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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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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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Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
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Protein-Drug Binding: Mechanism and Kinetics01:16

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Protein-drug binding refers to the interaction between drugs and proteins within the body. This binding process can occur intracellularly, involving drug interactions with enzymes or receptors within cells, or extracellularly, involving plasma proteins in the blood.
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FL-QSAR: a federated learning-based QSAR prototype for collaborative drug discovery.

Shaoqi Chen1, Dongyu Xue1, Guohui Chuai1

  • 1Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai 200092, China.

Bioinformatics (Oxford, England)
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Summary

Horizontal federated learning (HFL) enables collaborative drug discovery by enhancing quantitative structure-activity relationship (QSAR) modeling. This privacy-preserving approach achieves high prediction performance, overcoming institutional barriers.

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Area of Science:

  • Computational chemistry
  • Cheminformatics
  • Drug discovery

Background:

  • Quantitative structure-activity relationship (QSAR) analysis is crucial for drug discovery.
  • Inter-institutional collaborations improve QSAR prediction but are hindered by intellectual property concerns.

Purpose of the Study:

  • To evaluate the feasibility of horizontal federated learning (HFL) for privacy-preserving QSAR analysis.
  • To introduce FL-QSAR, a platform for collaborative drug discovery using HFL.

Main Methods:

  • Comparison of HFL with secure multiparty computation for privacy preservation.
  • Evaluation of FL-QSAR performance against single-client and public collaboration models.
  • Implementation of a prototype platform for federated-learning-based QSAR modeling.

Main Results:

  • FL-QSAR collaboration significantly outperforms single-client QSAR modeling.
  • FL-QSAR achieves prediction performance comparable to traditional cleartext collaboration.
  • HFL offers a viable privacy-preserving alternative for collaborative QSAR.

Conclusions:

  • FL-QSAR effectively overcomes barriers to inter-institutional collaboration in QSAR modeling.
  • The HFL framework promotes privacy-preserving and collaborative drug discovery.
  • FL-QSAR has potential applications in other privacy-sensitive biomedical research areas.