HF Spaces

About.py

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+# Copyright 2018-2022 Streamlit Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import streamlit as st
+from streamlit.logger import get_logger
+LOGGER = get_logger(__name__)
+
+
+def run():
+    st.set_page_config(
+        page_title="About WarmMolGen",
+        page_icon="🚀",
+        layout='wide'
+    )
+
+    st.write("## [Exploiting Pretrained Biochemical Language Models for Targeted Drug Design](https://doi.org/10.1093/bioinformatics/btac482)")
+    #st.sidebar.title("Model Demos")
+    st.sidebar.success("Select a model demo above.")
+
+    st.markdown(
+        """
+        This application demonstrates the generation capabilities of the models trained as part of the study below published in *Bioinformatics*. The available models are: 
+        * WarmMolGen
+            - WarmMolGenOne (i.e. EncDecBase)
+            - WarmMolGenTwo (i.e. EncDecLM)
+        * ChemBERTaLM 
+
+        👈 Select a model demo from the sidebar to generate molecules right away 🚀
+
+        ### Abstract
+        **Motivation:** The development of novel compounds targeting proteins of interest is one of the most important tasks in
+        the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown
+        promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein
+        language and the chemical language. However, such a model is limited by the availability of interacting protein–ligand
+        pairs. On the other hand, large amounts of unlabelled protein sequences and chemical compounds are available and
+        have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained
+        biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate
+        two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation
+        and (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target-specific training. We
+        also compare two decoding strategies to generate compounds: beamsearch and sampling.
+
+        **Results:** The results show that the warm-started models perform better than a baseline model trained from scratch.
+        The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics
+        from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests
+        that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that
+        beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound
+        quality.
+
+        **Availability and implementation:** The source code is available at https://github.com/boun-tabi/biochemical-lms-for-drug-design and the materials (i.e., data, models, and outputs) are archived in Zenodo at https://doi.org/10.5281/zenodo.6832145. 
+        ### Citation
+        ```bibtex
+        @article{10.1093/bioinformatics/btac482,
+            author = {Uludoğan, Gökçe and Ozkirimli, Elif and Ulgen, Kutlu O. and Karalı, Nilgün Lütfiye and Özgür, Arzucan},
+            title = "{Exploiting Pretrained Biochemical Language Models for Targeted Drug Design}",
+            journal = {Bioinformatics},
+            year = {2022},
+            doi = {10.1093/bioinformatics/btac482},
+            url = {https://doi.org/10.1093/bioinformatics/btac482}
+        }
+        ```
+    """
+    )
+    # page_names_to_funcs = {
+    #     "—": intro,
+    #     "Plotting Demo": plotting_demo,
+    #     "Mapping Demo": mapping_demo,
+    #     "DataFrame Demo": data_frame_demo
+    # }
+
+    # demo_name = st.sidebar.selectbox("Choose a demo", page_names_to_funcs.keys())
+    # page_names_to_funcs[demo_name]()
+
+if __name__ == "__main__":
+    run()

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -5,7 +5,7 @@ colorFrom: gray
 colorTo: blue
 sdk: streamlit
 sdk_version: 1.10.0
-app_file: app.py
+app_file: About.py
 pinned: false
 license: mit
 ---

pages/1_🔥_WarmMolGen.py

@@ -0,0 +1,148 @@
+import streamlit as st
+import streamlit.components.v1 as components
+import pandas as pd
+import mols2grid
+import textwrap
+import numpy as np
+from transformers import EncoderDecoderModel, RobertaTokenizer
+
+# @st.cache(allow_output_mutation=False, hash_funcs={Tokenizer: str})
+@st.cache(suppress_st_warning=True)
+def load_models():
+    # protein_tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/WarmMolGenTwo")
+    # mol_tokenizer = RobertaTokenizer.from_pretrained("seyonec/PubChem10M_SMILES_BPE_450k")
+    model1 = EncoderDecoderModel.from_pretrained("gokceuludogan/WarmMolGenOne")
+    model2 = EncoderDecoderModel.from_pretrained("gokceuludogan/WarmMolGenTwo")
+    return model1, model2#, protein_tokenizer, mol_tokenizer
+
+
+def warmmolgen_demo():
+    protein_tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/WarmMolGenTwo")
+    mol_tokenizer = RobertaTokenizer.from_pretrained("seyonec/PubChem10M_SMILES_BPE_450k")
+    #model1, model2, protein_tokenizer, mol_tokenizer = load_models()
+    model1, model2 = load_models()
+
+    st.sidebar.subheader("Configurable parameters")
+
+    model_name = st.sidebar.selectbox(
+        "Model Selector",
+        options=[
+            "WarmMolGenOne",
+            "WarmMolGenTwo",
+        ],
+        index=0,
+    )
+
+    num_mols = st.sidebar.number_input(
+        "Number of generated molecules",
+        min_value=0,
+        max_value=20,
+        value=10,
+        help="The number of molecules to be generated.",
+    )
+
+    max_new_tokens = st.sidebar.number_input(
+        "Maximum length",
+        min_value=0,
+        max_value=1024,
+        value=128,
+        help="The maximum length of the sequence to be generated.",
+    )
+    # temp = st.sidebar.slider(
+    #     "Temperature",
+    #     value=1.0,
+    #     min_value=0.1,
+    #     max_value=100.0,
+    #     help="The value used to module the next token probabilities.",
+    # )
+    # top_k = st.sidebar.number_input(
+    #     "Top k",
+    #     value=10,
+    #     help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
+    # )
+    # top_p = st.sidebar.number_input(
+    #     "Top p",
+    #     value=0.95,
+    #     help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
+    # )
+    do_sample = st.sidebar.selectbox(
+        "Sampling?",
+        (True, False),
+        help="Whether or not to use sampling; use beam decoding otherwise.",
+    )
+    # num_beams = st.sidebar.number_input(
+    #     "Number of beams",
+    #     min_value=0,
+    #     max_value=20,
+    #     value=0,
+    #     help="The number of beams to use for beam search.",
+    # )
+    num_beams = None if do_sample is True else int(num_mols)
+    # repetition_penalty = st.sidebar.number_input(
+    #     "Repetition Penalty",
+    #     min_value=0.0,
+    #     value=3.0,
+    #     step=0.1,
+    #     help="The parameter for repetition penalty. 1.0 means no penalty",
+    # )
+    # no_repeat_ngram_size = st.sidebar.number_input(
+    #     "No Repeat N-Gram Size",
+    #     min_value=0,
+    #     value=3,
+    #     help="If set to int > 0, all ngrams of that size can only occur once.",
+    # )
+    target = st.text_area(
+        "Target Sequence",
+        "MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTG",
+    )
+    inputs = protein_tokenizer(target, return_tensors="pt")
+
+    model = model1 if model_name == 'WarmMolGenOne' else model2
+    outputs = model.generate(**inputs, decoder_start_token_id=mol_tokenizer.bos_token_id, 
+                             eos_token_id=mol_tokenizer.eos_token_id, pad_token_id=mol_tokenizer.eos_token_id, 
+                             max_length=int(max_new_tokens), num_return_sequences=int(num_mols), 
+                             do_sample=do_sample, num_beams=num_beams)
+    output_smiles = mol_tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    st.write("### Generated Molecules")
+    #st.write(output_smiles)    
+    df_smiles = pd.DataFrame({'SMILES': output_smiles})
+    #st.write(df_smiles)    
+    raw_html = mols2grid.display(df_smiles, mapping={"SMILES": "SMILES"})._repr_html_()
+    components.html(raw_html, width=900, height=450, scrolling=True)
+    st.markdown("## How to Generate")
+    generation_code = f"""
+    from transformers import EncoderDecoderModel, RobertaTokenizer
+
+    protein_tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/{model_name}") 
+    mol_tokenizer = RobertaTokenizer.from_pretrained("seyonec/PubChem10M_SMILES_BPE_450k")
+    model = EncoderDecoderModel.from_pretrained("gokceuludogan/{model_name}") 
+
+    inputs = protein_tokenizer("{target}", return_tensors="pt")
+    outputs = model.generate(**inputs, decoder_start_token_id=mol_tokenizer.bos_token_id, 
+                             eos_token_id=mol_tokenizer.eos_token_id, pad_token_id=mol_tokenizer.eos_token_id, 
+                             max_length={max_new_tokens}, num_return_sequences={num_mols}, do_sample={do_sample}, num_beams={num_beams})
+
+    mol_tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    """
+    st.code(textwrap.dedent(generation_code)) # textwrap.dedent("".join("Halletcez")))
+
+st.set_page_config(page_title="WarmMolGen Demo", page_icon="🔥", layout='wide')
+st.markdown("# WarmMolGen Demo")
+st.sidebar.header("WarmMolGen Demo")
+st.markdown(
+    """
+    This demo illustrates WarmMolGen models' generation capabilities. 
+
+
+    Given a target sequence and a set of parameters, the models generate molecules targeting the given protein sequence.
+
+
+    Please enter an input sequence below 👇  and configure parameters from the sidebar 👈 to generate molecules!
+
+
+    See below for saving the output molecules and the code snippet generating them!
+"""
+)
+
+warmmolgen_demo()
+

pages/2_✨_ChemBERTaLM.py

@@ -0,0 +1,126 @@
+import streamlit as st
+import streamlit.components.v1 as components
+import pandas as pd
+import mols2grid
+from transformers import RobertaForCausalLM, RobertaTokenizer, pipeline
+
+# @st.cache(allow_output_mutation=False, hash_funcs={Tokenizer: str})
+@st.cache(suppress_st_warning=True)
+def load_models():
+    model = RobertaForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM")
+    return model
+
+
+def chembertalm_demo():
+    tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM")
+    model = load_models()
+    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+    st.sidebar.subheader("Configurable parameters")
+
+    num_mols = st.sidebar.number_input(
+        "Number of generated molecules",
+        min_value=0,
+        max_value=200,
+        value=20,
+        help="The number of molecules to be generated.",
+    )
+
+    max_new_tokens = st.sidebar.number_input(
+        "Maximum length",
+        min_value=0,
+        max_value=1024,
+        value=128,
+        help="The maximum length of the sequence to be generated.",
+    )
+    # temp = st.sidebar.slider(
+    #     "Temperature",
+    #     value=1.0,
+    #     min_value=0.1,
+    #     max_value=100.0,
+    #     help="The value used to module the next token probabilities.",
+    # )
+    # top_k = st.sidebar.number_input(
+    #     "Top k",
+    #     value=10,
+    #     help="The number of highest probability vocabulary tokens to keep for top-k-filtering.",
+    # )
+    # top_p = st.sidebar.number_input(
+    #     "Top p",
+    #     value=0.95,
+    #     help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation.",
+    # )
+    do_sample = st.sidebar.selectbox(
+        "Sampling?",
+        (True, False),
+        help="Whether or not to use sampling; use beam decoding otherwise.",
+    )
+    # num_beams = st.sidebar.number_input(
+    #     "Number of beams",
+    #     min_value=0,
+    #     max_value=20,
+    #     value=0,
+    #     help="The number of beams to use for beam search.",
+    # )
+    # num_beams = None if do_sample is True else int(num_mols)
+    # repetition_penalty = st.sidebar.number_input(
+    #     "Repetition Penalty",
+    #     min_value=0.0,
+    #     value=3.0,
+    #     step=0.1,
+    #     help="The parameter for repetition penalty. 1.0 means no penalty",
+    # )
+    # no_repeat_ngram_size = st.sidebar.number_input(
+    #     "No Repeat N-Gram Size",
+    #     min_value=0,
+    #     value=3,
+    #     help="If set to int > 0, all ngrams of that size can only occur once.",
+    # )
+    # target = st.text_input(
+    #     "Input Sequence",
+    #     "",
+    # )
+    target = ""
+    params = {'do_sample': do_sample, 'num_return_sequences': num_mols, 'max_length': max_new_tokens}
+    outputs = generator(target, **params)
+    output_smiles = [output["generated_text"] for output in outputs]
+    st.write("### Generated Molecules")
+    #st.write(output_smiles)    
+    df_smiles = pd.DataFrame({'SMILES': output_smiles})
+    #st.write(df_smiles)    
+    raw_html = mols2grid.display(df_smiles, mapping={"SMILES": "SMILES"})._repr_html_()
+    components.html(raw_html, width=900, height=450, scrolling=True)
+    st.markdown("## How to Generate")
+    generation_code = f"""
+    from transformers import RobertaForCausalLM, RobertaTokenizer, pipeline
+
+    tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM")
+    model = RobertaForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM")
+    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+    params = {params}
+    outputs = generator("{target}", **params)
+    output_smiles = [output["generated_text"] for output in outputs]
+    """
+    st.code(textwrap.dedent(generation_code)) # textwrap.dedent("".join("Halletcez")))
+
+st.set_page_config(page_title="ChemBERTaLM Demo", page_icon="✨", layout='wide')
+st.markdown("# ChemBERTaLM Demo")
+st.sidebar.header("ChemBERTaLM Demo")
+st.markdown(
+    """
+    This demo illustrates ChemBERTaLM models' generation capabilities. 
+
+
+    Given a set of parameters, ChemBERTaLM generates a collection of molecules.
+
+
+    Please configure parameters from the sidebar 👈 to generate molecules!
+
+
+    See below for saving the output molecules and the code snippet generating them!
+"""
+)
+
+chembertalm_demo()
+

requirements.txt

@@ -0,0 +1,4 @@
+pandas==1.4.2
+streamlit=1.12.0
+mols2grid=0.2.4
+transformers=4.21.1