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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Machine learning-driven protein engineering: a case study in computational drug discovery.

Harry F Rickerby1, Katya Putintseva1, Christopher Cozens1

  • 1LabGenius G06-G09 Cocoa Studios, 100 Drummond Road London UK.

Engineering Biology
|March 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning platform to accelerate drug discovery by integrating high-throughput data generation with deep learning. This approach enhances the efficiency of developing novel biotherapeutics and optimizes protein characteristics.

Keywords:
DNADNA library synthesisML‐driven drug discoverybiology computingcomputational drug discoverydeep learningdirected evolutiondrugsgeneration sequencinggreat expectationhigh‐quality datasetshigh‐throughput displaylearning (artificial intelligence)learningsmachine learning‐driven protein engineeringmolecular biophysicsmultiple important protein characteristicsoptimisationproteinsselection data generationsignificant efficiency gainssilico modelsultra‐high throughput selections

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Drug Discovery

Background:

  • The pharmaceutical industry requires significant efficiency gains in research and development (R&D) to sustain the discovery of novel drugs.
  • Machine learning (ML) is anticipated to enhance R&D productivity, but its full potential relies on generating high-quality datasets.
  • Developing new biotherapeutics necessitates innovative approaches to data generation and analysis.

Purpose of the Study:

  • To present a novel platform integrating high-throughput data generation with machine learning for biotherapeutic development.
  • To utilize deep learning to guide the directed evolution of novel biotherapeutics.
  • To establish a benchmarking model for evaluating ML-driven drug discovery platforms.

Main Methods:

  • A platform combining high-throughput display and selection data generation with ML was developed.
  • Deep learning models were employed to inform directed evolution strategies.
  • DNA library synthesis, ultra-high throughput selections, and next-generation sequencing were utilized for data generation.
  • Multiple in silico models were combined for multi-parameter optimization of protein characteristics.

Main Results:

  • The platform enables the optimization of multiple protein characteristics through integrated in silico and empirical approaches.
  • The study demonstrates the application of deep learning in guiding the directed evolution of biotherapeutics.
  • A benchmarking model was proposed to assess the performance of ML-driven drug discovery platforms based on model accuracy and experimental throughput.

Conclusions:

  • The presented platform offers a significant advancement in accelerating the efficiency of drug discovery R&D.
  • Combining machine learning with high-throughput experimentation is crucial for developing next-generation biotherapeutics.
  • The benchmarking model provides a framework for evaluating and improving ML-driven drug discovery methodologies.