Aug 2026· Nature Biomedical Engineering· 0 citations· 118 references
Medicine
TL;DR
It is argued that drug development should be reframed from a linear pipeline into an iterative learning system driven by continuous data feedback, and proposed that single-cell molecular readouts from therapeutic perturbations can both de-risk development and deepen mechanistic understanding of immune responses in humans.
This review argues that incorporating environmental perturbations as a foundational component of virtual cell development is necessary to extend these models toward environ-mental and public health applications, and proposes the development of stand-ardized environmental perturbation datasets, integrated and standalone...
Daniel Ukaegbu, Victor Curean, Andreas Bender et al.· ALTEX· 0 citations
Artificial intelligence-driven systems biology approaches could contribute to the development of dynamic, patient-specific computational representations of biological systems, potentially accelerating precision medicine and transforming drug discovery from empirical experimentation toward predictive, mechanism-guided t...
Kumar Selvarajoo· Frontiers in Immunology· 0 citations
A structured view of current advances, persistent gaps, and future directions in AI-enabled pharmaceutical innovation is offered, showing growing convergence between AI and precision therapeutics, but real-world application demands robust validation, collaboration, explainable AI, and global standards.
M. Fareed, S. Shityakov· Quantum Machine Intelligence· 0 citations
The development of therapeutic resistance in solid tumors is not merely an incidental response, but a relentless recapitulation of Darwinian evolution under profound pharmacological pressure. Consequently, traditional reactive clinical paradigms are increasingly demonstrating insurmountable limitations. This review hig...
TMEDRP is a novel two-stage learning framework that integrates tumor-intrinsic signatures and TME-specific components for enhanced clinical drug response prediction and exhibits promising zero-shot generalization across unseen cancer lineages and novel compounds, suggesting its robustness across diverse scenarios.
Ya-Bin Kuang, Hao-Chen Zhao, Yi Luo et al.· Bioinformatics· 0 citations
Glioblastoma (GBM) remains one of the most treatment-refractory malignancies, characterized by profound intratumoral heterogeneity, an immunologically “cold” microenvironment, and rapid adaptive resistance to immune checkpoint inhibition. Despite mechanistic advances targeting PD-1, PD-L1, and CTLA-4 pathways, clinic...
Arpita Mukherjee· Life Medicine· 0 citations
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