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545 papers
It is revealed that GenAI texts underrepresent the subtle interpersonal cues that give writing its persuasive, dialogic, and ethical texture, though they excel in both grammatical accuracy, and lexical variety.
Current structure-based drug design generative models often struggle to faithfully recapitulate genuine ligand-protein binding interactions. Instead, under the coupling of implicit learning architectures and biased training data, they tend to learn spurious statistical correlations. To address this, we propose EIP-Diff (Explicit Interaction-Prompted Diffusion), an architecture featuring a novel explicit interaction-prompt embedding mechanism that is better suited for real-world target-specific drug design. This architecture replaces biased implicit learning with explicit, residue-level biological guidance, thereby promoting more fine-grained geometric fidelity and more precise interaction-aware conditioning. To fully realize the capabilities of EIP-Diff and provide a reliable basis for performance evaluation, we further constructed CrystalData set, which provides higher-fidelity and less-biased structural supervision than existing data sets. This explicit architecture markedly improves distribution consistency: even when trained on the crossdocked data set, EIP-Diff achieves the highest alignment with authentic pharmacological distributions among evaluated models. Training on CrystalData set further enhances this alignment and improves 3D geometric accuracy, while retaining strong controllability, high chemical space coverage, and near-perfect uniqueness. In addition, target-based validation on KAT6A and YTHDC1 confirmed that EIP-Diff accurately recapitulates native-like binding modes. Furthermore, in a real-world drug design task against IDO1, we successfully designed a novel lead compound with nanomolar potency (IC50 = 0.31 nM). These results demonstrate that the EIP-Diff architecture can explicitly leverage experimentally derived structural data and biologically meaningful interaction information for target-specific molecular generation, thereby enabling its effective application to real-world structure-based drug design.
It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.
This conceptual paper argues that Theatre in Education (TIE), long valued for cultivating empathy, embodied cognition, and critical reflection, offers a necessary human-centred counterweight to the efficiency-oriented logic of AI-driven instruction.
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The reviewed literature indicates that AI-based methodologies often demonstrate superior detection capabilities for intricate and previously unseen attack patterns compared to traditional methods; however, direct performance comparisons are complicated due to discrepancies in datasets, experimental designs, and evaluation protocols.
Nursing education must respond proactively by establishing AI literacy frameworks, revising academic integrity policies, and embedding source verification and citation skills into curricula, as generative AI threatens to erode the scholarly standards essential to both academic rigor and professional nursing practice.
Generative AI in nursing education is an emerging field, and future research should address policy, long-term outcomes, and institutional adoption to ensure responsible integration to ensure responsible integration.
The adoption of generative artificial intelligence among communication practitioners and researchers surged after the launch of ChatGPT in November 2022, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.
MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
Earth AI
Gemini Omni 1.1 Flash brings a new suite of creative controls and generative video capabilities to developers.
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