mmpdb: An Open-Source Matched Molecular Pair Platform for Large Multiproperty Data Sets

By A. Dalke, J. Hert, Christian Kramer. 2018

102 citations recorded by the source corpus.

Matched molecular pair analysis (MMPA) enables the automated and systematic compilation of medicinal chemistry rules from compound/property data sets. Here we present mmpdb, an open-source matched molecular pair (MMP) platform to create, compile, store, retrieve, and use MMP rules. mmpdb is suitable for the large data sets typically found in pharmaceutical and agrochemical companies and provides new algorithms for fragment canonicalization and stereochemistry handling. The platform is written in Python and based on the RDKit toolkit. It is freely available from https://github.com/rdkit/mmpdb .

Subfields: Molecular Biology, Pharmaceutical Science, Spectroscopy

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. Tommi S. Jaakkola
2. Regina Barzilay
3. Wengong Jin
4. Kevin Yang
5. Jeremy Wohlwend

In Pharmaceutical Science

1. Regina Barzilay
2. Wengong Jin
3. Tommi S. Jaakkola

In Spectroscopy

1. Kevin Yang
2. Regina Barzilay
3. Wengong Jin
4. Tommi S. Jaakkola

Citations in the corpus

• Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (2018, methodology): “On the QED and DRD2 datasets, our models discover new compounds much more frequently than VSeq2Seq, but less often than MMPA and GCPN. Nonetheless, these methods have much lower translation success rate.”
• Hierarchical Generation of Molecular Graphs using Structural Motifs (2020, methodology): “Baselines We compare our method against the baselines including GCPN (You et al., 2018a), MMPA (Dalke et al., 2018) and translation based methods Seq2Seq and JTNN (Jin et al., 2019).”