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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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Scalable Generation of Mature Cerebellar Organoids from Human Pluripotent Stem Cells and Characterization by Immunostaining10:40

Scalable Generation of Mature Cerebellar Organoids from Human Pluripotent Stem Cells and Characterization by Immunostaining

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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

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

620
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.
620
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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Updated: Jan 20, 2026

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

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サンプル効率的な新規分子設計のための生成的多目的ベイズ最適化とスケーラブルなバッチ評価

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
まとめ

この研究は、複数の目的を持つ分子を効率的に設計するための新しい機械学習フレームワークを紹介します。このアプローチは、有機カソード材料などの新しい材料の発見を加速します。

キーワード:
機械学習分子設計ベイズ最適化多目的最適化生成モデル化学情報学

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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Last Updated: Jan 20, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

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科学分野:

  • 計算化学と材料科学。
  • 分子発見における機械学習の応用。

背景:

  • 分子空間の広大さとシミュレーションコストのため、複数の目的を持つ分子の設計は困難です。
  • 既存の機械学習手法は、連続的な潜在空間のスケーラビリティの問題に直面することがよくあります。

研究 の 目的:

  • 新規多目的分子設計のためのモジュラー「生成して最適化する」フレームワークを開発します。
  • 限られたデータを使用した分子発見の効率とスケーラビリティを向上させます。

主な方法:

  • 生成モデルを利用して多様な分子候補を作成します。
  • 最適なバッチ選択のための新しい取得関数、qPMHI（マルチポイント確率最大超体積改善）を導入します。
  • 確率ランキングに基づいたスケーラブルなバッチ選択のためにモンテカルロサンプリングを採用します。

主要な成果:

  • 合成ベンチマークおよび応用駆動タスクにおける最先端の方法に対する大幅な改善を実証します。
  • 水性レドックスフロー電池用の新規で多様な高性能有機カソード材料を特定します。
  • qPMHI取得関数は、正確でスケーラブルなバッチ選択を可能にします。

結論:

  • 提案されたフレームワークは、多目的分子発見のための強力で効率的なアプローチを提供します。
  • この方法は、持続可能なエネルギー貯蔵などの特定のアプリケーションの有望な分子候補の特定を加速します。
  • モジュラー設計とスケーラブルな選択プロセスは、以前の方法の主な制限に対処します。