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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

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We developed computational de novo protein design methods capable of tackling several important areas of protein design. To disseminate these methods we present Protein WISDOM, an online tool for protein design (http://www.proteinwisdom.org). Starting from a structural template, design of monomeric proteins for increased stability and complexes for increased binding affinity can be...
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This protocol describes a dynamic culture system to produce controlled size aggregates of human pluripotent stem cells and further stimulate differentiation in cerebellar organoids under chemically-defined and feeder-free conditions using a single-use...
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow08:58

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This protocol provides a method for the systematic global optimization of genetically encoded biosensors through automation-assisted genetic library generation and assessment. This is coupled with design-of-experiment methodologies to streamline experimentation and enable the selection of genetic components to tune biosensors to specific design outcomes.
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Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain08:48

Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain

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Herein we describe a novel method to generate antigen-specific T cell receptors (TCRs) by pairing the TCRα or TCRβ of an existing TCR, possessing the antigen-specificity of interest, with complementary hemichain of the peripheral T cell receptor repertoire. The de novo generated TCRs retain antigen-specificity with varying...
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Design and Optimization Strategies of a High-Performance Vented Box14:23

Design and Optimization Strategies of a High-Performance Vented Box

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Here, we present the range analysis method to optimize the sample points generated by an orthogonal experimental design to ensure that fresh food can be stored in a vented box for a long time by regulating the airflow...
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Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts10:37

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts

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The design-of-experiments procedure presented here allows the evaluation of different flocculants in terms of their ability to aggregate dispersed particles in plant extracts, thus reducing turbidity and the costs of downstream...
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Video Experimental Relacionado

Updated: Jan 20, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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Optimización Bayesiana Multiobjetivo Generativa con Evaluaciones por Lotes Escalables para el Diseño Molecular De

Madhav R Muthyala1, Farshud Sorourifar2, Tianhong Tan1

  • 1Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.

Industrial & engineering chemistry research
|January 19, 2026
PubMed
Resumen

Este estudio presenta un nuevo marco de aprendizaje automático para el descubrimiento de moléculas que diseña eficientemente moléculas con múltiples objetivos. El enfoque acelera el descubrimiento de materiales novedosos, como los materiales de cátodo orgánico para baterías.

Palabras clave:
optimización bayesianadiseño molecularaprendizaje automáticoquímica computacionaldescubrimiento de materiales

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Área de la Ciencia:

  • Química computacional y ciencia de materiales.
  • Aplicaciones de aprendizaje automático en el descubrimiento molecular.

Sus antecedentes:

  • El diseño de moléculas con múltiples objetivos es un desafío debido al vasto espacio químico y los costos de simulación.
  • Los métodos de aprendizaje automático existentes a menudo enfrentan problemas de escalabilidad con espacios latentes continuos.

Objetivo del estudio:

  • Desarrollar un marco modular de 'generar-then-optimize' para el diseño molecular multiobjetivo de novo.
  • Mejorar la eficiencia y escalabilidad del descubrimiento molecular utilizando datos limitados.

Principales métodos:

  • Utiliza un modelo generativo para crear diversos candidatos moleculares.
  • Introduce una nueva función de adquisición, qPMHI (Probability of Maximum Hypervolume Improvement multipunto), para la selección óptima de lotes.
  • Emplea muestreo de Monte Carlo para una selección de lotes escalable basada en la clasificación de probabilidad.

Principales resultados:

  • Demuestra mejoras significativas sobre los métodos de vanguardia en puntos de referencia sintéticos y tareas impulsadas por aplicaciones.
  • Identifica con éxito materiales de cátodo orgánico novedosos, diversos y de alto rendimiento para baterías de flujo redox acuosas.
  • La función de adquisición qPMHI permite una selección de lotes exacta y escalable.

Conclusiones:

  • El marco propuesto ofrece un enfoque potente y eficiente para el descubrimiento molecular multiobjetivo.
  • Este método acelera la identificación de candidatos moleculares prometedores para aplicaciones específicas, como el almacenamiento de energía sostenible.
  • El diseño modular y el proceso de selección escalable abordan las principales limitaciones de los métodos anteriores.