emmaking-smith/Modular_Latent_Space
The code corresponding to Transfer Learning for a Foundational Chemistry Model
This project helps chemists and materials scientists predict properties of small molecules, like toxicity, reaction yields, and fragrance profiles. You provide existing chemical data, and it outputs predictions for new compounds, helping you prioritize experiments and understand molecular behavior. It's designed for researchers working with chemical compounds and their various attributes.
No commits in the last 6 months.
Use this if you need to quickly and accurately predict outcomes for new or unseen chemical compounds based on existing experimental data in areas like toxicity, chemical reaction yields (e.g., Suzuki, Buchwald-Hartwig), or fragrance characteristics.
Not ideal if you are looking for a general-purpose machine learning library without specific applications in chemistry or if you require predictions for properties not covered (e.g., protein folding, drug docking).
Stars
12
Forks
5
Language
Python
License
MIT
Category
Last pushed
Dec 05, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/emmaking-smith/Modular_Latent_Space"
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...