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doc update

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Greg Landrum
2015-11-06 06:27:12 +01:00
parent d2bc5b3605
commit 90147c261c
2 changed files with 23 additions and 20 deletions
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41
Docs/Book/Overview.md
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@@ -32,7 +32,7 @@
# Functionality overview
## Basics
- Input/Output: SMILES/SMARTS, SDF, TDT, SLN [1](#footnote1), Corina mol2 [1](#footnote1), PDB
- Input/Output: SMILES/SMARTS, SDF, TDT, SLN [1](#footnote1), Corina mol2 [1](#footnote1), PDB, sequence notation, FASTA (peptides only), HELM (peptides only)
- Substructure searching
- Canonical SMILES
- Chirality support (i.e. R/S or E/Z labeling)
@@ -40,7 +40,7 @@
- Chemical reactions
- Molecular serialization (e.g. mol \<-\> text)
- 2D depiction, including constrained depiction
- Fingerprinting: Daylight-like, atom pairs, topological torsions, Morgan algorithm, “MACCS keys”, etc.
- Fingerprinting: Daylight-like, atom pairs, topological torsions, Morgan algorithm, “MACCS keys”, extended reduced graphs, etc.
- Similarity/diversity picking
- Gasteiger-Marsili charges
- Bemis and Murcko scaffold determination
@@ -48,26 +48,27 @@
- Functional-group filters
## 2D
- 2D pharmacophores [1](#footnote1)
- Hierarchical subgraph/fragment analysis
- RECAP and BRICS implementations
- Multi-molecule maximum common substructure [2](#footnote2)
- Functional group filtering
- Molecular descriptor library:
- Topological (κ3, Balaban J, etc.)
- Compositional (Number of Rings, Number of Aromatic Heterocycles, etc.)
- Electrotopological state (Estate)
- clogP, MR (Wildman and Crippen approach)
- “MOE like” VSA descriptors
- MQN [6](#footnote6)
- Similarity Maps [7](#footnote7)
- Machine Learning:
- Clustering (hierarchical, Butina)
- Information theory (Shannon entropy, information gain, etc.)
- Tight integration with the [IPython](http://ipython.org) notebook and [Pandas](http://pandas.pydata.org/).
- 2D pharmacophores [1](#footnote1)
- Hierarchical subgraph/fragment analysis
- RECAP and BRICS implementations
- Multi-molecule maximum common substructure [2](#footnote2)
- Functional group filtering
- Enumeration of molecular resonance structures
- Molecular descriptor library:
- Topological (κ3, Balaban J, etc.)
- Compositional (Number of Rings, Number of Aromatic Heterocycles, etc.)
- Electrotopological state (Estate)
- clogP, MR (Wildman and Crippen approach)
- “MOE like” VSA descriptors
- MQN [6](#footnote6)
- Similarity Maps [7](#footnote7)
- Machine Learning:
- Clustering (hierarchical, Butina)
- Information theory (Shannon entropy, information gain, etc.)
- Tight integration with the [IPython](http://ipython.org) notebook and [Pandas](http://pandas.pydata.org/).
## 3D
- 2D-\>3D conversion/conformational analysis via distance geometry
- 2D-\>3D conversion/conformational analysis via distance geometry, including optional use of experimental torsion angle potentials.
- UFF and MMFF94/MMFF94S implementations for cleaning up structures
- Pharmacophore embedding (generate a pose of a molecule that matches a 3D pharmacophore) [1](#footnote1)
- Feature maps

2
ReleaseNotes.md
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@@ -13,6 +13,8 @@ Maciek Wojcikowski
- Addition of parsers/writers for sequence notation, FASTA, and basic HELM
- Improved conformation generation based on experimental torsional parameters
- Much better filtering of generated conformations to ensure they
match the chirality of the input structure
- New method for enumerating molecular resonance structures
- Addition of a molecular FilterCatalog data structure