def delete_step():
reaction = request.form['reaction']
task_id = request.form['task_id']
data = json.loads(current_app.redis.get(task_id))
graph_dict = json.loads(data['graph_dict'])
attr_dict = json.loads(data['attr_dict'])
target_smiles = data['target_smiles']
network_options = json.loads(data['network_options'])
graph = nx.from_dict_of_lists(graph_dict, create_using=nx.DiGraph)
network = Network(graph=graph,
target_smiles=target_smiles,
print_log=not current_app.config['PRODUCTION'])
network.update_settings(network_options)
network.add_attributes(attr_dict)
to_delete = network.delete_reaction_node(reaction)
nodes = []
edges = []
data['graph_dict'] = json.dumps(nx.to_dict_of_lists(network.graph))
data['attr_dict'] = json.dumps(network.attributes_dict())
nodes = add_new(data['nodes'], nodes)
edges = add_new(data['edges'], edges)
nodes, edges = delete_nodes_and_edges(to_delete, nodes, edges)
data['nodes'] = nodes
data['edges'] = edges
current_app.redis.mset({task_id: json.dumps(data)})
time_to_expire = 15 * 60 # 15 mins * 60 seconds
current_app.redis.expire(task_id, time_to_expire)
result = {'to_delete': to_delete}
return jsonify(result=result)
def change_reaction_options():
task_id = request.form['task_id']
data = json.loads(current_app.redis.get(task_id))
graph_dict = json.loads(data['graph_dict'])
attr_dict = json.loads(data['attr_dict'])
target_smiles = data['target_smiles']
network_options = json.loads(data['network_options'])
graph = nx.from_dict_of_lists(graph_dict, create_using=nx.DiGraph)
network = Network(graph=graph, target_smiles=target_smiles)
network.update_settings(network_options)
network.add_attributes(attr_dict)
network.update_settings(
{'max_reactions': int(request.form['max_reactions'])})
"""
if len(retrobiocat.retrorules_diameters) != 0:
network.settings['rr_min_diameter'] = int(request.form['rr_min_diameter'])
network.settings['rr_min_products'] = int(request.form['rr_min_products'])
network.settings['rr_max_reactions'] = int(request.form['rr_max_reactions'])
"""
data['network_options'] = json.dumps(network.settings)
current_app.redis.mset({task_id: json.dumps(data)})
time_to_expire = 15 * 60 #15 mins * 60 seconds
current_app.redis.expire(task_id, time_to_expire)
result = {'network_options': json.dumps(network.settings)}
return jsonify(result=result)
def change_network_options():
reaction_colours = request.form['reaction_colours']
edge_colours = request.form['edge_colours']
task_id = request.form['task_id']
data = json.loads(current_app.redis.get(task_id))
graph_dict = json.loads(data['graph_dict'])
attr_dict = json.loads(data['attr_dict'])
target_smiles = data['target_smiles']
network_options = json.loads(data['network_options'])
graph = nx.from_dict_of_lists(graph_dict, create_using=nx.DiGraph)
network = Network(graph=graph, target_smiles=target_smiles)
network.update_settings(network_options)
network.add_attributes(attr_dict)
network.settings['colour_reactions'] = reaction_colours
network.settings["colour_arrows"] = edge_colours
nodes, edges = network.get_visjs_nodes_and_edges()
data['nodes'] = add_new(data['nodes'], nodes)
data['edges'] = add_new(data['edges'], edges)
data['network_options'] = json.dumps(network.settings)
current_app.redis.mset({task_id: json.dumps(data)})
time_to_expire = 15 * 60 #15 mins * 60 seconds
current_app.redis.expire(task_id, time_to_expire)
result = {
'nodes': nodes,
'edges': edges,
}
return jsonify(result=result)
def package_visjs_pathways(task_id, max_vis=100):
network_data = json.loads(current_app.redis.get(task_id + '__network'))
graph = nx.from_dict_of_lists(json.loads(network_data['graph_dict']),
create_using=nx.DiGraph)
network = Network(graph=graph,
target_smiles=network_data['target_smiles'],
print_log=not current_app.config['PRODUCTION'])
network.update_settings(json.loads(network_data['network_options']))
network.add_attributes(json.loads(network_data['attr_dict']))
evaluated_pathways = json.loads(
current_app.redis.get(f"{task_id}__evaluated_pathways"))
for i, pathway_varients in enumerate(evaluated_pathways):
if i > max_vis:
break
pathway_vis_js_data = []
max_var = len(pathway_varients)
for nodes in pathway_varients:
pathway = Pathway(nodes, network, calc_scores=False)
nodes, edges = pathway.get_visjs_nodes_and_edges()
pathway_vis_js_data.append((nodes, edges, max_var))
current_app.redis.mset(
{f"{task_id}__{i+1}": json.dumps(pathway_vis_js_data)})
current_app.redis.expire(f"{task_id}__{i+1}", 60 * 60)
def aizynth_step():
clicked_node = request.form['smiles']
x = request.form['x']
y = request.form['y']
task_id = request.form['task_id']
max_reactions = request.form['max_reactions']
aizynth_reaction_mode = request.form['aizynth_reaction_mode']
data = json.loads(current_app.redis.get(task_id))
graph_dict = json.loads(data['graph_dict'])
attr_dict = json.loads(data['attr_dict'])
target_smiles = data['target_smiles']
network_options = json.loads(data['network_options'])
graph = nx.from_dict_of_lists(graph_dict, create_using=nx.DiGraph)
network = Network(graph=graph,
target_smiles=target_smiles,
print_log=not current_app.config['PRODUCTION'])
network.update_settings(network_options)
network.add_attributes(attr_dict)
network.update_settings({
'max_reactions': int(max_reactions),
'aizynth_reaction_mode': aizynth_reaction_mode
})
new_substrate_nodes, new_reaction_nodes = network.add_chemical_step(
clicked_node)
all_new_nodes = [clicked_node] + new_substrate_nodes + new_reaction_nodes
subgraph = network.graph.subgraph(all_new_nodes)
nodes, edges = network.get_visjs_nodes_and_edges(graph=subgraph)
for i, node in enumerate(nodes):
nodes[i].update({'x': x, 'y': y})
result = {'nodes': nodes, 'edges': edges}
data['graph_dict'] = json.dumps(nx.to_dict_of_lists(network.graph))
data['attr_dict'] = json.dumps(network.attributes_dict())
nodes = add_new(data['nodes'], nodes)
edges = add_new(data['edges'], edges)
nodes, edges = delete_nodes_and_edges([], nodes, edges)
data['nodes'] = nodes
data['edges'] = edges
current_app.redis.mset({task_id: json.dumps(data)})
time_to_expire = 15 * 60 #15 mins * 60 seconds
current_app.redis.expire(task_id, time_to_expire)
return jsonify(result=result)
def task_add_retrorule_step(form_data, network_id):
job = get_current_job()
job.meta['progress'] = 'started'
job.save_meta()
clicked_node = form_data['smiles']
x = form_data['x']
y = form_data['y']
data = json.loads(current_app.redis.get(network_id))
graph_dict = json.loads(data['graph_dict'])
attr_dict = json.loads(data['attr_dict'])
target_smiles = data['target_smiles']
network_options = json.loads(data['network_options'])
graph = nx.from_dict_of_lists(graph_dict, create_using=nx.DiGraph)
network = Network(graph=graph, target_smiles=target_smiles)
network.update_settings(network_options)
network.add_attributes(attr_dict)
network.retrorules.retrorules_rxns = current_app.retrorules_rxns
network.retrorules.retrorule_db = current_app.retrorules_db
new_substrate_nodes, new_reaction_nodes = network.retrorules.add_step(
clicked_node)
all_new_nodes = [clicked_node] + new_substrate_nodes + new_reaction_nodes
subgraph = network.graph.subgraph(all_new_nodes)
nodes, edges = network.get_visjs_nodes_and_edges(graph=subgraph)
for i, node in enumerate(nodes):
nodes[i].update({'x': x, 'y': y})
result = {
'nodes': nodes,
'edges': edges,
'to_delete': [],
}
data['graph_dict'] = json.dumps(nx.to_dict_of_lists(network.graph))
data['attr_dict'] = json.dumps(network.attributes_dict())
data['nodes'] = add_new(data['nodes'], nodes)
data['edges'] = add_new(data['edges'], edges)
current_app.redis.mset({network_id: json.dumps(data)})
current_app.redis.expire(network_id, 5 * 60)
return result
def task_reorder_pathways(weights, pathways_id):
job = get_current_job()
job.meta['progress'] = 'started'
job.save_meta()
pathway_settings = json.loads(
current_app.redis.get(pathways_id + '__pathway_settings'))
pathway_settings.update({
'weight_num_enzymes': weights[0],
'weight_complexity': weights[1],
'weight_starting': weights[2],
'weight_known_enzymes': weights[3],
'weight_diversity': weights[4]
})
current_app.redis.mset(
{f"{pathways_id}__pathway_settings": json.dumps(pathway_settings)})
current_app.redis.expire(pathways_id, 60 * 60)
network_data = json.loads(current_app.redis.get(pathways_id + '__network'))
graph = nx.from_dict_of_lists(json.loads(network_data['graph_dict']),
create_using=nx.DiGraph)
network = Network(graph=graph,
target_smiles=network_data['target_smiles'],
print_log=not current_app.config['PRODUCTION'])
network.update_settings(json.loads(network_data['network_options']))
network.add_attributes(json.loads(network_data['attr_dict']))
all_pathways_nodes, all_scores = json.loads(
current_app.redis.get(f"{pathways_id}__all_pathways"))
pathways = load_pathways(all_pathways_nodes, all_scores, network)
pathway_evaluator = evaluate_pathways(pathways, weights)
print(weights)
package_evaluated_pathways(pathway_evaluator.df, pathways_id)
package_visjs_pathways(pathways_id)
job = get_current_job()
job.meta['progress'] = 'complete'
job.save_meta()
result = {}
return result
def get_visjs_pathway(task_id, pathway_id, varient):
network_data = json.loads(current_app.redis.get(task_id + '__network'))
pathway_data = json.loads(
current_app.redis.get(task_id + f'__{pathway_id}'))
pathway_nodes = pathway_data[varient - 1]
graph = nx.from_dict_of_lists(json.loads(network_data['graph_dict']),
create_using=nx.DiGraph)
network = Network(graph=graph,
target_smiles=network_data['target_smiles'],
print_log=not current_app.config['PRODUCTION'])
network.update_settings(json.loads(network_data['network_options']))
network.add_attributes(json.loads(network_data['attr_dict']))
pathway = Pathway(pathway_nodes, network, calc_scores=False)
nodes, edges = pathway.get_visjs_nodes_and_edges()
max_var = len(pathway_data)
return nodes, edges, max_var
def custom_reaction():
product_smiles = str(request.form['product'])
substrate_smiles = str(request.form['substrate'])
reaction_name = str(request.form['name'])
task_id = request.form['task_id']
data = json.loads(current_app.redis.get(task_id))
graph_dict = json.loads(data['graph_dict'])
attr_dict = json.loads(data['attr_dict'])
target_smiles = data['target_smiles']
network_options = json.loads(data['network_options'])
graph = nx.from_dict_of_lists(graph_dict, create_using=nx.DiGraph)
network = Network(graph=graph, target_smiles=target_smiles)
network.update_settings(network_options)
network.add_attributes(attr_dict)
new_substrate_nodes, new_reaction_nodes = network.custom_reaction(
product_smiles, substrate_smiles, reaction_name)
all_new_nodes = new_substrate_nodes + new_reaction_nodes
subgraph = network.graph.subgraph(all_new_nodes)
nodes, edges = network.get_visjs_nodes_and_edges(graph=subgraph)
result = {
'nodes': nodes,
'edges': edges,
}
data['graph_dict'] = json.dumps(nx.to_dict_of_lists(network.graph))
data['attr_dict'] = json.dumps(network.attributes_dict())
nodes = add_new(data['nodes'], nodes)
edges = add_new(data['edges'], edges)
data['nodes'] = nodes
data['edges'] = edges
current_app.redis.mset({task_id: json.dumps(data)})
current_app.redis.expire(task_id, 5 * 60)
return jsonify(result=result)
def task_make_network(form_data):
job = get_current_job()
job.meta['progress'] = 'started'
job.save_meta()
network = Network(include_experimental=bool(form_data['include_experimental']),
include_two_step=bool(form_data['include_two_step']),
include_requires_absence_of_water=bool(form_data['include_requires_absence_of_water']),
print_log=not current_app.config['PRODUCTION'])
network.update_settings({"allow_backwards_steps": bool(form_data['allow_backwards']),
"remove_simple": bool(form_data['remove_small']),
"similarity_score_threshold": float(form_data['sub_thres']),
"combine_enantiomers" : bool(form_data['combine_enantiomers']),
"num_enzymes": 1,
"calculate_complexities": bool(form_data['calc_complexity']),
"calculate_substrate_specificity": bool(form_data['sub_sim']),
"max_nodes": int(form_data['max_initial_nodes'],),
"colour_reactions" : form_data['colour_reactions'],
"colour_arrows": form_data['colour_edges'],
"show_negative_enzymes" : form_data['show_neg_enz'],
"only_postitive_enzyme_data" : not form_data['show_neg_enz'],
"max_reactions": form_data["max_reactions"],
'only_reviewed_activity_data': bool(form_data["only_reviewed"])})
if form_data["specificity_scoring_mode"] == 'Product + substrates (slower)':
network.update_settings({'specificity_score_substrates' : True})
#print(f"include_experimental = {network.settings['include_experimental']}")
#print(f"include_two_step = {network.settings['include_two_step']}")
network.generate(form_data['target_smiles'], form_data['number_steps'], calculate_scores=False)
job.meta['progress'] = 'network_generated'
job.save_meta()
network.calculate_scores()
job.meta['progress'] = 'scores_calculated'
job.save_meta()
nodes, edges = network.get_visjs_nodes_and_edges()
#options = {'interaction': {'multiselect': 'true',}}
options = {}
default_network_name = 'Network for ' + str(network.target_smiles)
result = {'save_id':str(uuid.uuid4()),
'save_links' : [],
'save_name' : default_network_name,
'nodes':nodes,
'edges':edges,
'options':json.dumps(options),
'graph_dict':json.dumps(nx.to_dict_of_lists(network.graph)),
'target_smiles':str(network.target_smiles),
'network_options':json.dumps(network.settings),
'attr_dict':json.dumps(network.attributes_dict()),
'max_reactions' : int(network.settings['max_reactions'])}
current_app.redis.mset({job.id: json.dumps(result)})
time_to_expire = 15*60 #15 mins * 60 seconds
current_app.redis.expire(job.id, time_to_expire)
return result
def task_get_pathways(form_data):
job = get_current_job()
job.meta['progress'] = 'started'
job.save_meta()
network = Network(print_log=not current_app.config['PRODUCTION'],
include_experimental=form_data['include_experimental'],
include_two_step=form_data['include_two_step'],
include_requires_absence_of_water=bool(
form_data['include_requires_absence_of_water']))
network.update_settings({
"remove_simple":
bool(form_data['remove_small']),
"combine_enantiomers":
bool(form_data['combine_enantiomers']),
'max_nodes':
int(form_data['max_nodes']),
'similarity_score_threshold':
float(form_data['sub_thres']),
'colour_reactions':
form_data['colour_reactions'],
"colour_arrows":
form_data['colour_edges'],
"show_negative_enzymes":
form_data['show_neg_enz'],
"only_postitive_enzyme_data":
not form_data['show_neg_enz'],
'only_reviewed_activity_data':
bool(form_data["only_reviewed"])
})
if form_data[
"specificity_scoring_mode"] == 'Product + substrates (slower)':
network.update_settings({'specificity_score_substrates': True})
network.generate(form_data['target_smiles'],
form_data['number_steps'],
calculate_scores=False)
job.meta['progress'] = 'network_generated'
job.save_meta()
network.calculate_scores()
job.meta['progress'] = 'network_scored'
job.save_meta()
network_data = {
'graph_dict': json.dumps(nx.to_dict_of_lists(network.graph)),
'target_smiles': str(network.target_smiles),
'network_options': json.dumps(network.settings),
'attr_dict': json.dumps(network.attributes_dict())
}
current_app.redis.mset({f"{job.id}__network": json.dumps(network_data)})
current_app.redis.expire(f"{job.id}__network", 60 * 60)
bfs = BFS(network=network,
max_pathways=form_data['max_pathways'],
max_pathway_length=form_data['number_steps'],
min_weight=float(form_data['min_weight']),
print_log=not current_app.config['PRODUCTION'])
bfs.run()
pathways = bfs.get_pathways()
job.meta['progress'] = 'pathways_generated'
job.save_meta()
package_all_pathways(job.id, pathways)
pathway_evaluator = evaluate_pathways(pathways, [
form_data['weight_num_enzymes'], form_data['weight_complexity'],
form_data['weight_starting'], form_data['weight_known_enzymes'],
form_data['weight_diversity']
])
package_evaluated_pathways(pathway_evaluator.df, job.id)
package_visjs_pathways(job.id)
job.meta['progress'] = 'pathways_scored'
job.save_meta()
options = {}
if form_data['hierarchical'] == True:
options.update({
"layout": {
"improvedLayout": 'true',
'hierarchical': {
'direction': 'DU',
"sortMethod": "hubsize",
"nodeSpacing": 200,
"treeSpacing": 400
}
}
})
pathway_settings = {
'weight_num_enzymes': form_data['weight_num_enzymes'],
'weight_complexity': form_data['weight_complexity'],
'weight_starting': form_data['weight_starting'],
'weight_known_enzymes': form_data['weight_known_enzymes'],
'weight_diversity': form_data['weight_diversity'],
'options': options
}
current_app.redis.mset(
{f"{job.id}__pathway_settings": json.dumps(pathway_settings)})
current_app.redis.expire(job.id, 60 * 60)