BioSystemsUM/DeepMol
DeepMol: A Machine and Deep Learning Framework for Computational Chemistry
DeepMol helps drug discovery scientists and computational chemists efficiently screen potential drug candidates. It takes molecular structures, typically represented as SMILES strings or SDF files, and applies machine and deep learning to predict properties or activities, outputting insights for prioritizing compounds. This framework is designed for researchers in pharmaceutical R&D, academia, or biotech looking to accelerate their computational chemistry workflows.
171 stars.
Use this if you need to build, train, and evaluate machine learning models for drug discovery or chemoinformatics tasks, handling molecular data from initial loading to advanced featurization and prediction.
Not ideal if you are looking for a simple, out-of-the-box solution for a single, pre-defined molecular task without any need for customization or model building.
Stars
171
Forks
20
Language
Python
License
BSD-2-Clause
Category
Last pushed
Mar 20, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/BioSystemsUM/DeepMol"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
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...