update airtable csv [skip ci]

dockerfiles/model-deploy/base/Dockerfile

@@ -26,4 +26,4 @@ ENV PATH="/venv/bin:$PATH"
 RUN python -m venv /venv && \
  pip install --upgrade pip && \
  cd /root/ersilia && \
- pip install -e .
+ pip install .

ersilia/hub/content/data/models.tsv

@@ -33,7 +33,7 @@ eos2thm ['Compound'] Pretrained https://github.com/ersilia-os/eos2thm https://ar
 $ ersilia api -i 'CCCOCCC'
 $ ersilia close" 2021-09-17 https://github.com/miquelduranfrigola
 eos9ueu ['Compound'] Online https://github.com/ersilia-os/eos9ueu https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3606195/ https://pypi.org/project/smallworld-api/ MIT ['Compound'] "Small World is an index of chemical space containing more than 230B molecular substructures. Here we use the Small World API to post a query to the SmallWorld server. We sample 100 molecules within a distance of 10 specifically for the Enamine REAL map, not the entire SmallWorld domain. Please check other small-world models available in our hub.
-" Ready small-world-enamine-real Small World Enamine REAL search ['Similarity'] Single List of 100 nearest neighbors ['Similarity'] miquelduranfrigola List ['String'] https://hub.docker.com/r/ersiliaos/eos9ueu ['AMD64'] https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9ueu.zip {'label': 'GitHub', 'url': 'https://github.com/ersilia-os/eos9ueu'} "$ ersilia serve small-world-enamine-real
+" Ready small-world-enamine-real Small World Enamine REAL search ['Similarity'] Single List of 100 nearest neighbors ['Similarity'] miquelduranfrigola List ['String'] https://hub.docker.com/r/ersiliaos/eos9ueu ['AMD64', 'ARM64'] https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9ueu.zip {'label': 'GitHub', 'url': 'https://github.com/ersilia-os/eos9ueu'} "$ ersilia serve small-world-enamine-real
 $ ersilia api -i 'CCCOCCC'
 $ ersilia close" 2023-11-01 https://github.com/miquelduranfrigola
 eos481p ['Compound'] Pretrained https://github.com/ersilia-os/eos481p https://papers.nips.cc/paper/2020/hash/94aef38441efa3380a3bed3faf1f9d5d-Abstract.html https://github.com/tencent-ailab/grover MIT ['Probability'] "Prediction across the ToxCast toxicity panel, containing hundreds of toxicity outcomes, as part of the MoleculeNet benchmark. This model has been trained using the GROVER transformer (see eos7w6n or grover-embedding for a detail of the molecular featurization step with GROVER)
@@ -323,7 +323,7 @@ eos2re5 ['Compound'] Pretrained https://github.com/ersilia-os/eos2re5 https://jc
 $ ersilia api -i 'CCCOCCC'
 $ ersilia close" 2022-07-28 https://github.com/svolk19-stanford 
 eos2hzy ['Compound'] Pretrained https://github.com/ersilia-os/eos2hzy https://academic.oup.com/nar/article/51/D1/D1373/6777787 https://github.com/ersilia-os/chem-sampler/blob/main/chemsampler/samplers/pubchem/sampler.py GPL-3.0 ['Compound'] "A simple sampler of the PubChem database using their API. It looks for similar molecules to the input molecule and returns a list of 100 molecules by default. This model has been developed by Ersilia and posts queries to an online server.
-" Ready pubchem-sampler PubChem Molecular Sampler ['Similarity'] Single ['eos2hzy', 'pubchem-sampler'] 100 nearest molecules in PubChem ['Similarity'] GemmaTuron List ['String'] https://hub.docker.com/r/ersiliaos/eos2hzy ['AMD64', 'ARM64'] https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos2hzy.zip {'label': 'GitHub', 'url': 'https://github.com/ersilia-os/eos2hzy'} "$ ersilia serve pubchem-sampler
+" Ready pubchem-sampler PubChem Molecular Sampler ['Similarity'] Single ['eos2hzy', 'pubchem-sampler'] 100 nearest molecules in PubChem ['Similarity'] GemmaTuron List ['String'] https://hub.docker.com/r/ersiliaos/eos2hzy ['AMD64'] https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos2hzy.zip {'label': 'GitHub', 'url': 'https://github.com/ersilia-os/eos2hzy'} "$ ersilia serve pubchem-sampler
 $ ersilia api -i 'CCCOCCC'
 $ ersilia close" 2023-08-10 https://github.com/GemmaTuron
 eos2l0q ['Compound'] In-house https://github.com/ersilia-os/eos2l0q https://pubmed.ncbi.nlm.nih.gov/30398059 https://github.com/ersilia-os/lazy-qsar GPL-3.0 ['Probability'] "Prediction of the activity of small molecules against the schistosoma parasite. This model has been developed by Ersilia thanks to the data provided by the Swiss TPH. In vitro activity against newly transformed schistosoma (nts) and adult worms was measured (% of inhibition of activity and IC50, respectively)
@@ -510,7 +510,7 @@ eos5505 ['Compound'] Pretrained https://github.com/ersilia-os/eos5505 https://ww
 $ ersilia api -i 'CCCOCCC'
 $ ersilia close" 2023-01-12 https://github.com/pauline-banye
 eos694w ['Compound'] Pretrained https://github.com/ersilia-os/eos694w https://chemrxiv.org/engage/chemrxiv/article-details/65463cafc573f893f1cae33a https://github.com/MolecularAI/REINVENT4 Apache-2.0 ['Compound'] "The Mol2MolMediumSimilarity leverages REINVENT4's `mol2mol_medium_similarity.prior` to generate approximately 100 unique molecules. The generated molecules will be relatively similar to the input molecule.
-" In progress reinvent4-mol2mol-medium-similarity REINVENT 4 Mol2MolMediumSimilarity ['Similarity'] Single Model generates ~100 similar molecules per input molecule. ['Generative'] ankitskvmdam List ['String'] {'label': 'GitHub', 'url': 'https://github.com/ersilia-os/eos694w'} "$ ersilia serve reinvent4-mol2mol-medium-similarity
+" In progress reinvent4-mol2mol-medium-similarity REINVENT 4 Mol2MolMediumSimilarity ['Similarity'] Single Model generates ~100 similar molecules per input molecule. ['Generative'] ankitskvmdam List ['String'] https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos694w.zip {'label': 'GitHub', 'url': 'https://github.com/ersilia-os/eos694w'} "$ ersilia serve reinvent4-mol2mol-medium-similarity
 $ ersilia api -i 'CCCOCCC'
 $ ersilia close" 2024-02-07 https://github.com/ankitskvmdam
 eos3804 ['Compound'] Pretrained https://github.com/ersilia-os/eos3804 https://www.nature.com/articles/s41589-023-01349-8 https://github.com/GaryLiu152/chemprop_abaucin None ['Score'] "This model is a Chemprop neural network trained with a growth inhibition dataset. Authors screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. They discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii.
@@ -606,7 +606,7 @@ eos157v ['Compound'] Pretrained https://github.com/ersilia-os/eos157v https://pa
 $ ersilia api -i 'CCCOCCC'
 $ ersilia close" 2022-07-13 https://github.com/Amna-28
 eos4qda ['Compound'] Pretrained https://github.com/ersilia-os/eos4qda https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00566-4 https://github.com/UnixJunkie/FASMIFRA GPL-3.0 ['Compound'] "FasmiFra is a molecular generator based on (deep)SMILES fragments. The authors use Deep SMILES to ensure the generated molecules are syntactically valid, and by working on string operations they are able to obtain high performance (>340,000 molecule/s). Here, we use 100k compounds from ChEMBL to sample fragments. Only assembled molecules containing one of the fragments of the input molecule are retained.
-" Ready fasmifra FasmiFra molecule generator ['Compound generation'] Single 1000 generated molecules per each input ['Generative'] miquelduranfrigola List ['String'] https://hub.docker.com/r/ersiliaos/eos4qda ['AMD64'] https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos4qda.zip {'label': 'GitHub', 'url': 'https://github.com/ersilia-os/eos4qda'} "$ ersilia serve fasmifra
+" Ready fasmifra FasmiFra molecule generator ['Compound generation'] Single 1000 generated molecules per each input ['Generative'] miquelduranfrigola List ['String'] https://hub.docker.com/r/ersiliaos/eos4qda ['AMD64', 'ARM64'] https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos4qda.zip {'label': 'GitHub', 'url': 'https://github.com/ersilia-os/eos4qda'} "$ ersilia serve fasmifra
 $ ersilia api -i 'CCCOCCC'
 $ ersilia close" 2023-08-01 https://github.com/miquelduranfrigola
 eos2b6f ['Compound'] Pretrained https://github.com/ersilia-os/eos2b6f https://www.biorxiv.org/content/10.1101/2022.01.20.476787v1 https://github.com/mayrf/pkasolver MIT ['Experimental value'] "This model employs transfer learning with graph neural networks in order to predict micro-state pKa values of small molecules. The model enumerates the molecule's protonation states and predicts its pKa values. It was trained in two phases, first, using a large ChEMBL dataset and then fine-tuning the model for a small training set of molecules with available pKa values. The model in this repository is the pkasolver-light, which does not require an Epik license and is limited to monoprotic molecules.