AI in Pharmacology
AI is in routine use for predicting protein structures and basic drug properties, and growing in pharmacovigilance for sorting adverse event reports. Machine learning for spotting new safety signals and designing drugs is active research. Residents can try several free tools today.
What AI does in Pharmacology
Look up predicted 3D structures for any protein to understand drug binding sites.
Examples: AlphaFold Protein Structure Database
Free web tools estimate pharmacokinetic properties of small molecules.
Examples: SwissADME
Search curated data on how molecules act on targets to find leads and compare drugs.
Examples: ChEMBL
NLP extracts adverse events from text and ranks reports by seriousness.
Examples: Not listed
Random forest and gradient boosting methods are reported to outperform older frequentist methods on some measures and found one signal six months earlier than humans in a 2024 pilot.
Examples: XGBoost, random forest models in published studies
Models propose or rank candidate molecules and predict their properties.
Examples: Not listed
routine = in everyday use in many centres · early adoption = in use at some sites, evidence still building · research = studies only
Key ideas for this specialty
New to the basics? Start with the theory lessons.
Tools
Free, login-free web tool for physicochemical properties, pharmacokinetics and drug-likeness of molecules.
Regulatory status: Not listed
Manually curated database of bioactive molecules with drug-like properties, under CC BY-SA 3.0.
Regulatory status: Not listed
Over 260 million predicted protein structures from Google DeepMind and EMBL-EBI, under CC BY 4.0.
Regulatory status: Not listed
Indian Android app and online portal for adverse drug reaction reports; PvPI had 1,200 monitoring centres per its 2026 newsletter.
Regulatory status: Not listed
Finds papers and extracts study details for a drug review.
Regulatory status: Not listed
A listing here is not an endorsement. Ask any vendor for its CDSCO licence and class before clinical use.
Where to learn
The original paper explaining BOILED-Egg, bioavailability radar and other outputs.
Maps AI use across adverse event extraction, signal detection and triage.
Compares machine learning with traditional signal detection.
Includes a treatment-effect course; needs basic Python.
No-code ground rules for reading AI performance claims.
Current Indian pharmacovigilance activity and report numbers.
Do it yourself
Paste the SMILES of five drugs you study into SwissADME. Compare predicted GI absorption and Lipinski violations with what your textbook says about their bioavailability.
Open the dataset or tool →Search a receptor you teach, such as the beta-2 adrenergic receptor, in the AlphaFold database. Note the confidence colours and which regions are low confidence.
Open the dataset or tool →Search a drug you know and list its targets and measured potencies. Compare them with its textbook mechanism.
Open the dataset or tool →Use public or properly de-identified data only, and get ethics approval before any project on your own patients.
Papers worth reading
- Advancing pharmacovigilance through artificial intelligence: A review of applications and ethical considerations Annals of Tropical Research, 2025
- New avenues in artificial-intelligence-assisted drug discovery Drug Discovery Today, 2023
- SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules Scientific Reports, 2017
Societies and guidance
National drug safety programme and ADR reporting; no AI-specific page confirmed.
Ethical guidelines for AI in biomedical research.
Links checked 3 October 2026. “Not listed” means we could not confirm the detail, not that it is absent. Educational material, not clinical advice.