Ranking Candidates in Virtual Screening
Virtual screening is a key technique in chemoinformatics, enabling you to rapidly evaluate large libraries of compounds and identify those most likely to be active against a biological target. However, after filtering or predicting properties for thousands of molecules, you face the challenge of deciding which compounds to prioritize for experimental testing. This is where ranking comes in: by assigning scores to each molecule—based on predicted activity, similarity to a known active, or other criteria—you can systematically select the most promising candidates. Ranking is crucial because it helps you focus limited resources on the compounds most likely to succeed, increasing the efficiency of drug discovery efforts.
123456789101112131415161718192021import pandas as pd import numpy as np # Example: scoring molecules by predicted activity (QSAR output) # Suppose you have a DataFrame with SMILES and predicted activities data = { "smiles": [ "CCO", # ethanol "CCCN", # propylamine "CC(=O)O", # acetic acid "CCN(CC)CC", # triethylamine "CCOC(=O)C" # ethyl acetate ], "predicted_activity": [0.23, 0.78, 0.12, 0.56, 0.44] } df = pd.DataFrame(data) # Add a 'score' column (here, same as predicted activity) df["score"] = df["predicted_activity"] print(df)
Once you have assigned a score to each molecule, you need to sort and rank the compounds to identify the top candidates. Sorting the list by score in descending order puts the highest-scoring molecules at the top, making it easy to select those most likely to meet your objectives. In practice, you can use pandas to sort your DataFrame by the score column, and then assign a rank to each compound. This process is essential whether you are ranking by predicted activity, binding affinity, or any other computed property.
12345# Sort molecules by score (highest first) and assign rank df_sorted = df.sort_values(by="score", ascending=False).reset_index(drop=True) df_sorted["rank"] = df_sorted.index + 1 print(df_sorted[["smiles", "score", "rank"]])
Another common approach is to rank compounds by their similarity to a reference molecule, such as a known active compound. By comparing molecular fingerprints, you can calculate a similarity score (such as Tanimoto similarity) between each candidate and the reference. This allows you to prioritize molecules that are structurally similar to compounds with proven activity, which is often a useful strategy in lead optimization.
123456789101112131415161718192021222324252627282930313233343536373839import pandas as pd from rdkit import Chem, DataStructs from rdkit.Chem.rdFingerprintGenerator import GetMorganGenerator # Example DataFrame (replace with your real df loading) df = pd.DataFrame({ "smiles": ["CCN(CC)CC", "CCO", "CCCN", "CCOC(=O)C", "CC(=O)O"] }) # Sanity check print(type(df)) print(df.columns) # Reference reference_smiles = "CCN(CC)CC" reference_mol = Chem.MolFromSmiles(reference_smiles) if reference_mol is None: raise ValueError("Reference SMILES could not be parsed") gen = GetMorganGenerator(radius=2, fpSize=1024) reference_fp = gen.GetFingerprint(reference_mol) # Similarities similarities = [] for smi in df["smiles"]: mol = Chem.MolFromSmiles(smi) if mol is None: similarities.append(None) continue fp = gen.GetFingerprint(mol) sim = DataStructs.TanimotoSimilarity(reference_fp, fp) similarities.append(sim) df["similarity_to_reference"] = similarities df_sorted = df.sort_values(by="similarity_to_reference", ascending=False, na_position="last").reset_index(drop=True) df_sorted["similarity_rank"] = df_sorted.index + 1 print(df_sorted[["smiles", "similarity_to_reference", "similarity_rank"]])
After scoring and ranking, you must decide how many and which compounds to advance to further testing. Common strategies include selecting the top N compounds, choosing all compounds above a certain score threshold, or using a combination of property-based and similarity-based ranking to ensure diversity among top candidates. Balancing the desire for high predicted activity with chemical diversity can increase the likelihood of finding true actives and avoiding false positives. By carefully selecting compounds for follow-up, you make the most efficient use of laboratory resources and maximize the impact of your virtual screening campaign.
1. What is the main goal of ranking in virtual screening?
2. Which method can be used to rank molecules by similarity?
3. Why is it important to rank compounds after screening?
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How can I select the top N compounds based on their scores?
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Virtual screening is a key technique in chemoinformatics, enabling you to rapidly evaluate large libraries of compounds and identify those most likely to be active against a biological target. However, after filtering or predicting properties for thousands of molecules, you face the challenge of deciding which compounds to prioritize for experimental testing. This is where ranking comes in: by assigning scores to each molecule—based on predicted activity, similarity to a known active, or other criteria—you can systematically select the most promising candidates. Ranking is crucial because it helps you focus limited resources on the compounds most likely to succeed, increasing the efficiency of drug discovery efforts.
123456789101112131415161718192021import pandas as pd import numpy as np # Example: scoring molecules by predicted activity (QSAR output) # Suppose you have a DataFrame with SMILES and predicted activities data = { "smiles": [ "CCO", # ethanol "CCCN", # propylamine "CC(=O)O", # acetic acid "CCN(CC)CC", # triethylamine "CCOC(=O)C" # ethyl acetate ], "predicted_activity": [0.23, 0.78, 0.12, 0.56, 0.44] } df = pd.DataFrame(data) # Add a 'score' column (here, same as predicted activity) df["score"] = df["predicted_activity"] print(df)
Once you have assigned a score to each molecule, you need to sort and rank the compounds to identify the top candidates. Sorting the list by score in descending order puts the highest-scoring molecules at the top, making it easy to select those most likely to meet your objectives. In practice, you can use pandas to sort your DataFrame by the score column, and then assign a rank to each compound. This process is essential whether you are ranking by predicted activity, binding affinity, or any other computed property.
12345# Sort molecules by score (highest first) and assign rank df_sorted = df.sort_values(by="score", ascending=False).reset_index(drop=True) df_sorted["rank"] = df_sorted.index + 1 print(df_sorted[["smiles", "score", "rank"]])
Another common approach is to rank compounds by their similarity to a reference molecule, such as a known active compound. By comparing molecular fingerprints, you can calculate a similarity score (such as Tanimoto similarity) between each candidate and the reference. This allows you to prioritize molecules that are structurally similar to compounds with proven activity, which is often a useful strategy in lead optimization.
123456789101112131415161718192021222324252627282930313233343536373839import pandas as pd from rdkit import Chem, DataStructs from rdkit.Chem.rdFingerprintGenerator import GetMorganGenerator # Example DataFrame (replace with your real df loading) df = pd.DataFrame({ "smiles": ["CCN(CC)CC", "CCO", "CCCN", "CCOC(=O)C", "CC(=O)O"] }) # Sanity check print(type(df)) print(df.columns) # Reference reference_smiles = "CCN(CC)CC" reference_mol = Chem.MolFromSmiles(reference_smiles) if reference_mol is None: raise ValueError("Reference SMILES could not be parsed") gen = GetMorganGenerator(radius=2, fpSize=1024) reference_fp = gen.GetFingerprint(reference_mol) # Similarities similarities = [] for smi in df["smiles"]: mol = Chem.MolFromSmiles(smi) if mol is None: similarities.append(None) continue fp = gen.GetFingerprint(mol) sim = DataStructs.TanimotoSimilarity(reference_fp, fp) similarities.append(sim) df["similarity_to_reference"] = similarities df_sorted = df.sort_values(by="similarity_to_reference", ascending=False, na_position="last").reset_index(drop=True) df_sorted["similarity_rank"] = df_sorted.index + 1 print(df_sorted[["smiles", "similarity_to_reference", "similarity_rank"]])
After scoring and ranking, you must decide how many and which compounds to advance to further testing. Common strategies include selecting the top N compounds, choosing all compounds above a certain score threshold, or using a combination of property-based and similarity-based ranking to ensure diversity among top candidates. Balancing the desire for high predicted activity with chemical diversity can increase the likelihood of finding true actives and avoiding false positives. By carefully selecting compounds for follow-up, you make the most efficient use of laboratory resources and maximize the impact of your virtual screening campaign.
1. What is the main goal of ranking in virtual screening?
2. Which method can be used to rank molecules by similarity?
3. Why is it important to rank compounds after screening?
Tack för dina kommentarer!
