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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

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Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
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Introduction to Learning01:18

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Related Experiment Video

Updated: Sep 13, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

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A Unified Peptide Generative Framework via a Weakly Order-Dependent Autoregressive Language Model and Lifelong

Zhiwei Nie1,2, Daixi Li3, Yutian Liu4

  • 1School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.

Journal of Chemical Information and Modeling
|August 1, 2025
PubMed
Summary

We developed PepGenWL, a novel framework for generating therapeutic peptides. This deep learning model effectively captures peptide interactions and flexibility, outperforming existing methods in generating antimicrobial and anticancer peptides.

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Drug discovery

Background:

  • Bioactive peptides show therapeutic promise, driving demand for advanced peptide generation models.
  • Current deep generative models struggle with peptide conformational flexibility and residue interactions.

Purpose of the Study:

  • To introduce PepGenWL, a unified framework for peptide generation using a weakly order-dependent autoregressive language model and lifelong learning.
  • To address limitations in existing models regarding conformational flexibility and residue dependencies.

Main Methods:

  • PepGenWL employs an autoregressive language model with tolerance for out-of-order input.
  • Mixture-of-Experts-style plugins balance memory stability and learning plasticity during fine-tuning.
  • The framework was evaluated on generating antimicrobial peptides, anticancer peptides, and peptide binders.

Main Results:

  • PepGenWL surpassed state-of-the-art models in generating therapeutic peptides, including antimicrobial and anticancer types.
  • The model demonstrated an ability to incorporate beneficial residues for bioactivity.
  • A property-guided pipeline achieved a 28.6% target binding rate for peptide binders with specificity.
  • PepGenWL's applicability extends to peptide SMILES, enabling generation of modified and cyclic peptides.

Conclusions:

  • PepGenWL offers a unified and flexible framework for general-purpose peptide generation.
  • The model effectively handles peptide conformational flexibility and residue interactions.
  • PepGenWL shows significant potential for advancing therapeutic peptide discovery and development.