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Effects of Chemicals: Overview

Effects of Chemicals: Overview

Drugs, encompassing various chemical compounds from natural sources, lab synthesis, or genetic engineering, elicit different biological responses in living organisms. Some of these responses are desirable or therapeutic, while others are undesirable. The primary goal of administering a drug is to achieve a therapeutic effect, that is, to address a specific disease or health condition. Any concurrent effects outside of this therapeutic outcome are considered undesirable. These undesirable...

Agonism and Antagonism: Quantification

Agonism and Antagonism: Quantification

When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
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Factors Affecting Drug Response: Overview

Factors Affecting Drug Response: Overview

When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...

Time Course of Drug Effect

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The progression of a drug's impact can be analyzed by examining both the concentration-time course and the effect-time course. The concentration-time course is determined by the drug's half-life and is influenced by factors such as its pharmacokinetics, including absorption, distribution, metabolism, and elimination. The effect of the drug is often related to its concentration in the plasma and is calculated using the maximum drug effect and the plasma concentration that generates 50...

Structure-Activity Relationships and Drug Design

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Factors Affecting Drug Biotransformation: Biological

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Biological factors significantly impact drug metabolism, influencing drug clearance, efficacy, and potential toxicity.
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Related Experiment Video

Updated: Jul 26, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Explainable drug side effect prediction via biologically informed graph neural network.

Tongtong Huang¹, Ko-Hong Lin¹, Rodrigo Machado-Vieira²

¹School of Biomedical Informatics, UTHealth, Houston, TX, United States.

Medrxiv : the Preprint Server for Health Sciences

|June 19, 2023

Summary

Predicting drug side effects (SEs) early is crucial. A new graph-based model, HHAN-DSI, accurately forecasts SEs for unseen drugs, aiding drug discovery and patient safety.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Area of Science:

Pharmacology and Toxicology
Computational Biology
Drug Discovery

Background:

Early detection of drug side effects (SEs) is vital but challenging during preclinical stages.
Traditional in-vitro or in-vivo methods are often not scalable for numerous drug candidates.
Explainable machine learning offers potential for predicting SEs and understanding biological mechanisms before market release.

Conclusions:

HHAN-DSI demonstrates a scalable and accurate approach for predicting drug side effects early in development.
The model enhances drug discovery by identifying potential risks and mechanisms, improving patient care.
Explainable AI integrated with graph-based methods offers a powerful tool for pharmaceutical research and safety assessment.