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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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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Big Data in Drug Discovery.

Nathan Brown1, Jean Cambruzzi1, Peter J Cox1

  • 1BenevolentAI, London, United Kingdom.

Progress in Medicinal Chemistry
|April 23, 2018
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can help drug discovery by interpreting Big Data to improve early decision-making and reduce clinical trial failures. AI, using natural language processing, links diverse data for hypothesis generation and efficient data utilization.

Keywords:
Artificial intelligenceBig DataBiologyChemistryClinical trialsDrug discovery

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Area of Science:

  • Biomedical informatics
  • Drug discovery and development
  • Data science in pharmaceuticals

Background:

  • Drug discovery faces challenges with Big Data volume, ingestion, and infrastructure.
  • Data reproducibility and experimental context are critical for success in drug development.
  • Artificial intelligence (AI) is essential for interpreting complex experimental data and context.

Purpose of the Study:

  • To explore the role of AI in enhancing Big Data interpretation for drug discovery.
  • To address challenges in data management and utilization within the pharmaceutical research community.
  • To showcase public domain data sources and initiatives for interrogating biological, chemical, and clinical trial data.

Main Methods:

  • Leveraging natural language processing (NLP) pipelines for AI-driven data analysis.
  • Developing infrastructure for efficient ingestion and housing of large datasets.
  • Integrating data from biology, chemistry, and clinical trials for comprehensive analysis.

Main Results:

  • AI facilitates improved early decision-making, potentially shortening project timelines.
  • AI assists in linking disparate data sources to generate novel hypotheses.
  • Publicly available Big Data resources are highlighted for their potential in drug discovery.

Conclusions:

  • AI is crucial for unlocking the value of Big Data in drug discovery, improving efficiency and reducing attrition.
  • Effective data infrastructure and context interpretation are key to successful Big Data utilization.
  • The integration of AI and accessible data sources offers a promising avenue for advancing pharmaceutical research.