chembl/chembl_multitask_model
Target prediction multitask neural network, with examples running it in Python, C++, Julia and JS
This tool helps drug discovery scientists quickly predict how a chemical compound might interact with a panel of biological targets. You provide the chemical structure of a compound (e.g., as a SMILES string), and it outputs a list of potential targets along with a prediction of the compound's activity against them. It's designed for researchers evaluating large collections of compounds for potential off-target effects or repurposing opportunities.
No commits in the last 6 months.
Use this if you need to rapidly screen many compounds to identify likely biological targets or predict potential off-target activity in early-stage drug discovery.
Not ideal if you require a highly specific model for a single target or need to predict complex binding mechanisms beyond simple activity thresholds.
Stars
20
Forks
10
Language
Python
License
MIT
Category
Last pushed
Sep 20, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/chembl/chembl_multitask_model"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
deepmodeling/deepmd-kit
A deep learning package for many-body potential energy representation and molecular dynamics
chemprop/chemprop
Message Passing Neural Networks for Molecule Property Prediction
Acellera/moleculekit
MoleculeKit: Your favorite molecule manipulation kit
mir-group/nequip
NequIP is a code for building E(3)-equivariant interatomic potentials
CederGroupHub/chgnet
Pretrained universal neural network potential for charge-informed atomistic modeling...