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関連する概念動画

Mismatch Repair01:20

Mismatch Repair

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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
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Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

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Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
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Mutations in Microorganisms01:18

Mutations in Microorganisms

477
Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
477
Spontaneous and Induced Mutations01:30

Spontaneous and Induced Mutations

2.0K
Spontaneous mutations arise infrequently during DNA replication due to errors in the process. A key factor behind these errors is tautomeric shifts in nitrogenous bases, where bases transition from keto to enol forms or amino to imino forms. This shift can alter base-pairing rules, leading to mutations. Additionally, reactive oxygen species (ROS) arising from aerobic metabolism can damage DNA, resulting in depurination (loss of a purine base) or depyrimidination (loss of a pyrimidine base).
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In-vitro Mutagenesis01:16

In-vitro Mutagenesis

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To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
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Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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進化型創薬のための分子変異演算子のベンチマーキング

Raúl Acosta Murillo1, Patricio Adrián Zapata-Morin1, José Carlos Ortiz-Bayliss2

  • 1Department of Microbiology and Immunology, School of Biological Sciences, Universidad Autónoma de Nuevo León, Pedro de Alba SN, San Nicolás de los Garza 66455, Nuevo Leon, Mexico.

International journal of molecular sciences
|December 11, 2025
PubMed
まとめ

AI駆動型創薬において適切な分子変異戦略を選択することが重要です。グラフベース遺伝的アルゴリズムは高い有効性と効率を提供しますが、他の戦略は分子の複雑さと生物活性に異なる影響を与えます。

キーワード:
計算支援創薬遺伝的演算子分子変異分子再結合

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Measuring Microbial Mutation Rates with the Fluctuation Assay
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科学分野:

  • 計算化学; バイオインフォマティクス; 創薬

背景:

  • 遺伝的アルゴリズムは創薬のための強力なツールです。分子変異演算子は、化学空間を探索するために不可欠です。これらの演算子を最適化することで、AI駆動型創薬の効率が向上します。

研究 の 目的:

  • 創薬における遺伝的アルゴリズムのための5つの分子変異戦略を比較すること。計算効率、分子有効性、および複雑さへの影響を評価すること。生物活性と構造的保存への影響を評価すること。

主な方法:

  • グラフベース遺伝的アルゴリズム、グラフベース生成モデル、SmilesClickChem、SELFIESトークン、およびSMILESトークン変異を評価しました。計算効率、分子有効性、複雑さ、および構造的保存を評価しました。pIC50効力と生物活性における変異誘発性の変化を分析しました。

主要な成果:

  • グラフベース遺伝的アルゴリズムは、最も高い分子有効性（96.5％）と効率を示しました。SmilesClickChemおよびグラフベース生成モデルは、分子の複雑さを増加させました。SELFIESトークンは、特にSRC標的分子において生物活性を著しく変化させました。

結論:

  • 変異戦略の選択は、有効性、多様性、およびコストのバランスを取りながら、創薬の結果に影響を与えます。グラフベース遺伝的アルゴリズムは、迅速な創薬に適しています。本研究の結果は、分子生成および候補選択のための進化型アルゴリズムの改良を導きます。