arxiv submission

README.md

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+# Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation
+
+Implementation for [[add arxiv link]], submitted to NeurIPS 2021.
+
+This is a minimum working version of the code used for the paper, which is extracted from the internal repository of the [Mila Molecule Discovery](https://mila.quebec/en/ai-society/exascale-search-of-molecules/) project. Original commits are lost here, but the credit for this code goes to [@bengioe](https://github.com/bengioe), [@MJ10](https://github.com/MJ10) and [@MKorablyov](https://github.com/MKorablyov/) (see paper).
+
+## Grid experiments
+
+Requirements for base experiments: 
+- `torch numpy scipy tqdm`
+
+Additional requirements for active learning experiments: 
+- `botorch gpytorch`
+
+
+## Molecule experiments
+
+Additional requirements:
+- `pandas rdkit torch_geometric h5py`
+- a few biochemistry programs, see `mols/Programs/README`
+
+For `rdkit` in particular we found it to be easier to install through (mini)conda. [`torch_geometric`](https://github.com/rusty1s/pytorch_geometric) has non-trivial installation instructions.
+
+We compress the 300k molecule dataset for size. To uncompress it, run `cd mols/data/; gunzip docked_mols.h5.gz`.
+
+We omit docking routines since they are part of a separate contribution still to be submitted. These are available on demand, please do reach out to [email protected] or [email protected].