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Related Concept Videos

Overview of Advanced Functional Groups02:22

Overview of Advanced Functional Groups

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Functional groups are groups of atoms with specific chemical properties that occur within organic molecules and are sometimes denoted as “R”. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.
Types of Advanced Functional Groups
The table below summarizes some of the major functional groups in organic chemistry.
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Introduction to Functional Groups02:08

Introduction to Functional Groups

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Functional groups are group of atoms with specific chemical properties that occur within organic molecules and sometimes denoted as “R”. Functional groups are found along the carbon backbone of macromolecules can form chains or rings of carbon atoms. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.  
Types of common functional groups
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Predicting Molecular Geometry02:27

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Functional Groups02:45

Functional Groups

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Overview of Functional Groups01:19

Overview of Functional Groups

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Functional groups are a group of atoms with characteristic properties, which when linked to the carbon skeleton of a molecule, alter the properties of that molecule. For example, certain functional groups will make a molecule hydrophilic, whereas others will make them hydrophobic. These functional groups are an indispensable part of organic chemistry and important components of biological molecules, such as carbohydrates, proteins, lipids, and nucleic acids. Each functional group is a unique...
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Fischer Projections02:18

Fischer Projections

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Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines.
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A Protocol for Computer-Based Protein Structure and Function Prediction
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FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for

Biaoshun Li1, Mujie Lin1, Tiegen Chen2

  • 1Guangdong Provincial Key Laboratory of Fermentation and Enzyme Engineering, Joint International Research Laboratory of Synthetic Biology and Medicine, Ministry of Education, Guangdong Provincial Engineering and Technology Research Center of Biopharmaceuticals, School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China.

Briefings in Bioinformatics
|November 6, 2023
PubMed
Summary

We developed FG-BERT, a deep learning framework for predicting molecular properties. This AI model excels in drug discovery tasks, offering high performance and interpretability without needing manual feature engineering.

Keywords:
FG-BERTdeep learningmolecular property predictionmolecular representationsself-supervised learning

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Area of Science:

  • Computational chemistry and cheminformatics
  • Artificial intelligence in drug discovery
  • Deep learning for molecular modeling

Background:

  • Accurate molecular property prediction is crucial for designing novel bioactive molecules and functional materials.
  • Existing methods often require extensive feature engineering or lack interpretability.
  • Deep learning offers potential for learning complex molecular representations.

Purpose of the Study:

  • To introduce FG-BERT, a novel self-supervised deep learning framework for molecular property prediction.
  • To enable learning of meaningful molecular representations directly from functional groups.
  • To provide a highly performant and interpretable model for molecular design tasks.

Main Methods:

  • Developed a self-supervised deep learning framework named Functional Group Bidirectional Encoder Representations from Transformers (FG-BERT).
  • Pretrained FG-BERT on approximately 1.45 million unlabeled drug-like molecules.
  • Fine-tuned the pretrained FG-BERT for various molecular property prediction tasks.
  • Utilized attention mechanisms within FG-BERT for enhanced interpretability.

Main Results:

  • FG-BERT demonstrated superior performance compared to state-of-the-art machine learning and deep learning methods across 44 benchmark datasets.
  • The model achieved high accuracy in predicting properties related to physical chemistry, biophysics, and physiology.
  • Attention mechanisms highlighted critical functional group features relevant to target properties, enhancing model interpretability.

Conclusions:

  • FG-BERT provides an out-of-the-box framework for developing state-of-the-art models in molecular discovery, particularly for drug development.
  • The framework eliminates the need for artificial feature engineering, simplifying the prediction process.
  • FG-BERT offers excellent interpretability, aiding in understanding the basis of property predictions.