def 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']
rbc_reaction_mode = request.form['rbc_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),
'retrobiocat_reaction_mode': rbc_reaction_mode
})
new_substrate_nodes, new_reaction_nodes = network.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}
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)
class BFS():
def __init__(self,
network=None,
target=None,
max_pathways=50000,
max_pathway_length=5,
min_weight=1,
use_random=False,
print_log=False,
score_pathways=True,
allow_longer_pathways=False):
"""
Best First Search object, for generating pathways from a network
After initialising, run search using the .run() method
Args:
network: a network object which has been generated
min_weight: the minimum weight to assign to zero complexity change (and Stop)
max_pathways: the maximum number of pathways to generate before stopping
use_random: set the bfs to use weighted random selection rather than always picking the best
"""
self.score_pathways = score_pathways
self.print_log = print_log
self.min_weight = min_weight
self.choices = {}
self.max_pathways = max_pathways
self.max_pathway_length = max_pathway_length
self.allow_longer_pathways = allow_longer_pathways
self.pathways = []
self.use_random = use_random
self.network = network
self.generate_network = False
if self.network == None:
self.target = node_analysis.rdkit_smile(target, warning=True)
self.generate_network = True
self.network = Network(target_smiles=self.target,
number_steps=self.max_pathway_length,
print_log=False)
self.network.generate(self.target, 0)
self.log('BFS - will generate network')
else:
self.target = self.network.target_smiles
def log(self, msg):
if self.print_log == True:
print(msg)
def _get_context(self, nodes):
""" Returns the pathway context, which is a string of node numbers"""
list_node_numbers = []
context = ''
for node in nodes:
list_node_numbers.append(
self.network.graph.nodes[node]['attributes']['node_num'])
sorted_node_numbers = sorted(list_node_numbers)
for node_num in sorted_node_numbers:
context += str(node_num)
context += '-'
return context
def _expand_network(self, smi):
nodes_added = []
new_substrates, new_reactions = self.network.add_step(smi)
nodes_added.extend(new_substrates)
nodes_added.extend(new_reactions)
return nodes_added
def _get_choices(self, end_nodes):
""" Returns a list of reaction nodes (and Stop) which are choices for the next step"""
def get_choice_scores(choices):
scores = [0]
for node in choices[1:]:
scores.append(self.network.graph.nodes[node]['attributes']
['change_in_complexity'])
return scores
def get_weighted_scores(scores):
# invert changes so decreases in complexity are favoured
inverted_reaction_complexity_changes = [x * -1 for x in scores]
min_change = min(inverted_reaction_complexity_changes)
if min_change < 0:
min_change = -min_change
else:
min_change = 0
non_neg_changes = [
x + self.min_weight + min_change
for x in inverted_reaction_complexity_changes
]
return non_neg_changes
def get_choices(end_nodes, graph):
successor_reactions = ['Stop']
for node in end_nodes:
successor_reactions.extend(list(graph.successors(node)))
return successor_reactions
def make_choice_dict(choices, scores):
choice_dict = {}
for i, choice in enumerate(choices):
choice_dict[choice] = scores[i]
return choice_dict
choices = get_choices(end_nodes, self.network.graph)
scores = get_choice_scores(choices)
weighted_scores = get_weighted_scores(scores)
choice_dict = make_choice_dict(choices, weighted_scores)
return choice_dict
def _pick_choice(self, context):
""" Given a context, picks an option to extend (or stop) that pathway """
def pick_best(choices, scores):
sorted_options = node_analysis.sort_by_score(choices,
scores,
reverse=False)
return sorted_options[0]
def pick_weighted_random(choices, scores):
return random.choices(choices, scores, k=1)[0]
def get_lists_choices_scores(choices_dict):
list_choices = []
list_scores = []
for choice in choices_dict:
list_choices.append(choice)
list_scores.append(choices_dict[choice])
return list_choices, list_scores
choices, scores = get_lists_choices_scores(self.choices[context])
if self.use_random == False:
option = pick_best(choices, scores)
else:
option = pick_weighted_random(choices, scores)
return option
def _add_reaction(self, reaction_choice):
new_end_nodes = list(self.network.graph.successors(reaction_choice))
added_nodes = [reaction_choice] + new_end_nodes
return added_nodes, new_end_nodes
def _check_pathway_has_end(self, nodes):
pathway_subgraph = self.network.graph.subgraph(nodes)
end_nodes = node_analysis.get_nodes_with_no_successors(
pathway_subgraph)
if len(end_nodes) == 0:
return False
return True
def _make_pathway(self, nodes):
""" Create pathway object from list of nodes"""
return Pathway(nodes, self.network, calc_scores=self.score_pathways)
def _check_if_should_expand_network(self, end_nodes, pathway_nodes):
if self.generate_network == True:
if self._num_reactions(pathway_nodes) < self.max_pathway_length:
for node in end_nodes:
if len(list(self.network.graph.successors(node))) == 0:
self._expand_network(node)
def _is_node_already_in_pathway(self, current_nodes, new_nodes):
for node in new_nodes:
if node in current_nodes:
return True
return False
def _num_reactions(self, nodes):
count = 0
for node in nodes:
if self.network.graph.nodes[node]['attributes'][
'node_type'] == 'reaction':
count += 1
return count
def run(self):
"""
Generate pathways using best first search
Returns: list of pathways
"""
self.log('Run BFS')
self.pathways = []
self.choices = {}
nodes = [self.target]
context = self._get_context(nodes)
self._check_if_should_expand_network(nodes, nodes)
self.choices[context] = self._get_choices(nodes)
start_context = copy.deepcopy(context)
while (len(self.pathways) < self.max_pathways) and (len(
self.choices[start_context]) > 0):
nodes = [self.network.target_smiles]
context = self._get_context(nodes)
steps = 0
while len(self.choices[context]) > 0:
if steps > self.max_pathway_length:
self.choices[context] = []
if self._check_pathway_has_end(nodes) == True:
self.pathways.append(nodes)
break
best_choice = self._pick_choice(context)
if best_choice == 'Stop':
if self._check_pathway_has_end(nodes) == True:
self.pathways.append(nodes)
self.choices[context].pop('Stop')
steps = 0
break
else:
steps += 1
added_nodes, new_end_nodes = self._add_reaction(
best_choice)
if self._is_node_already_in_pathway(nodes,
added_nodes) == True:
self.choices[context].pop(best_choice)
break
else:
new_context = self._get_context(nodes + added_nodes)
if new_context not in self.choices:
self._check_if_should_expand_network(
new_end_nodes, nodes + added_nodes)
self.choices[new_context] = self._get_choices(
new_end_nodes)
if len(self.choices[new_context]) == 0:
self.choices[context].pop(best_choice)
else:
nodes = nodes + added_nodes
context = new_context
self.log('BFS complete')
if len(self.pathways) >= self.max_pathways:
self.log('Max pathways reached')
return self.pathways
def get_pathways(self):
pathway_objects = []
for list_nodes in self.pathways:
pathway = self._make_pathway(list_nodes)
if self.allow_longer_pathways == True:
pathway_objects.append(pathway)
elif len(pathway.reactions) <= self.max_pathway_length:
pathway_objects.append(pathway)
return pathway_objects
