jglaser commited on
Commit
0db6cdd
1 Parent(s): ed3b8f2

Upload README.md

Browse files
Files changed (1) hide show
  1. README.md +18 -10
README.md CHANGED
@@ -8,7 +8,9 @@ tags:
8
 
9
  # jglaser/protein-ligand-mlp-1
10
 
11
- This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
 
12
 
13
  <!--- Describe your model here -->
14
 
@@ -17,14 +19,15 @@ This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentence
17
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
18
 
19
  ```
20
- pip install -U sentence-transformers
 
21
  ```
22
 
23
  Then you can use the model like this:
24
 
25
  ```python
26
  from sentence_transformers import SentenceTransformer
27
- sentences = ["This is an example sentence", "Each sentence is converted"]
28
 
29
  model = SentenceTransformer('jglaser/protein-ligand-mlp-1')
30
  embeddings = model.encode(sentences)
@@ -32,15 +35,10 @@ print(embeddings)
32
  ```
33
 
34
 
35
-
36
  ## Evaluation Results
37
 
38
  <!--- Describe how your model was evaluated -->
39
 
40
- For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=jglaser/protein-ligand-mlp-1)
41
-
42
-
43
-
44
  ## Full Model Architecture
45
  ```
46
  SentenceTransformer(
@@ -61,5 +59,15 @@ SentenceTransformer(
61
  ```
62
 
63
  ## Citing & Authors
64
-
65
- <!--- Describe where people can find more information -->
 
 
 
 
 
 
 
 
 
 
 
8
 
9
  # jglaser/protein-ligand-mlp-1
10
 
11
+ This is a [sentence-transformers](https://www.SBERT.net) model: It maps pairs of protein and chemical sequences (canonical SMILES) onto binding affinities (pIC50 values).
12
+
13
+ Each member of the ensemble has been trained using a different seed and you can use the different models as independent samples to estimate the uncertainty.
14
 
15
  <!--- Describe your model here -->
16
 
 
19
  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
20
 
21
  ```
22
+ #pip install -U sentence-transformers
23
+ pip install git+https://github.com/jglaser/sentence-transformers.git@enable_mixed
24
  ```
25
 
26
  Then you can use the model like this:
27
 
28
  ```python
29
  from sentence_transformers import SentenceTransformer
30
+ sentences = [{'protein': ["SEQVENCE"], 'ligand': ["c1ccccc1"]}]
31
 
32
  model = SentenceTransformer('jglaser/protein-ligand-mlp-1')
33
  embeddings = model.encode(sentences)
 
35
  ```
36
 
37
 
 
38
  ## Evaluation Results
39
 
40
  <!--- Describe how your model was evaluated -->
41
 
 
 
 
 
42
  ## Full Model Architecture
43
  ```
44
  SentenceTransformer(
 
59
  ```
60
 
61
  ## Citing & Authors
62
+ - [Andrew E Blanchard](https://github.com/blnchrd)
63
+ - [John Gounley](https://github.com/gounley)
64
+ - [Debsindhu Bhowmik](https://github.com/debsindhu)
65
+ - [Mayanka Chandra Shekar](https://github.com/mayankachandrashekar)
66
+ - [Isaac Lyngaas](https://github.com/irlyngaas)
67
+ - Shang Gao
68
+ - Junqi Yin
69
+ - Aristeidis Tsaris
70
+ - Feiyi Wang
71
+ - [Jens Glaser](https://github.com/jglaser)
72
+
73
+ Find more information in our [bioRxiv preprint](https://www.biorxiv.org/content/10.1101/2021.12.10.471928v1)