Drug Predictions

Gut Rhythm utilizes its Gastrointestinal Pacemaker Activity Drug Database (GIPADD) into deep learning aiming to identify correlations between drug profiles (electrophysiology) and drug-induced side effects in human, such as vomiting, nausea, diarrhea, etc. We achieve >90% precision in classifying vomiting, nausea and diarrhea-inducing drugs in our 1st generation prediction model. The model is also able to predict beyond the gut, e.g. oedema, dizziness, etc. with >90% precision. Gut Rhythm continues research and develop to identify more novel correlations, and applying these correlations to predict drug-induced side effects to aid decision making in drug discovery.

(updated on 15 January, 2024)

Our Services

Drug Testing

Test novel drug using Basic Service Plan A

Drug Prediction Service

Limited time offer!
Join our model validation program to get a free prediction trial-run.

Contact us to let us know your needs

Drug Prediction Report

Step 1

Test your drug-of-interest on gastrointestinal pacemaker activity

Step 2

Collect 60 datasets to build a drug profile

Step 3

Input the profile into our trained deep learning model (trained by >100 drug profiles, >10,000 datasets)

Step 4

Predict how similar your drug profiles are compare to other known Adverse Drug Reaction inducing drug

Drug Profile

Raw Data

Parameter Extraction

AI Deep Learning

Predictable Drug Adverse Effects with >90% Precision

Gut-related

  • Vomiting
  • Nausea
  • Diarrhoea
  • Abdominal pain
  • Gastrointestinal
  • disorder

Beyond the gut

  • Oedema
  • Chest pain
  • Dematitis
  • Dizziness
  • Asthenia
  • Rash
  • Shock
  • Paraesthesia

A drug profile stores information of the drug-induced effects on gastrointestinal pacemaker activities on 4 gut tissues types and tested on at least 3 concentrations. Electrical raw data were analyzed and transformed using our automatic analytical protocols. These information are input into training deep learning model to identify correlation with drug-induced side effects in human.

Gains for drug development sectors

  • To identify the potential risks of side effects for novel drug before entering clinical trials
  • Better clinical trial design
  • Improve decision making process – pick the drugs with good efficacy plus low side effect risks
  • Lower the risk of market failure
  • Lower the risk of patients’ withdrawal due to severe side effects
  • Low cost ($10k USD) and short time (1 week) to test and predict using our service

Gains for everyone

  • Better drugs to cure and care

Translatable technologies

  • Continue to grow with more tested drug profile
  • Collect data from more types of tissues
  • Predict more types of side effects
  • Sustainable data resources