chemprop/chemprop is ranked 3,657 among these 8 peers; google/tunix is ranked 3,660.

Chemprop Chemprop is a repository containing message passing neural networks for molecular property prediction. There are tutorial notebooks in the directory. Chemprop recently underwent a ground-up rewrite and new major release (v2. A helpful transition guide from Chemprop v1 to v2 can be found here. This includes a side-by-side comparison of CLI argument options, a list of which arguments will be implemented in later versions of v2, and a list of changes to default hyperparameters. **License:** Chemprop

is free to use under the MIT License. The Chemprop logo is free to use under CC0 1. **References**: Please cite the appropriate papers if Chemprop is helpful to your research. **Version 1**: Chemprop: A Machine Learning Package for Chemical Property Prediction. **Version 2**: Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction. **Theory**: The theory underpinning Chemprop was initially described in the papers Analyzing Learned Molecular Representations for Property Prediction for molecules and Machine Learning of

Reaction Properties via Learned Representations of the Condensed Graph of Reaction for reactions. **Interpretability**: The interpretation functionality (available in v2 as an example notebook) is based on the paper Multi-Objective Molecule Generation using Interpretable Substructures. **Atom and Bond Predictions**: Based on the paper When Do Quantum Mechanical Descriptors Help Graph Neural Networks to Predict Chemical Properties? A known inconsistency between these references and this repository is the edge update function in all versions uses preactivation instead of postactivation initial

edge hidden states. Details can be found in the SI of the version 2 paper. **Selected Applications**: Chemprop has been successfully used in the following works. A Deep Learning Approach to Antibiotic Discovery30102-1) - _Cell_ (2020): Chemprop was used to predict antibiotic activity against _E. coli_, leading to the discovery of Halicin, a novel antibiotic candidate. Model checkpoints are availabile on Zenodo. Discovery of a structural class of antibiotics with explainable deep learning - _Nature_ (2023): Identified a structural class of

antibiotics selective against methicillin-resistant _S. aureus_ (MRSA) and vancomycin-resistant enterococci using ensembles of Chemprop models, and explained results using Chemprop's interpret method. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries: Chemprop was trained on 41 absorption, distribution, metabolism, excretion, and toxicity (ADMET) datasets from the Therapeutics Data Commons. The Chemprop models in ADMET-AI are available both as a web server at admet. A more extensive list of successful Chemprop applications is

given in our 2023 paper Version 1. x For users who have not yet made the switch to Chemprop v2. 0, please reference the following resources. v1 Documentation Documentation of Chemprop v1 is available here. Note that the content of this site is several versions behind the final v1 release (v1. 1) and does not cover the full scope of features available in chemprop v1. The v1 README is the best source for documentation on more recently-added features. Please

also see descriptions of all the possible command line arguments in the v1 file. v1 Tutorials and Examples Benchmark scripts