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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Toxic Reactions: Overview01:26

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When toxic substances penetrate the human body, they disseminate to various tissues, undergoing metabolic changes. This process yields reactive metabolites that may covalently bind with specific target molecules, resulting in toxicity.
Toxicity falls into two primary categories: local and systemic.
Local toxicity appears at the exposure site, such as protein denaturation caused by caustic substances.
In contrast, systemic toxicity requires the toxic agent's absorption and distribution,...
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Related Experiment Video

Updated: Aug 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Machine Learning Toxicity Prediction: Latest Advances by Toxicity End Point.

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Machine learning (ML) models predict small molecule toxicity, aiding early drug discovery. This review details recent ML progress, challenges, and state-of-the-art models for various toxic endpoints.

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Area of Science:

  • Computational toxicology
  • cheminformatics
  • drug discovery

Background:

  • Machine learning (ML) models are increasingly used for predicting small molecule toxicity.
  • Computational toxicity prediction aids early-stage drug discovery by filtering potential drug candidates.
  • Advancements in toxicology databases support ML applications, yet understanding ML scope and applicability remains crucial.

Purpose of the Study:

  • To review recent progress and challenges in ML-based toxicity prediction.
  • To provide a detailed description of state-of-the-art ML models for various toxic endpoints.
  • To analyze molecular representation, algorithms, and evaluation metrics used in ML toxicity studies.

Main Methods:

  • Literature review focusing on recent advancements in ML for toxicity prediction.
  • Analysis of ML models applied to specific toxic endpoints (e.g., acute oral toxicity, hepatotoxicity, mutagenicity).
  • Examination of molecular representations, algorithms, and evaluation metrics in published research.

Main Results:

  • Detailed overview of ML model performance across different toxic endpoints.
  • Identification of common molecular representations, algorithms, and evaluation metrics.
  • Highlighting of challenges in comparing ML algorithm performance due to data complexity and chemical space variations.

Conclusions:

  • ML models show promise for predicting diverse toxic endpoints, but performance varies.
  • Further research is needed to understand the scope and limitations of ML methods in toxicology.
  • Standardized evaluation and reporting are essential for robust ML-driven toxicity assessments.