Awesome Drug Discovery
A curated resource list of computational methods for drug discovery, covering molecular modeling tools, machine learning frameworks, databases, and open-source pipelines organized by application area.
Jan 16, 2024
Computational Drug Repurposing with Multiscale Interactomes
Build one graph of drugs, proteins, pathways, and a disease, embed it, and rank candidates by proximity to the disease node. Three ways of reading that ranking, then an assay.
Jul 15, 2023
Interpretable Machine Learning as a Key to Understanding BBB Permeability
A random forest on the B3DB blood-brain barrier set, scored on a held-out split, then opened with impurity importance and SHAP. TopoPSA is in the descriptor block only if you select that block on purpose.
May 24, 2023
Data-Driven Chemistry: How Data Science Empowers Drug Discovery
A chemical table becomes a decision only after the row is defined, the model is scored on rows it did not fit, and the next experiment is allowed to disagree.
Feb 9, 2023
Using PaDELPy to Generate Molecular Fingerprints for Machine Learning-Based QSAR
Turn a SMILES table into PaDEL fingerprints and a random forest, then score a held-out split with Matthews correlation. The cross-validation number is computed, not assumed.
Jan 15, 2023
Supervised vs. Unsupervised Methods in Machine Learning
Supervised learning needs a label on each example. Unsupervised learning looks for structure when that label is absent. The difference is in the table, before any algorithm is chosen.
Oct 29, 2022
Chemical Databases Every ML Scientist Should Know for Drug Discovery
ChEMBL, BindingDB, PubChem, PDBbind, and BRENDA answer different questions. The useful choice is the table that holds the measurement you mean to model.
Oct 3, 2022
How to Perform Data Curation and Classify Bioactivity Data on ChEMBL Database
How a ChEMBL IC50 table for one target becomes a labelled set: one row per molecule, a mean potency, and a class that leaves the uncertain middle out.
Sep 7, 2022