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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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Structure-Activity Relationships and Drug Design01:28

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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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.  
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Functional Groups02:45

Functional Groups

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Functional Groups02:45

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, the presence of certain functional groups on a molecule will make them 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...
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Related Experiment Video

Updated: Apr 11, 2026

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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NumMolFormer: an explicit functional group number-guided framework for structure-based drug design.

Zhicun Zeng1, Yifan Wu1, Zhangli Lu1

  • 1School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.

Bioinformatics (Oxford, England)
|April 9, 2026
PubMed
Summary
This summary is machine-generated.

NumMolFormer, a new framework, generates molecules by explicitly modeling functional group numbers. This approach improves binding affinity, synthetic accessibility, and drug-likeness in drug design.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Artificial intelligence in chemistry

Background:

  • Balancing binding affinity and physicochemical properties is crucial but challenging in structure-based drug design.
  • Molecular functionalization impacts both binding affinity and physicochemical properties, requiring careful control.
  • Existing methods struggle to effectively incorporate functional group number constraints.

Purpose of the Study:

  • To develop a novel framework, NumMolFormer, for rational molecule generation that explicitly models functional group numbers.
  • To improve the generation of molecules with optimized binding affinity and favorable physicochemical properties.

Main Methods:

  • Developed NumMolFormer, a Transformer-based framework using a dual-sequence input strategy.
  • Integrated a numerical embedding module and a dual-stream differential attention mechanism for separate encoding of molecular structures and functional group numbers.
  • Constructed a large-scale dataset of 18 million molecules for pre-training and fine-tuned using self-supervised and reinforcement learning under protein pocket constraints.

Main Results:

  • NumMolFormer effectively models functional group numbers, overcoming limitations of standard Transformers with numerical data.
  • The framework demonstrated improved binding affinity, synthetic accessibility, and drug-likeness compared to baseline methods.
  • Generated molecules showed enhanced properties by leveraging functional group information.

Conclusions:

  • NumMolFormer represents a significant advancement in structure-based drug design by enabling precise control over molecular functionalization.
  • The model's ability to balance binding affinity and physicochemical properties offers a promising approach for de novo drug design.
  • The framework and associated dataset facilitate further research in AI-driven molecular generation.