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Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Synthetic Biology02:55

Synthetic Biology

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Data Collection by Observations01:08

Data Collection by Observations

Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Automated Microbial Diagnostics01:24

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Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...

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Related Experiment Video

Updated: May 27, 2026

Automated Robotic Liquid Handling Assembly of Modular DNA Devices
11:22

Automated Robotic Liquid Handling Assembly of Modular DNA Devices

Published on: December 1, 2017

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Smart distributed data factory volunteer computing platform for active learning-driven molecular data acquisition.

Tsolak Ghukasyan1, Vahagn Altunyan1, Aram Bughdaryan1

  • 1Deep Origin, Yerevan, Armenia.

Scientific Reports
|February 27, 2025
PubMed
Summary
This summary is machine-generated.

The Smart Distributed Data Factory (SDDF) accelerates drug discovery using AI and volunteer computing for quantum chemistry calculations. It builds comprehensive molecular datasets, reducing the need for extensive computations and improving molecular model accuracy.

Keywords:
Active learningConformational energyGraph neural networksMachine learningVolunteer computing

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Data science for molecular modeling

Background:

  • Drug discovery faces challenges with vast chemical spaces and limited high-quality data.
  • Accelerating quantum chemistry calculations is crucial for molecular property prediction.
  • Existing methods often require extensive computational resources.

Purpose of the Study:

  • To present the Smart Distributed Data Factory (SDDF), an AI-driven platform for generating comprehensive molecular datasets.
  • To leverage volunteer computing and machine learning to accelerate drug discovery.
  • To create a scalable and cost-effective solution for developing accurate molecular models.

Main Methods:

  • Utilizing volunteer computing for distributed density functional theory (DFT) calculations.
  • Employing an ensemble of machine learning (ML) models for property prediction and active learning.
  • Generating new molecular conformations via molecular dynamics using ML-derived forces.

Main Results:

  • Developed a volunteer computing platform for accelerated DFT calculations.
  • Constructed a large, public dataset of diverse ENAMINE molecules with calculated energies.
  • Created an ensemble of ML models demonstrating accurate energy prediction capabilities.
  • Validated the SDDF approach in reducing the need for extensive computational calculations.

Conclusions:

  • SDDF combines active learning, distributed computing, and quantum chemistry for efficient molecular modeling.
  • The platform offers a scalable and cost-effective solution for accelerating drug discovery.
  • The generated dataset supports training and benchmarking of energy prediction models.