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An open API service providing mapping between scientific papers and software projects that are mentioned in them.

Projects: pypi: chemprop

https://packages.ecosyste.ms/registries/pypi.org/packages/chemprop

Molecular Property Prediction with Message Passing Neural Networks
13 versions
Latest release: almost 2 years ago
209,666 downloads last month

Enhanced Analysis
Educational Contributors: [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected]
Repository Activity:
Repository Owner: chemprop (organization)
Home of the official chemprop project
README Analysis: DOI Found ArXiv Preprint
Science Score: 100/100
Starting Score: 100 points
Bonuses:
  • +700 Educational commit emails
    35 contributors with educational email addresses
  • +32 Science terms in README
    16 scientific terms found in README
  • +10 DOI references
    1 DOI references found in README
  • +18 Academic links
    3 academic links found in README
Penalties:
  • -10 PyPI ecosystem
    General-purpose ecosystem
Very Likely Science (100)

Papers Mentioning chemprop 3

10.1007/s10822-021-00405-6
Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge
Cited by: 9
Author(s): Eelke B. Lenselink, Pieter F. W. Stouten
Software Mentions: 4
Published: about 4 years ago
10.1186/s13065-021-00737-2
De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning
Cited by: 43
Author(s): Marcos V.S. Santana, Floriano Paes Silva
Software Mentions: 1
Published: over 4 years ago
10.1186/s13321-021-00533-z
MLSolvA: solvation free energy prediction from pairwise atomistic interactions by machine learning
Cited by: 13
Author(s): Hyuntae Lim, YounJoon Jung
Software Mentions: 1
Published: almost 4 years ago