2026

Chance, Persistent Advantage, and the Generative-AI Era in Open-Source Package Careers

Studies of careers in science, film, music, and books report a common pattern. When a person's most successful work arrives is close to a random draw over the works they produce. How large their successes tend to be, in contrast, follows a stable, person-specific factor. We test whether this pattern holds for open-source software careers and whether it changed when generative AI coding tools arrived. From the complete public record of GitHub push events (2015-2025), we reconstruct 102.2M career works by 6.15M contributors, and for the 908k contributors whose repositories publish packages, we measure each work's impact by how many downstream packages come to depend on it. First, we find that the timing of a career's biggest hit is close to a lottery over their works, as in science and the arts, with a small, replicable lean toward early career that grows as careers get longer. Second, some coders reliably produce higher-impact work than others, but this lasting personal factor accounts for only part of why impact persists (about a fifth in our primary specification); the rest behaves like momentum, success feeding on itself for a period of time. Third, within the same contributors, this structure did not change after ChatGPT's release. The stable factor's weight grew by about as much as it grew for an earlier cohort that simply aged, and subtracting the effect of aging from the effect of generative AI puts the shift at +0.03 (95% CI [-0.22, +0.23]), indistinguishable from zero. The success pattern documented in science and the arts therefore describes open-source careers too, and it shows no detectable break across the arrival of generative AI. These results have implications for how track records on open platforms should be read and on what to expect from generative AI for the careers built on them.

cs.SI↗
arXiv2026

How neighbourhood ideology shapes misinformation belief in densely tied social networks

With the rapid spread of news on social media, understanding the propagation of misinformation is becoming increasingly important. One factor that affects individuals' vulnerability to false information is their ideological predisposition. Despite the large number of agent-based models that focus on social influence as a driver of the spread of false claims, they often fail to explicitly integrate personal ideological biases into belief formation. In this work, we explore how misinformation spreads through the interaction between individuals' ideological biases and social influence. Our model accounts for both the strength of individuals' ideological biases and the extent to which a false claim aligns with their ideology. Social influence modifies the effects of ideological intensity and false claim alignment through network interactions. Notably, the influence of neighbours' ideological intensity on belief is strongly affected by how well those neighbours are connected to one another. These results highlight the importance of considering both network structure and personal ideological biases when modelling misinformation propagation.

cs.SI↗